From c09b693922c8c3c8ac55648de2772312f319d487 Mon Sep 17 00:00:00 2001 From: Khalim Conn-Kowlessar Date: Sun, 16 Feb 2025 18:25:17 +0000 Subject: [PATCH] minor tweaks to engine during remote assessments --- backend/app/assumptions.py | 1 + backend/app/plan/router.py | 2 +- etl/customers/remote_assessments/app.py | 14 ++++++++------ 3 files changed, 10 insertions(+), 7 deletions(-) diff --git a/backend/app/assumptions.py b/backend/app/assumptions.py index 841ec2c1..8d0c05be 100644 --- a/backend/app/assumptions.py +++ b/backend/app/assumptions.py @@ -54,4 +54,5 @@ DESCRIPTIONS_TO_FUEL_TYPES = { "Gas instantaneous at point of use": {"fuel": "Natural Gas", "cop": 0.85}, "Room heaters, wood logs": {"fuel": "Wood Logs", "cop": 1}, "Boiler and radiators, coal": {"fuel": "Coal", "cop": 0.85}, + "From main system, no cylinderstat": {"fuel": "Natural Gas", "cop": 0.85}, } diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py index 949c8e4c..76c172ee 100644 --- a/backend/app/plan/router.py +++ b/backend/app/plan/router.py @@ -338,7 +338,7 @@ def extract_property_request_data( # Because we have some non-invasive recommendations that match on address and postcode, but not UPRN # we need to check existence of uprn - has_uprn = "uprn" in non_invasive_recommendations[0] if non_invasive_recommendations else True + has_uprn = "uprn" in non_invasive_recommendations[0] if non_invasive_recommendations else False if has_uprn: has_uprn = non_invasive_recommendations[0]["uprn"] not in ["", None] diff --git a/etl/customers/remote_assessments/app.py b/etl/customers/remote_assessments/app.py index cce0f4fb..ad97fd41 100644 --- a/etl/customers/remote_assessments/app.py +++ b/etl/customers/remote_assessments/app.py @@ -4,7 +4,7 @@ from dotenv import load_dotenv from utils.s3 import save_csv_to_s3 from etl.find_my_epc.AssetListEpcData import AssetListEpcData -PORTFOLIO_ID = 129 +PORTFOLIO_ID = 132 USER_ID = 8 load_dotenv(dotenv_path="backend/.env") @@ -19,9 +19,11 @@ def app(): asset_list = [ { - "address": "19", - "postcode": "IP21 4YJ", - "uprn": 2630134524, + "address": "3", + "postcode": "BB8 0JF", + "uprn": 100010509503, + "property_type": "House", + "built_form": "End-Terrace", } ] asset_list = pd.DataFrame(asset_list) @@ -52,8 +54,8 @@ def app(): valuation_data = [ { - "uprn": 2630134524, - "valuation": 96_000 + "uprn": 100010509503, + "valuation": 116_000 } ] # Store valuation data to s3