Added ha7

etl/eligibility/ha_15_32/ha_analysis_batch_3.py

@@ -49,8 +49,19 @@ PROPERTY_TYPE_LOOKUP = {
         }
     },
     "HA7": {
-        "property_type": {},
-        "built_form": {}
+        "property_type": {
+            "House": "House",
+            "Flat": "Flat",
+            "Bungalow": "Bungalow",
+            "Maisonette": "Maisonette",
+        },
+        "built_form": {
+            "Semi Detached": "Semi-Detached",
+            "Mid Terrace": "Mid-Terrace",
+            "End Terrace": "End-Terrace",
+            "Detached": "Detached",
+            "End Terraced": "End-Terrace",
+        }
     },
     "HA14": {
         "property_type": {
@@ -1042,6 +1053,9 @@ def get_property_type_and_built_form(property_meta, ha_name):
     elif ha_name == "HA6":
         property_type = PROPERTY_TYPE_LOOKUP[ha_name]["property_type"][property_meta["Dwelling type"]]
         built_form = property_meta["built_form"]
+    elif ha_name == "HA7":
+        property_type = PROPERTY_TYPE_LOOKUP[ha_name]["property_type"][property_meta["Archetype"]]
+        built_form = PROPERTY_TYPE_LOOKUP[ha_name]["built_form"][property_meta["Property Type"]]
     elif ha_name == "HA14":
         if property_meta["Asset Type Description"] == "Block - Repair":
             # We try and deduce if it's a flat or house, depending on if it has "room" or "flats" in the address
@@ -1106,9 +1120,6 @@ def get_epc_data(
     outputs = {}
     for ha_name, data_assets in loader.data.items():
 
-        if ha_name == "HA39":
-            continue
-
         if not pull_data:
             # Then we retrieve the data from S3
             processed_ha_results = read_pickle_from_s3(
@@ -1135,7 +1146,6 @@ def get_epc_data(
         scoring_data = []
         nodata = []
         failed_model_rows = []
-        # Failed at index 13691
         for index, property_meta in tqdm(asset_list.iterrows(), total=len(asset_list)):
 
             if property_meta["matching_postcode"] is None:
@@ -1906,8 +1916,6 @@ def app():
     loader.load()
     loader.ha_facts_and_figures()
 
-    loader.facts_and_figures.to_csv("facts_and_figures.csv", index=False)
-
     # We load in the additional data required to perform the analysis
     cleaned = read_from_s3(
         s3_file_name="cleaned_epc_data/cleaned.bson",