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https://github.com/Hestia-Homes/Model.git
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Merge pull request #452 from Hestia-Homes/debugging-api
adding extra inputs into backend body
This commit is contained in:
KhalimCK
GitHub
commit
ac139174b9
9 changed files with 256 additions and 4416 deletions
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@ -2622,7 +2622,8 @@ class AssetList:
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# Add in deal and pipeline information
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programme_data["dealname"] = (
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programme_data[self.STANDARD_FULL_ADDRESS] + " : " + programme_data["domna_product"]
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programme_data[self.STANDARD_FULL_ADDRESS] + ", " +
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programme_data[self.STANDARD_POSTCODE] + " : " + programme_data["domna_product"]
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)
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programme_data['Pipeline <DEAL pipeline>'] = hubspot_config.CRM_PIPELINE_NAME
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programme_data['Associations: Listing'] = "Property Owner"
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@ -2656,7 +2657,11 @@ class AssetList:
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# Ammend the property type and built form columns
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programme_data["hubspot_property_type"] = programme_data[self.STANDARD_PROPERTY_TYPE].copy()
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programme_data["hubspot_built_form"] = programme_data[self.STANDARD_BUILT_FORM].copy()
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# We don't already have this
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if self.STANDARD_BUILT_FORM in programme_data.columns:
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programme_data["hubspot_built_form"] = programme_data[self.STANDARD_BUILT_FORM].copy()
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else:
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programme_data["hubspot_built_form"] = None
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def _replace_property_description_data(programme_data, column_name):
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"""
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@ -13,10 +13,22 @@ from backend.app.utils import sap_to_epc
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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# project = pd.read_excel(
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# )
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#
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# cavity = project[project["cavity_reason"].isin(
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# ["EPC Shows Empty Cavity: SAP Rating 54 or less", "EPC Shows Empty Cavity: SAP Rating 55-68"]
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# )]
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asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/Livewest South-West - Standardised V2.xlsx",
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sheet_name="Cavity Route (Insta Review)"
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Abri/Abs Rates/Desktop ABRI data - Standardised After "
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"Programmes.xlsx",
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sheet_name="Reviewed List"
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)
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asset_list = asset_list[asset_list["cavity_reason"].isin(
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["EPC Shows Empty Cavity: SAP Rating 54 or less", "EPC Shows Empty Cavity: SAP Rating 55-68"]
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)]
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abs_matrix = pd.read_csv(
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"/Users/khalimconn-kowlessar/Downloads/ECO4 Full Project Scores Matrix.csv"
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@ -30,7 +42,7 @@ pps_matrix.columns = [c.strip() for c in pps_matrix.columns]
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# We need to estimate the number of points the work will produce and the finishing band. For this, we assume 7 for
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# cavity and 15 for solar. We'll be more specific in the future, but for now, this is a good enough estimate.
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route = asset_list[["domna_address_1", "domna_postcode", "epc_os_uprn"]].rename(
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columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "upr"}
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columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "uprn"}
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)
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route["address"] = route["address"].astype(str)
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@ -42,120 +54,121 @@ asset_list_epc_client = AssetListEpcData(
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asset_list_epc_client.get_data()
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asset_list_epc_client.get_non_invasive_recommendations()
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solar_sap_points = []
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for r in asset_list_epc_client.non_invasive_recommendations:
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if not r.get("recommendations"):
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continue
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solar_recommendations = [
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x for x in r["recommendations"] if "solar_pv" in x["type"]
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]
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if solar_recommendations:
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solar_recommendations = solar_recommendations[0]
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else:
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continue
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address = r["address"]
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postcode = r["postcode"]
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solar_sap_points.append(
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{
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"address": address,
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"postcode": postcode,
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"sap_points": solar_recommendations["sap_points"]
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}
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)
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solar_sap_points = pd.DataFrame(solar_sap_points)
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solar_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
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# Store the sap points in the cavity route to csv
