mirror of
https://github.com/Hestia-Homes/survey-extraction.git
synced 2026-06-30 13:10:56 +00:00
deem score calculation works for all cavities
This commit is contained in:
Jun-te Kim
parent
94f7ca3ab6
commit
e1b61bca17
5 changed files with 244 additions and 45 deletions
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@ -53,9 +53,9 @@ class HubSpotClient():
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deal_id= deal.properties["hs_object_id"],
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deal_name=deal.properties["dealname"],
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work_type=deal.properties["work_type"],
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needs_trickle_ventilation=True if deal.properties["property_needs_trickle_vents"].upper() == "YES" else False,
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needs_trickle_ventilation=True if deal.properties.get("property_needs_trickle_vents") else False,
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post_sap_score=int(deal.properties["domna_survey_post_sap"]),
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existing_wall_insulation=deal.properties["existing_wall_insulation"],
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existing_wall_insulation=deal.properties.get("existing_wall_insulation") if deal.properties.get("existing_wall_insulation") else "None",
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no_of_wet_rooms=int(deal.properties["number_of_wet_rooms_needing_ventilation"]),
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installer=deal.properties["installer"],
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))
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@ -69,4 +69,4 @@ class HubSpotClient():
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print(f" - Label: {stage.label}")
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print(f" ID: {stage.id}")
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@ -11,9 +11,10 @@ from etl.surveyPrice.surveyPrice import SurveyPrice
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sp = SurveyPrice()
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sp.get_cavity_pricing_table("JJC - EMPTIES")
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hubspot_df = sp.get_all_surveys_from_hubspot()
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sharepoint_df = sp.get_all_surveyed_data_from_sharepoint()
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df = sp.calculate_all_price()
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df = sp.merge_hub_spot_and_survey_information()
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# Surveyoed complete signed off, if normal
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# Make W.C. Folder and upload to commercials in Sharepoint
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# Move deal in hub spot
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@ -4,16 +4,19 @@ from etl.surveyedData.surveryedData import surveyedDataProcessor
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import pandas as pd
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# This script will break if we change our current processses, obviously...
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class SurveyPrice():
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"""
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A class to work out all prices and uploads to sharepoint for review
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"""
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def __init__(self):
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self.warmfront_sharepoint_client = SharePointScraper(SharePointInstaller.WARMFRONT)
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self.sharepoint_client = SharePointScraper(SharePointInstaller.WARMFRONT)
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self.master_rate_card_path = None
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self.all_hubspot_submissions = None
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self.all_survey_info_from_sharepoint = None
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self.download_price_card()
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self.required_sheets = [
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'JJC - EMPTIES',
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'JJC - GENERAL EXTRACTIONS',
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@ -27,18 +30,41 @@ class SurveyPrice():
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'SGEC - FORMALDEHYDE EXTRACTION'
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]
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self.installer = {
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"J & J CRUMP": "JJC",
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"SCIS": "SCIS",
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"SGEC": "SGEC",
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"BAXTER KELLY": "BAXTER KELLY",
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}
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self.domna_job_to_price_sheet_convertor = {
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"JJC - SOLAR": "JJC - SOLAR",
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"JJC - EMPTY CAVITY": "JJC - EMPTIES",
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"JJC - REMIDIAL FOAM FILLED CAVITY": "JJC - FORMALDEHYDE EXTRACTION",
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"JJC - REMIDIAL FILLED CAVITY": "JJC - GENERAL EXTRACTIONS",
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"SCIS - SOLAR": "SCIS - SOLAR",
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"SCIS - EMPTY CAVITY": "SCIS - EMPTIES",
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"SCIS - REMIDIAL FOAM FILLED CAVITY": "SCIS - FORMALDEHYDE EXTRACTION",
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"SCIS - REMIDIAL FILLED CAVITY": "SCIS - GENERAL EXTRACTIONS",
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"SGEC - EMPTY CAVITY": "SGEC - EMPTIES",
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"SGEC - REMIDIAL FOAM FILLED CAVITY": "SGEC - FORMALDEHYDE EXTRACTION",
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"SGEC - REMIDIAL FILLED CAVITY": "SGEC - GENERAL EXTRACTIONS",
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}
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def download_price_card(self):
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url = None
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for files in self.warmfront_sharepoint_client.get_folders_in_path("/Commercials/Rate Cards")['value']:
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# TODO: Some sanity checks to ensure rate cards title stays consistent
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for files in self.sharepoint_client.get_folders_in_path("/Commercials/Rate Cards")['value']:
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if files['name'] == "MASTER RATE CARD.xlsx":
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url = files['@microsoft.graph.downloadUrl']
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break
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if url:
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content = self.warmfront_sharepoint_client.get_file_content(url)
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self.master_rate_card_path = self.warmfront_sharepoint_client.create_temp_file(content, "rate_card/rate_card_all.xlsx")
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content = self.sharepoint_client.get_file_content(url)
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self.master_rate_card_path = self.sharepoint_client.create_temp_file(content, "rate_card/rate_card_all.xlsx")
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return self.master_rate_card_path
