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Merge pull request #440 from Hestia-Homes/remote-assessment-api
Fixing led costing methodology
This commit is contained in:
KhalimCK
GitHub
commit
7c5eaa698c
3 changed files with 131 additions and 5 deletions
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@ -138,6 +138,7 @@ not_seen["Note"] = np.where(
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not_seen["Note"] = not_seen["Note"].fillna("Property not in original lists")
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# Store
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not_seen = os.path.join(
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data_folder, "Reconciled Programme/Properties not inspected by Domna.xlsx"
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not_seen.to_csv(
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os.path.join(data_folder, "Reconciled Programme/Properties not inspected by Domna.csv"),
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index=False
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)
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114
etl/customers/thrive/Programme Analysis.py
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114
etl/customers/thrive/Programme Analysis.py
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@ -0,0 +1,114 @@
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"""
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The Thrive programme has not been completed to specification. This script re-builds the programme and attempts to
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address the following concerns:
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- Which properties have been surveyed
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- Of the properties that have been surveyed, what has been installed
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- Which properties have been visited
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"""
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import pandas as pd
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# This is Thrive's list of properties and when they should have been surveyed
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thrive_tracker = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/Programme Reconciliation/Thrive Asset List - "
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"Standardised.xlsx",
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sheet_name="Tracker",
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header=2
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)
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original_asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/Programme Reconciliation/Thrive Asset List.xlsx",
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header=0
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)
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# Find properties that are on the thrive tracker that
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missed_properties = thrive_tracker[
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~thrive_tracker["UPRN"].astype(str).isin(original_asset_list["Client ref 1"].astype(str).values)
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].copy()
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# We produce the complete list, with all of the columns we need, for data standardisation
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original_columns = {
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"Client ref 1": "thrive_property_id",
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"Address": "full_address",
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"Address Line 1": "address_line_1",
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"Address Line 2": "address_line_2",
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"Address Line 3": "address_line_3",
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"Address Line 4": "address_line_4",
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"County": "county",
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"Postcode": "postcode",
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"Block Name": "block_reference",
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"Construction Year": "construction_year",
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"Age band (calculated)": "age_band_calculated",
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"Property type": "property_type",
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"Client property type 1": "detailed_property_type",
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"Client property type 2": "detailed_property_type_2",
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"bed count": "number_of_bedrooms",
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"Heating Type": "heating_type",
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"WFT Findings": "WFT Findings",
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"ECO Eligibility": "ECO Eligibility",
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}
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original_asset_list = original_asset_list[original_columns.keys()].rename(columns=original_columns)
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original_asset_list["Data Source"] = "Thrive Tracker"
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# We append on the missed properties, with the information we have
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# 'Unnamed: 0', 'Thrive Notes', 'Priority', 'UPRN', 'Short Address', '#',
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# 'Adress Line 1', 'Postcode', 'Property Type', 'Build Form',
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# 'Build year', 'Assumed mm ', 'SAP', 'Name', 'Primary Number',
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# 'Secondary Number', 'Email', 'Thrive use: Tenancy Number',
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# 'Special Requirements ', 'CIGA', 'Date CIGA check received',
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# 'Proposed Progamme', 'New Proposed Programme',
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# 'Missing from Route March?', 'Date Letters Sent (w.c)', 'Work Type',
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# 'Warmfront Survey Notes', 'Notes Reply (Thrive)', 'Loft Insulation',
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# 'Cost for Vents', 'Cavity Depth', 'Cavity Condition',
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# 'Date Submitted to installer', 'PRRN Number',
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# 'Loft insulation required? (Thrive)', 'Date booked ',
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# 'Completed\n(yes/no)', 'Date Completed',
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# 'Vents installed?\n(number and location)',
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# 'Loft Top Up\n(amount of insulation) ', 'CIGA Warranty Provided ',
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# 'Notes', 'Works Number', 'CW KGI Uploaded', 'Keystone Fan Added',
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# 'SA Cavity Condition Updated', 'SA Loft & Energy Updated',
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# 'PRRN Submitted '
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missed_properties["Full Address"] = (
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missed_properties["#"].astype(str) + ", " +
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missed_properties["Adress Line 1"].astype(str) + ", " +
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missed_properties["Postcode"].astype(str)
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)
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missed_columns = {
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"UPRN": "thrive_property_id",
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"Full Address": "full_address",
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"Short Address": "address_line_1",
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"Postcode": "postcode",
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"Property Type": "property_type",
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"Build Form": "build_form",
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"Build year": "age_band_calculated",
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"Assumed mm ": "assumed_loft_insulation_thickness",
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"SAP": "sap_rating",
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}
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missed_properties = missed_properties[missed_columns.keys()].rename(columns=missed_columns)
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missed_properties["WFT Findings"] = "Property Not Inspected"
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missed_properties["ECO Eligibility"] = "Property Not Inspected"
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missed_properties["Data Source"] = "Thrive Tracker"
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master_list = pd.concat([missed_properties, original_asset_list], ignore_index=True)
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# We were provided with a data update for a sample of properties. We update the data with this information
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data_update = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Thrive/Programme Reconciliation/Thrive Property List "
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"13_05.xlsx",
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header=0
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)
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new_properties = data_update[~data_update["UPRN"].isin(master_list["thrive_property_id"].astype(str).values)]
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data_update = = data_update[["UPRN", ""]]
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# TODO: Flag the Thrive priorities and create a separate project code for these
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# TODO: Add the general project code
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# TODO: Add the thrive
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@ -601,21 +601,32 @@ class Costs:
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# If there are no lights fitted in the property, we increase the contingency in case there are potential wiring
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# blockers
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if number_current_lel_lights == 0:
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contingency = self.HIGH_RISK_CONTINGENCY
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else:
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contingency = self.CONTINGENCY
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if material["is_installer_quote"]:
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total_cost = material["total_cost"] * number_of_lights * (1 + contingency)
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labour_hours = 1
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labour_days = (labour_hours / 8)
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return {
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"total": total_cost,
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"labour_hours": labour_hours,
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"labour_days": labour_days,
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}
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material_cost = material["material_cost"] * number_of_lights
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labour_cost = material["labour_cost"] * number_of_lights * self.labour_adjustment_factor
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subtotal_before_profit = material_cost + labour_cost
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contingency_cost = subtotal_before_profit * contingency
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preliminaries_cost = subtotal_before_profit * self.PRELIMINARIES
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profit_cost = subtotal_before_profit * self.PROFIT_MARGIN
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subtotal_before_vat = subtotal_before_profit + contingency_cost + preliminaries_cost + profit_cost
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subtotal_before_vat = subtotal_before_profit + contingency_cost
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vat_cost = subtotal_before_vat * self.VAT_RATE
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total_cost = subtotal_before_vat + vat_cost
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