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# cwi_sap_points.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/cwi_sap_points_livewest_sw.csv",
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# index=False
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# )
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avg_solar_points_by_postcode = solar_sap_points.groupby(["postcode"]).agg({"sap_points": "mean"}).reset_index()
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avg_solar_points = solar_sap_points["sap_points"].median()
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asset_list["domna_address_1"] = asset_list["domna_address_1"].astype(str)
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asset_list = asset_list.merge(
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solar_sap_points, how="left", left_on=["domna_address_1", "domna_postcode"], right_on=["address", "postcode"]
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).drop(
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columns=["address", "postcode"]
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)
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# Fill the sap points with the average cwi points
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asset_list = asset_list.merge(
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avg_solar_points_by_postcode.rename(columns={"postcode": "domna_postcode"}),
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how="left", on=["domna_postcode"], suffixes=("", "_avg")
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)
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asset_list["sap_points"] = asset_list["sap_points"].fillna(asset_list["sap_points_avg"])
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asset_list.drop(columns=["sap_points_avg"], inplace=True)
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asset_list["sap_points"] = asset_list["sap_points"].fillna(avg_solar_points)
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asset_list["post_works_sap"] = asset_list["epc_sap_score_on_register"] + asset_list["sap_points"]
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asset_list["post_works_epc"] = asset_list["post_works_sap"].apply(lambda x: sap_to_epc(x))
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asset_list["starting_half_band"] = asset_list["epc_sap_score_on_register"].apply(lambda x: Funding.get_sap_band(x))
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asset_list["ending_half_band"] = asset_list["post_works_sap"].apply(lambda x: Funding.get_sap_band(x))
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asset_list["floor_area_band"] = asset_list["epc_total_floor_area"].apply(lambda x: Funding.get_floor_area_band(x))
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asset_list["ending_half_band"] = np.where(
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(asset_list["post_works_epc"] == asset_list["epc_rating_on_register"]),
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"Low_C",
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asset_list["ending_half_band"]
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)
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# Realistically, we'll take the properties to a low C at worst
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asset_list["ending_half_band"] = np.where(
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(asset_list["post_works_sap"] < 69),
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"Low_C",
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asset_list["ending_half_band"]
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)
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asset_list = asset_list.merge(
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abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
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right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
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)
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asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
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asset_list = asset_list.rename(
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columns={"Cost Savings": "funding_abs"}
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)
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print(asset_list["domna_property_id"].duplicated().sum())
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# Store this data
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asset_list.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/livewest_sw_solar_abs_estimates-solar.csv",
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index=False
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)
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# Cavity process!
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# cwi_sap_points = []
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# solar_sap_points = []
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# for r in asset_list_epc_client.non_invasive_recommendations:
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# if not r.get("recommendations"):
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# continue
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# cwi_recommendations = [
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# x for x in r["recommendations"] if "cavity_wall_insulation" in x["type"]
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# solar_recommendations = [
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# x for x in r["recommendations"] if "solar_pv" in x["type"]
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# ]
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# if cwi_recommendations:
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# cwi_recommendations = cwi_recommendations[0]
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# if solar_recommendations:
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# solar_recommendations = solar_recommendations[0]
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# else:
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# continue
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#
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# address = r["address"]
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# postcode = r["postcode"]
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#
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# cwi_sap_points.append(
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# solar_sap_points.append(
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# {
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# "address": address,
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# "postcode": postcode,
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# "sap_points": cwi_recommendations["sap_points"]
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# "sap_points": solar_recommendations["sap_points"]
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# }
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# )
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#
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# cwi_sap_points = pd.DataFrame(cwi_sap_points)