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def get_cavity_pricing_table(self, sheet_name):
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excel_file = pd.ExcelFile(self.master_rate_card_path)
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@ -58,31 +84,34 @@ class SurveyPrice():
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def get_price_matrix(self, sheet_name):
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df = pd.read_excel(self.master_rate_card_path, sheet_name)
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columns_to_check = {
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"no extractors or ventilation required": {"Trickle Vent": 0, "Wetrooms": 0},
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"Trickle Vents ONLY": {"Trickle Vent": 1, "Wetrooms": 0},
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"1 wet room extractor required": {"Trickle Vent": 0, "Wetrooms": 1},
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"2 wet room extractor required": {"Trickle Vent": 0, "Wetrooms": 2},
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"3 wet room extractor required": {"Trickle Vent": 0, "Wetrooms": 3},
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'Trickle Vents + 1 wet room extractor': {"Trickle Vent": 1, "Wetrooms": 1},
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'Trickle Vents + 2 wet room extractor': {"Trickle Vent": 1, "Wetrooms": 2},
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'Trickle Vents + 3 wet room extractor': {"Trickle Vent": 1, "Wetrooms": 3},
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}
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pricing_table = []
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for _, row in df.iterrows():
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for key, variables in columns_to_check.items():
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pricing_table.append(
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{
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"WORK TYPE": row["WORK TYPE"],
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"Floor Area Group": row["Total Floor Area"][:-1],
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**variables,
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"PRICE": row[key] if row[key] != "Not viable" else None,
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}
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)
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pricing_table = pd.DataFrame(pricing_table)
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return pricing_table
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if "SOLAR" in sheet_name.upper():
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return df
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else:
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columns_to_check = {
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"no extractors or ventilation required": {"TRICKLE_VENT": 0, "NO_OF_WETROOMS": 0},
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"Trickle Vents ONLY": {"TRICKLE_VENT": 1, "NO_OF_WETROOMS": 0},
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"1 wet room extractor required": {"TRICKLE_VENT": 0, "NO_OF_WETROOMS": 1},
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"2 wet room extractor required": {"TRICKLE_VENT": 0, "NO_OF_WETROOMS": 2},
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"3 wet room extractor required": {"TRICKLE_VENT": 0, "NO_OF_WETROOMS": 3},
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'Trickle Vents + 1 wet room extractor': {"TRICKLE_VENT": 1, "NO_OF_WETROOMS": 1},
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'Trickle Vents + 2 wet room extractor': {"TRICKLE_VENT": 1, "NO_OF_WETROOMS": 2},
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'Trickle Vents + 3 wet room extractor': {"TRICKLE_VENT": 1, "NO_OF_WETROOMS": 3},
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}
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pricing_table = []
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for _, row in df.iterrows():
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for key, variables in columns_to_check.items():
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pricing_table.append(
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{
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"WORK TYPE": row["WORK TYPE"],
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"FLOOR_AREA_BANDING": row["Total Floor Area"][:-1],
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**variables,
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"PRICE": row[key] if row[key] != "Not viable" else None,
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}
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)
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pricing_table = pd.DataFrame(pricing_table)
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return pricing_table
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def get_all_surveys_from_hubspot(self):
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hubSpotClient = HubSpotClient()
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@ -92,18 +121,21 @@ class SurveyPrice():
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for deal in deals:
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all_deals.append({
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"HUBSPOT_DEAL_ID": deal.deal_id,
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"HUBSPOT_WORK_TYPE": deal.work_type,
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"HUBSPOT_DEAL_ADDRESS": deal.deal_name,
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"HUBSPOT_TRICKLE_VENT":1 if deal.needs_trickle_ventilation else 0,
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"HUBSPOT_WALL_INSULATION": deal.existing_wall_insulation,
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"HUBSPOT_POST_INSTALL_SAP_SCORE": deal.post_sap_score,
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"HUBSPOT_INSTALLER": deal.installer
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"HUBSPOT_INSTALLER": deal.installer,
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"HUBSPOT_WETROOMS": deal.no_of_wet_rooms,
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})
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self.all_hubspot_submissions = pd.DataFrame(all_deals)
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return self.all_hubspot_submissions
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def get_all_surveyed_data_from_sharepoint(self):
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# TODO: A wrapper function
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self.all_survey_info_from_sharepoint = self.sharepoint_data_for_jjc()
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return self.all_survey_info_from_sharepoint
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@ -118,6 +150,10 @@ class SurveyPrice():
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all_survey_info = []
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for surveyInfo in jjc_surveys:
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cavity_wall_as_built = False
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csr = False
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foam_insulation = False
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info = {
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"SHAREPOINT INSTALLER": "J & J CRUMP",
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"SHAREPOINT PRE_SITE_NOTES FOUND": True if surveyInfo.pre_site_note else False,
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@ -131,16 +167,47 @@ class SurveyPrice():
