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rounding up roof coverage %

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This commit is contained in:
Khalim Conn-Kowlessar 2024-04-18 15:16:46 +01:00
parent e000c87cad
commit acada27061
6 changed files with 50 additions and 11 deletions
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9
backend/SearchEpc.py
View file

@ -709,8 +709,13 @@ class SearchEpc:
self.full_sap_epc = {}
# Finally, set a standardised address 1 and postcode
self.address_clean = self.ordnance_survey_client.address_os
self.postcode_clean = self.ordnance_survey_client.postcode_os
self.address_clean = (
self.ordnance_survey_client.address_os if self.ordnance_survey_client.address_os else self.address1
)
self.postcode_clean = (
self.ordnance_survey_client.postcode_os if self.ordnance_survey_client.postcode_os else
self.postcode
)
return
os_response = self.ordnance_survey_client.get_places_api()

11
backend/app/plan/router.py
View file

@ -52,6 +52,10 @@ def patch_epc(patch, epc_records):
"""
for patch_variable, patch_value in patch.items():
if patch_variable in ["address", "postcode"]:
continue
if patch_value == "":
continue
if patch_variable in epc_records["original_epc"]:
@ -268,9 +272,12 @@ async def trigger_plan(body: PlanTriggerRequest):
postcode=config["postcode"],
uprn=uprn,
auth_token=get_settings().EPC_AUTH_TOKEN,
os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY
os_api_key=get_settings().ORDNANCE_SURVEY_API_KEY,
)
epc_searcher.find_property()
epc_searcher.ordnance_survey_client.built_form = config.get("built_form", None)
epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
# For the moment, our OS API access is unavailable, so we skip and interpolate
epc_searcher.find_property(skip_os=True)
# Create a record in db
property_id, is_new = create_property(
session, body.portfolio_id, epc_searcher.address_clean, epc_searcher.postcode_clean, epc_searcher.uprn

8
backend/ml_models/Valuation.py
View file

@ -63,6 +63,14 @@ class PropertyValuation:
90093693: 279_000, # Based on Zoopla
90055152: 149_000, # Based on Zoopla
90028499: 238_000, # Based on Zoopla
# IMMO Dudley Pilot 2- search by going to https://www.zoopla.co.uk/property/uprn/{uprn}/
90039318: 177_000, # Based on Zoopla
90038384: 170_000, # Based on Zoopla
90105380: 185_000, # Based on Zoopla
90124001: 165_000, # Based on Zoopla
90013980: 148_000, # Based on Zoopla
90087154: 184_000, # Based on Zoopla
90046817: 167_000, # Based on Zoopla
}
# We base our valuation uplifts on a number of sources

21
etl/customers/immo/pilot/asset_list_2.py
View file

@ -10,6 +10,7 @@ patches = [
{
'address': '116 Parkes Hall Road',
'postcode': 'DY1 3RJ',
'uprn': '90046817',
'walls-description': 'Cavity wall, filled cavity',
'walls-energy-eff': 'Average',
'roof-description': 'Pitched, 270 mm loft insulation',
@ -21,7 +22,7 @@ patches = [
'mainheatcont-description': 'Programmer, room thermostat and TRVs',
'mainheatc-energy-eff': 'Good',
'lighting-description': 'Low energy lighting in 27% of fixed outlets',
'lighting-energy-eff': 'Good',
'lighting-energy-eff': 'Average',
'floor-description': 'Solid, no insulation (assumed)',
'secondheat-description': 'None',
'current-energy-efficiency': '73',
@ -39,7 +40,11 @@ patches = [
# This is information that is found as a result of the non-invasives, that mean that certain measures
# have been installed already. To reflect this in the front end, it is included in the recommendation, however
# the cost is removed and instead, a message is presented saying that the measure is already installed.
already_installed = []
already_installed = [
{
'address': '28 Sangwin Road', 'postcode': 'WV14 9EQ', "already_installed": ["loft_insulation"]
}
]
non_invasive_recommendations = []
@ -58,13 +63,23 @@ def app():
raw_asset_list["postcode"] = raw_asset_list["Full Address"].str.split(",").str[-1].str.strip()
# We're provided with number of bathrooms and number of bedrooms.
# THe UPRNs are not the official ones
asset_list = raw_asset_list.rename(
columns={
"No. of Beds": "n_bedrooms",
"No. of WC's": "n_bathrooms"
"No. of WC's": "n_bathrooms",
'Property Type': 'property_type',
'Architype': 'built_form'
}
)
# Remap the values
asset_list["built_form"] = asset_list["built_form"].map({
"SEMI DETACHED": "Semi-Detached",
"MID TERRACE": "Mid-Terrace",
"END TERRACE": "End-Terrace",
})
# Store the asset list in s3
filename = f"{USER_ID}/{PORTFOLIO_ID}/pilot.csv"
save_csv_to_s3(

2
etl/epc/Record.py
View file

@ -191,7 +191,7 @@ class EPCRecord:
This method will clean the records using the data processor
"""
epc_data_processor = EPCDataProcessor(
data=self.epc_record_as_dataframe("prepared_epc"),
data=self.epc_record_as_dataframe("prepared_epc").copy(),
run_mode="newdata",
cleaning_averages=self.cleaning_data,
)

10
recommendations/SolarPvRecommendations.py
View file

@ -56,14 +56,18 @@ class SolarPvRecommendations:
if not is_valid_property_type or not is_valid_roof_type or not has_no_existing_solar_pv:
return
solar_pv_percentage = self.property.solar_pv_percentage
# We round up to the neaest 10%
solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10
# For the solar recommendations, we produce the following scenarios:
# 1) Solar panels only, we present a high, medium and low coverage
# 2) With and without battery
roof_coverage_scenarios = [
self.property.solar_pv_percentage - 0.1, self.property.solar_pv_percentage,
solar_pv_percentage - 0.1, solar_pv_percentage,
]
if self.property.solar_pv_percentage <= 0.4:
roof_coverage_scenarios.append(self.property.solar_pv_percentage + 0.1)
if solar_pv_percentage <= 0.4:
roof_coverage_scenarios.append(solar_pv_percentage + 0.1)
# We make sure we haven't gone too low or high - we allow no more than 60% coverage
roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
# If we only have two scenarios, we add a coverage scenario 10% less than the smallest