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# cwi_sap_points = pd.read_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/cwi_sap_points_livewest_sw.csv"
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# solar_sap_points = pd.DataFrame(solar_sap_points)
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# solar_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
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# # Store the sap points in the cavity route to csv
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# solar_sap_points.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Abri/Abs Rates/cwi_sap_points.csv",
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# index=False
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# )
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# cwi_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
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#
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# avg_solar_points_by_postcode = solar_sap_points.groupby(["postcode"]).agg({"sap_points": "mean"}).reset_index()
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# avg_solar_points = solar_sap_points["sap_points"].median()
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# asset_list["domna_address_1"] = asset_list["domna_address_1"].astype(str)
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# asset_list = asset_list.merge(
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# solar_sap_points, how="left", left_on=["domna_address_1", "domna_postcode"], right_on=["address", "postcode"]
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# ).drop(
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# columns=["address", "postcode"]
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# )
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#
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# # Fill the sap points with the average cwi points
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# asset_list = asset_list.merge(
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# avg_solar_points_by_postcode.rename(columns={"postcode": "domna_postcode"}),
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# how="left", on=["domna_postcode"], suffixes=("", "_avg")
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# )
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# asset_list["sap_points"] = asset_list["sap_points"].fillna(asset_list["sap_points_avg"])
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# asset_list.drop(columns=["sap_points_avg"], inplace=True)
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#
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# asset_list["sap_points"] = asset_list["sap_points"].fillna(avg_solar_points)
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# asset_list["post_works_sap"] = asset_list["epc_sap_score_on_register"] + asset_list["sap_points"]
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# asset_list["post_works_epc"] = asset_list["post_works_sap"].apply(lambda x: sap_to_epc(x))
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# asset_list["starting_half_band"] = asset_list["epc_sap_score_on_register"].apply(lambda x: Funding.get_sap_band(x))
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# asset_list["ending_half_band"] = asset_list["post_works_sap"].apply(lambda x: Funding.get_sap_band(x))
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# asset_list["floor_area_band"] = asset_list["epc_total_floor_area"].apply(lambda x: Funding.get_floor_area_band(x))
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#
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# asset_list["ending_half_band"] = np.where(
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# (asset_list["post_works_epc"] == asset_list["epc_rating_on_register"]),
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# "Low_C",
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# asset_list["ending_half_band"]
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# )
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# # Realistically, we'll take the properties to a low C at worst
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# asset_list["ending_half_band"] = np.where(
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# (asset_list["post_works_sap"] < 69),
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# "Low_C",
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# asset_list["ending_half_band"]
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# )
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#
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# asset_list = asset_list.merge(
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# abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
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# right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
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# )
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# asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
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#
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# asset_list = asset_list.rename(
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# columns={"Cost Savings": "funding_abs"}
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# )
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#
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# print(asset_list["domna_property_id"].duplicated().sum())
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#
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# # Store this data
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# asset_list.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/livewest_sw_solar_abs_estimates-solar.csv",
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# index=False
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# )
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# Cavity process!
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cwi_sap_points = []
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for r in asset_list_epc_client.non_invasive_recommendations:
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if not r.get("recommendations"):
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continue
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cwi_recommendations = [
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x for x in r["recommendations"] if "cavity_wall_insulation" in x["type"]
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]
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if cwi_recommendations:
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cwi_recommendations = cwi_recommendations[0]
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else:
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continue
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address = r["address"]
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postcode = r["postcode"]
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cwi_sap_points.append(
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{
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"address": address,
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"postcode": postcode,
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"type": cwi_recommendations["type"],