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if surveyInfo.pre_site_note:
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floor_banding, total_floor_area = surveyInfo.work_out_total_floor_area()
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pre_sap_score = surveyInfo.get_current_sap_score()
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pre_sap_score_banding = surveyedDataProcessor.get_band(pre_sap_score)
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info.update({
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"SHAREPOINT TOTAL_FLOOR_AREA": total_floor_area,
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"SHAREPOINT FLOOR_AREA_BANDING": floor_banding,
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"SHAREPOINT PRE_INSTALL_SAP_SCORE": surveyInfo.get_current_sap_score(),
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"SHAREPOINT PRE_INSTALL_SAP_SCORE": pre_sap_score,
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"SHAREPOINT PRE_INSTALL_SAP_SCORE_BANDING": pre_sap_score_banding,
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})
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if surveyInfo.pre_site_note.property_description.main_property.wall.insulation.lower() == 'as built' \
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and surveyInfo.pre_site_note.property_description.main_property.wall.construction.lower() == "cavity wall":
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cavity_wall_as_built = True
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if surveyInfo.csr:
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csr = True
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insulation = surveyInfo.get_insulation_info()
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info.update({
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"SHAREPOINT INSULATION MATERIAL": surveyInfo.get_insulation_info(),
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"SHAREPOINT INSULATION MATERIAL": insulation,
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})
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if "FOAM" in insulation.upper():
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foam_insulation = True
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if cavity_wall_as_built:
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if csr:
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if foam_insulation:
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info.update({
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"DOMNA JOB TYPE": "REMIDIAL FOAM FILLED CAVITY",
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})
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else:
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info.update({
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"DOMNA JOB TYPE": "REMIDIAL FILLED CAVITY"
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})
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else:
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info.update({
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"DOMNA JOB TYPE": "EMPTY CAVITY"
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})
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else:
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info.update({
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"DOMNA JOB TYPE": "SOLAR"
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})
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all_survey_info.append(info)
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@ -163,11 +230,18 @@ class SurveyPrice():
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# re-name to installer
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self.all_survey_info_from_sharepoint = self.all_survey_info_from_sharepoint.rename(
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columns={'SHAREPOINT INSTALLER': 'INSTALLER'}
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columns={
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'SHAREPOINT INSTALLER': 'INSTALLER',
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'SHAREPOINT FLOOR_AREA_BANDING': 'FLOOR_AREA_BANDING',
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}
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)
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self.all_hubspot_submissions = self.all_hubspot_submissions.rename(
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columns={'HUBSPOT_INSTALLER': 'INSTALLER'}
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columns={
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'HUBSPOT_INSTALLER': 'INSTALLER',
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'HUBSPOT_WETROOMS': 'NO_OF_WETROOMS',
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'HUBSPOT_TRICKLE_VENT': 'TRICKLE_VENT',
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}
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)
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merged_df = pd.merge(
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@ -180,9 +254,47 @@ class SurveyPrice():
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merged_df.drop(columns=['clean_address'], inplace=True)
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return merged_df
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def compute_energy_grant(row):
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pre_band_letter = row["SHAREPOINT PRE_INSTALL_SAP_SCORE_BANDING"][-1]
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post_band_letter = surveyedDataProcessor.get_band(row["HUBSPOT_POST_INSTALL_SAP_SCORE"])[-1]
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return surveyedDataProcessor.gbis_or_eco4_scheme(pre_band_letter, post_band_letter)
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def compute_banding_for_post_sap(row):
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post_sap_banding = surveyedDataProcessor.get_band(row["HUBSPOT_POST_INSTALL_SAP_SCORE"])
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return post_sap_banding
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def work_type(row):
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if row["ENERGY_GRANT"] == "GBIS":
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return row["ENERGY GRANT"]
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else:
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return f"{row["ENERGY_GRANT"]} - SAP {row["SHAREPOINT PRE_INSTALL_SAP_SCORE_BANDING"]} to {row["POST_INSTALL_SAP_SCORE_BANDING"]}"
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# Add missing variables
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merged_df["ENERGY_GRANT"] = merged_df.apply(compute_energy_grant, axis=1)
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merged_df["POST_INSTALL_SAP_SCORE_BANDING"] = merged_df.apply(compute_banding_for_post_sap, axis=1)
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merged_df["WORK TYPE"] = merged_df.apply(work_type, axis=1)
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return merged_df
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def calculate_all_price(self):
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self.get_all_surveys_from_hubspot()
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self.get_all_surveyed_data_from_sharepoint()
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submission_data = self.merge_hub_spot_and_survey_information()
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final_list = []
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for _, row in submission_data.iterrows():
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if "SOLAR" in row["DOMNA JOB TYPE"].upper():
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raise NotImplementedError("Please implement solar pricing")
