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Merge pull request #620 from Hestia-Homes/eco-eligiblity-bug
Eco eligiblity bug
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commit
f67260a1d6
6 changed files with 31 additions and 6 deletions
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@ -22,7 +22,6 @@ from recommendations.recommendation_utils import (
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)
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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from backend.app.utils import sap_to_epc
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from backend.Funding import Funding
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import backend.app.assumptions as assumptions
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ENVIRONMENT = os.environ.get("ENVIRONMENT", "dev")
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@ -340,7 +339,6 @@ class Property:
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recommendation_record=recommendation_record,
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recommendations=property_representative_recommendations,
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primary_recommendation_id=self.id,
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non_invasive_recommendations=self.non_invasive_recommendations,
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)
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return scoring_dict
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@ -1243,7 +1241,8 @@ class Property:
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"heat network": "Natural Gas (Community Scheme)",
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"lpg": 'LPG',
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"biodiesel": "Smokeless Fuel",
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"b30d": "B30K Biofuel"
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"b30d": "B30K Biofuel",
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"coal": "Coal",
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}
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self.heating_energy_source = list({
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@ -15,7 +15,6 @@ from sqlmodel import Session
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from etl.epc.Record import EPCRecord
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from sqlalchemy.exc import IntegrityError, OperationalError
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from sqlalchemy.orm import sessionmaker
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from starlette.responses import Response
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from backend.app.BatterySapScorer import BatterySAPScorer
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@ -0,0 +1,12 @@
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"""
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For the Peabody project, there were a number of subtasks that failed due to issues, with the most
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prominent being errors with the property address and ID data.
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This script will fetch those failed subtasks, get the associated properties and delete the properties
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from the database so that the subtasks can be restarted cleanly.
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Additionally, we wil find the problematic records and remove them
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Given we ran an EPC C scenario, we should check how many properties, below EPC C we have, that have no plan
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or recommendations in case something went wrong
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"""
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@ -697,8 +697,12 @@ class TrainingDataset(BaseDataset):
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for x in missing_descriptions:
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desc_cleaner = cleaner(x)
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cleaned = desc_cleaner.process()
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# IF NODATA, REMAP TO NONE VALUES
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if all((pd.DataFrame(cleaned, index=[0]).T)[0] == False):
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# IF NODATA, REMAP TO NONE VALUES, apart from walls which we want to keep as is
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# If we convert the walls data to None, we end up converting booleans to None which
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# causes issues downstream
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if all(
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(pd.DataFrame(cleaned, index=[0]).T)[0] == False
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) and component != "walls":
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cleaned = {key: None for key in cleaned.keys()}
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cleaned_data.append(
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{
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@ -85,6 +85,10 @@ resource "aws_db_instance" "default" {
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ca_cert_identifier = "rds-ca-rsa2048-g1"
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# Temporary to enfore immediate change
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apply_immediately = true
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# Set up storage type to gp3 for better performance
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storage_type = "gp3"
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iops = 3000
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storage_throughput = 125
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}
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# Set up the bucket that recieve the csv uploads of epc to be retrofit
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@ -698,6 +698,13 @@ class Recommendations:
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"heating_cop": 0.85,
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"hotwater_cop": 0.85
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}
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if main_fuel_description in ['coal (community)']:
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return {
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"heating_fuel_type": "Coal",
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"hotwater_fuel_type": "Coal",
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"heating_cop": 0.85,
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"hotwater_cop": 0.85
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}
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logger.warning(
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"Unhandled community fuel."
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f"Fuel: {main_fuel_description}"
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