diff --git a/backend/SearchEpc.py b/backend/SearchEpc.py index cc2ee4a9..44178792 100644 --- a/backend/SearchEpc.py +++ b/backend/SearchEpc.py @@ -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() diff --git a/backend/app/plan/router.py b/backend/app/plan/router.py index 9854abe8..a8464ee6 100644 --- a/backend/app/plan/router.py +++ b/backend/app/plan/router.py @@ -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 diff --git a/backend/ml_models/Valuation.py b/backend/ml_models/Valuation.py index 251c016a..39ea5a98 100644 --- a/backend/ml_models/Valuation.py +++ b/backend/ml_models/Valuation.py @@ -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 diff --git a/etl/customers/immo/pilot/asset_list_2.py b/etl/customers/immo/pilot/asset_list_2.py index f722a490..121e7a81 100644 --- a/etl/customers/immo/pilot/asset_list_2.py +++ b/etl/customers/immo/pilot/asset_list_2.py @@ -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( diff --git a/etl/epc/Record.py b/etl/epc/Record.py index e74330a2..9a965c6a 100644 --- a/etl/epc/Record.py +++ b/etl/epc/Record.py @@ -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, ) diff --git a/recommendations/SolarPvRecommendations.py b/recommendations/SolarPvRecommendations.py index 58cf9735..b44557ab 100644 --- a/recommendations/SolarPvRecommendations.py +++ b/recommendations/SolarPvRecommendations.py @@ -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