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"sap_points": cwi_recommendations["sap_points"]
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}
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)
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cwi_sap_points = pd.DataFrame(cwi_sap_points)
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cwi_sap_points = pd.read_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Abri/Abs Rates/cwi_sap_points.csv",
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)
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cwi_sap_points.drop_duplicates(subset=["address", "postcode"], inplace=True)
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avg_cwi_points_by_postcode = cwi_sap_points.groupby(["postcode"]).agg({"sap_points": "mean"}).reset_index()
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avg_cwi_points = cwi_sap_points["sap_points"].median()
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asset_list = asset_list.merge(
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@ -186,13 +199,22 @@ asset_list["funding_scheme"] = np.where(
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"GBIS",
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"ECO4"
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)
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# Note - anything that is EPC E or below that doesn't go up to a C will be GBIS
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# To detect this, if the starting sap score is 54 or below and the endding SAP sore is 68 or below
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# we will assume it is GBIS
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asset_list["funding_scheme"] = np.where(
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(asset_list["post_works_sap"] < 69) & (asset_list["epc_sap_score_on_register"] < 55),
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"GBIS",
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asset_list["funding_scheme"]
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)
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asset_list = asset_list.merge(
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abs_matrix, how="left", left_on=["starting_half_band", "ending_half_band", "floor_area_band"],
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right_on=['Starting Band', 'Finishing Band', 'Floor Area Segment', ]
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)
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asset_list = asset_list.drop(columns=['Starting Band', 'Finishing Band', 'Floor Area Segment'])
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# Using CWI solid 1.7 -> 0.3 rates
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# Using CWI 0.033 as the partial project score
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cwi_pps_matrix = pps_matrix[
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pps_matrix["Measure_Type"].isin(["CWI_0.033"])
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]
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@ -220,10 +242,26 @@ asset_list["funding_abs"] = np.where(
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asset_list["Cost Savings"]
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)
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asset_list["domna_property_id"].duplicated().sum()
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from recommendations.recommendation_utils import (
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estimate_external_wall_area,
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)
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# For some reason, estimated insulation wall area is missing
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asset_list["estimated_insulation_wall_area"] = asset_list.apply(
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lambda x: estimate_external_wall_area(
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num_floors=x["attribute_est_number_floors"],
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floor_height=(
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float(x["epc_floor_height"]) if
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not pd.isnull(x["epc_floor_height"]) else 2.5
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),
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perimeter=x["attribute_est_perimter"],
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built_form=x["epc_archetype"]
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),
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axis=1
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)
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# Store this data
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asset_list.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Instagroup Review/livewest_sw_abs_estimates.csv",
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Abri/Abs Rates/Abri CWI ABS Estimates.csv",
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index=False
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)
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|
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@ -60,11 +60,11 @@ def app():
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"""
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# TODO: Delete me
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data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/Apr 2025 Programme Rebuild"
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data_filename = "Bromford Asset List.xlsx"
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sheet_name = "Asset List"
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postcode_column = 'PostCode'
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fulladdress_column = "FullAddress"
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data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/NRLA/"
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data_filename = "20250716 Asset List.xlsx"
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sheet_name = "Sheet 1"
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postcode_column = 'Postcode'
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fulladdress_column = "Full Address"
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address1_column = None
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address1_method = "house_number_extraction"
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address_cols_to_concat = []
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@ -76,24 +76,93 @@ def app():
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landlord_wall_construction = None
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landlord_heating_system = None
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landlord_existing_pv = None
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landlord_property_id = "Asset"
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landlord_property_id = "Row ID"
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outcomes_filename = []
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outcomes_sheetname = []
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outcomes_postcode = []
