working on thrive and added method for calculating led costs given an isntaller quote

etl/customers/places_for_people/finalise_programme.py

@@ -138,6 +138,7 @@ not_seen["Note"] = np.where(
 not_seen["Note"] = not_seen["Note"].fillna("Property not in original lists")
 
 # Store
-not_seen = os.path.join(
-    data_folder, "Reconciled Programme/Properties not inspected by Domna.xlsx"
+not_seen.to_csv(
+    os.path.join(data_folder, "Reconciled Programme/Properties not inspected by Domna.csv"),
+    index=False
 )

etl/customers/thrive/Programme Analysis.py

@@ -0,0 +1,97 @@
+"""
+The Thrive programme has not been completed to specification. This script re-builds the programme and attempts to
+address the following concerns:
+- Which properties have been surveyed
+- Of the properties that have been surveyed, what has been installed
+- Which properties have been visited
+
+"""
+
+import pandas as pd
+
+# This is Thrive's list of properties and when they should have been surveyed
+thrive_tracker = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/Programme Reconciliation/Thrive Asset List - "
+    "Standardised.xlsx",
+    sheet_name="Tracker",
+    header=2
+)
+
+original_asset_list = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/Programme Reconciliation/Thrive Asset List.xlsx",
+    header=0
+)
+
+# Find properties that are on the thrive tracker that
+missed_properties = thrive_tracker[
+    ~thrive_tracker["UPRN"].astype(str).isin(original_asset_list["Client ref 1"].astype(str).values)
+].copy()
+
+# We produce the complete list, with all of the columns we need, for data standardisation
+
+original_columns = {
+    "Client ref 1": "thrive_property_id",
+    "Address": "full_address",
+    "Address Line 1": "address_line_1",
+    "Address Line 2": "address_line_2",
+    "Address Line 3": "address_line_3",
+    "Address Line 4": "address_line_4",
+    "County": "county",
+    "Postcode": "postcode",
+    "Block Name": "block_reference",
+    "Construction Year": "construction_year",
+    "Age band (calculated)": "age_band_calculated",
+    "Property type": "property_type",
+    "Client property type 1": "detailed_property_type",
+    "Client property type 2": "detailed_property_type_2",
+    "bed count": "number_of_bedrooms",
+    "Heating Type": "heating_type",
+    "WFT Findings": "WFT Findings",
+    "ECO Eligibility": "ECO Eligibility",
+}
+
+original_asset_list = original_asset_list[original_columns.keys()].rename(columns=original_columns)
+original_asset_list["Data Source"] = "Thrive Tracker"
+
+# We append on the missed properties, with the information we have
+# 'Unnamed: 0', 'Thrive Notes', 'Priority', 'UPRN', 'Short Address', '#',
+#        'Adress Line 1', 'Postcode', 'Property Type', 'Build Form',
+#        'Build year', 'Assumed mm ', 'SAP', 'Name', 'Primary Number',
+#        'Secondary Number', 'Email', 'Thrive use: Tenancy Number',
+#        'Special Requirements ', 'CIGA', 'Date CIGA check received',
+#        'Proposed Progamme', 'New Proposed Programme',
+#        'Missing from Route March?', 'Date Letters Sent (w.c)', 'Work Type',
+#        'Warmfront Survey Notes', 'Notes Reply (Thrive)', 'Loft Insulation',
+#        'Cost for Vents', 'Cavity Depth', 'Cavity Condition',
+#        'Date Submitted to installer', 'PRRN Number',
+#        'Loft insulation required? (Thrive)', 'Date booked ',
+#        'Completed\n(yes/no)', 'Date Completed',
+#        'Vents installed?\n(number and location)',
+#        'Loft Top Up\n(amount of insulation) ', 'CIGA Warranty Provided ',
+#        'Notes', 'Works Number', 'CW KGI Uploaded', 'Keystone Fan Added',
+#        'SA Cavity Condition Updated', 'SA Loft & Energy Updated',
+#        'PRRN Submitted '
+
+missed_properties["Full Address"] = (
+    missed_properties["#"].astype(str) + ", " +
+    missed_properties["Adress Line 1"].astype(str) + ", " +
+    missed_properties["Postcode"].astype(str)
+)
+missed_columns = {
+    "UPRN": "thrive_property_id",
+    "Full Address": "full_address",
+    "Short Address": "address_line_1",
+    "Postcode": "postcode",
+    "Property Type": "property_type",
+    "Build Form": "build_form",
+    "Build year": "age_band_calculated",
+    "Assumed mm ": "assumed_loft_insulation_thickness",
+    "SAP": "sap_rating",
+}
+
+missed_properties = missed_properties[missed_columns.keys()].rename(columns=missed_columns)
+missed_properties["WFT Findings"] = "Property Not Inspected"
+missed_properties["ECO Eligibility"] = "Property Not Inspected"
+missed_properties["Data Source"] = "Thrive Tracker"
+
+master_list = pd.concat([missed_properties, original_asset_list], ignore_index=True)

recommendations/Costs.py

@@ -601,11 +601,24 @@ class Costs:
 
         # If there are no lights fitted in the property, we increase the contingency in case there are potential wiring
         # blockers
+
         if number_current_lel_lights == 0:
             contingency = self.HIGH_RISK_CONTINGENCY
         else:
             contingency = self.CONTINGENCY
 
+        if material["is_installer_quote"]:
+            total_cost = material["total_cost"] * number_of_lights * (1 + contingency)
+
+            labour_hours = 1
+            labour_days = (labour_hours / 8)
+
+            return {
+                "total": total_cost,
+                "labour_hours": labour_hours,
+                "labour_days": labour_days,
+            }
+
         material_cost = material["material_cost"] * number_of_lights
         labour_cost = material["labour_cost"] * number_of_lights * self.labour_adjustment_factor