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else:
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# Cavity wall
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sheet_name = f"{self.domna_job_to_price_sheet_convertor[f'{self.installer[row["INSTALLER"]]} - {row["DOMNA JOB TYPE"]}'].upper()}"
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price_matrix = self.get_price_matrix(sheet_name)
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merged_row = pd.merge(row.to_frame().T, price_matrix, on=['WORK TYPE', 'TRICKLE_VENT', 'FLOOR_AREA_BANDING', 'NO_OF_WETROOMS'], how='left')
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final_list.append(merged_row)
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return pd.concat(final_list, ignore_index=True)
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@ -191,4 +303,9 @@ class SurveyPrice():
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# Get it working for JJC first, with an idea to make it more diverse
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# Add some TDD to ensure JJC values are correct
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# The script can run weekly, for development I can just get one data
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# Upload both the W.C. calculations and Rate Card to sharepoint
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# Upload both the W.C. calculations and Rate Card to sharepoint
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# Due considerations -> Piece of UI, as osmosis, dashboard upload and pushes site notes database and condition report to database. Link the two, make a due consideration report, due consideration report, propery_id ()
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# Property Tbale
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# Add hubspot deal id into pandas
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# Deem score and move deal id to 'Surveyed Completed Signed off"
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@ -28,6 +28,80 @@ class surveyedDataProcessor():
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insultation = self.csr.insulation_info.type.upper()
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return insultation
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return None
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@staticmethod
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def get_band(sap_score_number):
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bands = [
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("HIGH A", 96, float("inf")),
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("LOW A", 92, 96),
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("HIGH B", 86, 92),
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("LOW B", 81, 86),
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("HIGH C", 74.5, 81),
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("LOW C", 69, 74.5),
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("HIGH D", 61.5, 69),
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("LOW D", 55, 61.5),
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("HIGH E", 46.5, 55),
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("LOW E", 39, 46.5),
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("HIGH F", 29.5, 39),
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("LOW F", 21, 29.5),
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("HIGH G", 10.5, 21),
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("LOW G", 1, 10.5),
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]
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for band, lower, upper in bands:
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if lower <= sap_score_number < upper:
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return band
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return None
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@staticmethod
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def gbis_or_eco4_scheme(presap_letter, postsap_letter):
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"""
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*ECO4 (minimum movement)*
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D to C
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E to C
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F to D
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G to D
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G -> ABCD
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F -> ABCD
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E -> ABC
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D -> ABC
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*GBIS - Cavity Wall Insulation ONLY*
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D-D
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E-D
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E-E
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F-E
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F-F
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G-E
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G-F
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G-G
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"""
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eco4 = {
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"G": ['A', 'B', 'C', 'D'],
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"F": ['A', 'B', 'C', 'D'],
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"E": ['A', 'B', 'C'],
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"D": ['A', 'B', 'C'],
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}
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gbis ={
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'D': ['D'],
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'E': ['E', 'D'],
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'F': ['E', 'F'],
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'G': ['E', 'F', 'G'],
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}
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if presap_letter.upper() in eco4:
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if postsap_letter.upper() in eco4[presap_letter.upper()]:
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return "ECO4"
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if presap_letter.upper() in gbis:
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if postsap_letter.upper() in gbis[presap_letter.upper()]:
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return "GBIS"
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return None
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def work_out_total_floor_area(self):
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@ -66,7 +140,8 @@ class surveyedDataProcessor():
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raise NotImplementedError(f"unknown floor area {floor_area} {self.pre_site_note.summary_information.address}")
|
||||
|
||||
def get_current_sap_score(self):
|
||||
return self.pre_site_note.survey_information.current_sap.split(" ")[1]
|
||||
|
||||
score_list = self.pre_site_note.survey_information.current_sap.split(" ")
|
||||
score = int(score_list[1])
|
||||
return score
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -203,5 +203,11 @@ class PropertyDescription(BaseModel):
|
|||
mainHeating2: Optional[Heating]
|
||||
secondaryHeatingType: Optional[HeatingType]
|
||||
|
||||
# class PropertyReport():
|
||||
# TODO: Property description
|
||||
# TODO: Due consideration foregin key
|
||||
# TODO: Which company did it (Osmosis, Warmfront etc)
|
||||
# TODO: Links to more foreign keys per report etc
|
||||
|
||||
class Insulation(BaseModel):
|
||||
type: str
|
||||
Loading…
Add table
Reference in a new issue