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outcomes_houseno = []
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outcomes_address = []
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outcomes_id = [None]
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master_filepaths = [os.path.join("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/",
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"Needs ID/SOLAR PV ONLY-Table 1.csv")]
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outcomes_id = []
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master_filepaths = []
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master_to_asset_list_filepath = None
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asset_list_header = 0
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landlord_block_reference = None
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master_id_colnames = [None]
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master_id_colnames = []
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landlord_roof_construction = None
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phase = False
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landlord_sap = None
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ecosurv_landlords = None
|
||||
|
||||
# Southend
|
||||
data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Southend/July 2025 Programme"
|
||||
data_filename = "SOUTHEND - RYAN.xlsx"
|
||||
sheet_name = "July 2025 Surveys"
|
||||
postcode_column = 'Postcode'
|
||||
fulladdress_column = "Full postal address"
|
||||
address1_column = None
|
||||
address1_method = "house_number_extraction"
|
||||
address_cols_to_concat = []
|
||||
missing_postcodes_method = None
|
||||
landlord_year_built = "Property age"
|
||||
landlord_os_uprn = None
|
||||
landlord_property_type = "Property type"
|
||||
landlord_built_form = "Property type"
|
||||
landlord_wall_construction = None
|
||||
landlord_heating_system = None
|
||||
landlord_existing_pv = None
|
||||
landlord_property_id = "ID"
|
||||
outcomes_filename = []
|
||||
outcomes_sheetname = []
|
||||
outcomes_postcode = []
|
||||
outcomes_houseno = []
|
||||
outcomes_address = []
|
||||
outcomes_id = []
|
||||
master_filepaths = []
|
||||
master_to_asset_list_filepath = None
|
||||
asset_list_header = 0
|
||||
landlord_block_reference = None
|
||||
master_id_colnames = []
|
||||
landlord_roof_construction = None
|
||||
phase = False
|
||||
landlord_sap = None
|
||||
ecosurv_landlords = None
|
||||
|
||||
# For Rooftop
|
||||
data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Rooftop"
|
||||
data_filename = "Rooftop Asset List - July 2025.xlsx"
|
||||
sheet_name = "Sheet1"
|
||||
postcode_column = 'post_code'
|
||||
fulladdress_column = None
|
||||
address1_column = "add_1"
|
||||
address1_method = None
|
||||
address_cols_to_concat = [
|
||||
"add_1", "add_2", "add_3", "add_4"
|
||||
]
|
||||
missing_postcodes_method = None
|
||||
landlord_year_built = "date_built"
|
||||
landlord_os_uprn = None
|
||||
landlord_property_type = "ConstructionStyle"
|
||||
landlord_built_form = "ConstructionStyle"
|
||||
landlord_wall_construction = None
|
||||
landlord_heating_system = "Description"
|
||||
landlord_existing_pv = None
|
||||
landlord_property_id = "PropertyCode"
|
||||
outcomes_filename = [os.path.join(data_folder, "Rooftop_Outcomes.xlsx")]
|
||||
outcomes_sheetname = ["OUTCOMESs"]
|
||||
outcomes_postcode = ["POSTCODE"]
|
||||
outcomes_houseno = ["NO"]
|
||||
outcomes_address = ["ADDRESS"]
|
||||
outcomes_id = [None]
|
||||
master_filepaths = [os.path.join(data_folder, "Master.csv")]
|
||||
master_to_asset_list_filepath = None
|
||||
asset_list_header = 1
|
||||
landlord_block_reference = "bl_rec_ref"
|
||||
master_id_colnames = [None]
|
||||
landlord_roof_construction = None
|
||||
phase = False
|
||||
landlord_sap = None
|
||||
ecosurv_landlords = "rooftop"
|
||||
|
||||
# For Housing
|
||||
data_folder = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/For Housing/New Programme July 2025"
|
||||
data_filename = "FOR HOUSING Asset List (Combined).xlsx"
|
||||
|
|
|
|||
|
|
@ -45,13 +45,13 @@ def app():
|
|||
|
||||
# inputs:
|
||||
reconcile_programme = True # If True, the hubspot upload will include all properties with a project code
|
||||
customer_domain = "https://calico.org.uk"
|
||||
installer_name = "WARM FRONT"
|
||||
customer_domain = "https://southend.gov.uk"
|
||||
installer_name = "J & J CRUMP"
|
||||
asset_list_filepath = (
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Calico/Hubspot/07.04 CALICO - Final List - "
|
||||
"Standardised.xlsx"
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Southend/July 2025 Programme/SOUTHEND - RYAN - "
|
||||
"Standardised 2.xlsx"
|
||||
)
|
||||
asset_list_sheet_name = "Final Route March"
|
||||
asset_list_sheet_name = "Standardised Asset List"
|
||||
asset_list_header = 0
|
||||
|
||||
contact_details_filepath = None
|
||||
|
|
@ -107,7 +107,7 @@ def app():
|
|||
raise ValueError("FIX MEEE")
|
||||
|
||||
if pd.isnull(asset_list.hubspot_data['Deal Stage <DEAL dealstage>']).any():
|
||||
raise ValueError("Warning: Some rows have missing project codes. These will not be uploaded to HubSpot.")
|
||||
raise ValueError("Warning: Some rows have missing deal stage. These will not be uploaded to HubSpot.")
|
||||
|
||||
# Just store locally
|
||||
asset_list.hubspot_data.to_csv(output_filepath, index=False, encoding="utf-8-sig")
|
||||
|
|
|
|||
|
|
@ -384,6 +384,7 @@ BUILT_FORM_MAPPINGS = {
|
|||
'Cottage Flat': 'ground floor',
|
||||
'Maisonette Over Shop': 'mid-floor',
|
||||
'Medium Rise Flat': 'mid-floor',
|
||||
'Maisonette Medium Rise': 'unknown'
|
||||
'Maisonette Medium Rise': 'unknown',
|
||||
'End-terraced house': 'end-terrace'
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -102,3 +102,9 @@ class PlanTriggerRequest(BaseModel):
|
|||
# If true, before optimising the engine will select a slightly larger package, to account for the SAP 10 causing
|
||||
# scores to drop by a few points
|
||||
simulate_sap_10: Optional[bool] = False
|
||||
|
||||
# Add in optional fields which describe the format of the asset list being used
|
||||
|
||||
file_type: Optional[Literal["csv", "xlsx"]] = None,
|
||||
file_format: Optional[Literal["domna_asset_list"]] = None,
|
||||
sheet_name: Optional[str] = None
|
||||
|
|
|
|||
12
etl/customers/abri/abs_rates.py
Normal file
12
etl/customers/abri/abs_rates.py
Normal file
|
|
@ -0,0 +1,12 @@
|
|||
import pandas as pd
|
||||
|
||||
project = pd.read_excel(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Abri/Abs Rates/Desktop ABRI data - Standardised After "
|
||||
"Programmes.xlsx"
|
||||
)
|
||||
|
||||
cavity = project[project["cavity_reason"].isin(
|
||||
["EPC Shows Empty Cavity: SAP Rating 54 or less", "EPC Shows Empty Cavity: SAP Rating 55-68"]
|
||||
)]
|
||||
|
||||
# Pull the data
|
||||
File diff suppressed because it is too large
Load diff
|
|
@ -681,7 +681,9 @@ class RetrieveFindMyEpc:
|
|||
],
|
||||
"High heat retention storage heaters and dual rate meter": [
|
||||
"high_heat_retention_storage_heater"
|
||||
]
|
||||
],
|
||||
"Increase loft insulation to 250mm": ["loft_insulation"],
|
||||
"Solar photovoltaics panels, 25% of roof area": ["solar_pv"],
|
||||
}
|
||||
|
||||
survey = True
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue