mirror of
https://github.com/Hestia-Homes/Model.git
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quidos site notes extraction
This commit is contained in:
Khalim Conn-Kowlessar
parent
764dc7901f
commit
0de14c4e28
4 changed files with 243 additions and 38 deletions
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@ -1,5 +1,4 @@
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import numpy as np
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from scipy.constants import value
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class PropertyValuation:
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@ -216,6 +215,30 @@ class PropertyValuation:
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cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
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)
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current_epc = property_instance.data["current-energy-rating"]
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if not current_value:
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return {
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"current_value": 0,
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"lower_bound_increased_value": 0,
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"upper_bound_increased_value": 0,
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"average_increased_value": 0,
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"average_increase": 0
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}
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return cls.estimate_valuation_improvement(current_value, current_epc, target_epc, total_cost)
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@classmethod
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def estimate_valuation_improvement(cls, current_value, current_epc, target_epc, total_cost=None):
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"""
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This function estimates the value of a property based on the current EPC rating and the target EPC rating
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:param current_value:
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:param current_epc:
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:param target_epc:
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:param total_cost:
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:return:
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"""
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if not current_value:
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return {
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"current_value": 0,
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@ -225,7 +248,6 @@ class PropertyValuation:
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"average_increase": 0
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}
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current_epc = property_instance.data["current-energy-rating"]
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# We get the spectrum of ratings between the current and target EPC
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epc_band_range = cls.EPC_BANDS[cls.EPC_BANDS.index(current_epc): cls.EPC_BANDS.index(target_epc) + 1]
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@ -24,21 +24,24 @@ load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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def get_data(asset_list, fulladdress_column, address1_column, postcode_column, manual_uprn_map, epc_api_only=False):
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def get_data(
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asset_list, fulladdress_column, address1_column, postcode_column, manual_uprn_map, uprn_column=None,
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epc_api_only=False
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):
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epc_data = []
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errors = []
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no_epc = []
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for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
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try:
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postcode = home[postcode_column]
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house_number = home[address1_column].strip()
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house_number = str(home[address1_column]).strip()
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full_address = home[fulladdress_column].strip()
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house_no = SearchEpc.get_house_number(address=str(house_number), postcode=postcode)
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if house_no is None:
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house_no = house_number
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uprn = manual_uprn_map.get(full_address, None)
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if uprn is None and home.get("uprn"):
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uprn = home["uprn"]
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if uprn is None and home.get(uprn_column):
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uprn = home[uprn_column]
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if pd.isnull(uprn):
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uprn = None
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@ -149,7 +152,7 @@ def get_data(asset_list, fulladdress_column, address1_column, postcode_column, m
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return epc_data, errors, no_epc
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def extract_address1(asset_list, full_address_col, method="first_two_words"):
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def extract_address1(asset_list, full_address_col, postcode_col, method="first_two_words"):
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if method == "first_two_words":
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asset_list["address1_extracted"] = asset_list[full_address_col].str.split(" ").str[:2].str.join(" ")
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return asset_list
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@ -158,6 +161,13 @@ def extract_address1(asset_list, full_address_col, method="first_two_words"):
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asset_list["address1_extracted"] = asset_list[full_address_col].str.split(" ").str[0]
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return asset_list
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if method == "house_number_extraction":
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asset_list["address1_extracted"] = asset_list.apply(
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lambda x: SearchEpc.get_house_number(address=x[full_address_col], postcode=x[postcode_col]),
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axis=1
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)
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return asset_list
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raise ValueError(f"Method {method} not recognized")
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@ -258,16 +268,29 @@ def app():
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# - We want: fully insulated property (all wall types), EPC D or below (floors should be solid)
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# - Or the insulation required is loft/cavity (floors should be solid)
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DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Community Housing"
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DATA_FILENAME = "Community Housing PV data pull.xlsx"
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SHEET_NAME = "Community Housing"
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POSTCODE_COLUMN = "Postcode"
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# For Westward
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# DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Westward"
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# DATA_FILENAME = "WESTWARD - completed list..xlsx"
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# SHEET_NAME = "Sheet1"
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# POSTCODE_COLUMN = "WFT EDIT Postcode"
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# FULLADDRESS_COLUMN = "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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# MISSING_POSTCODES_METHOD = None
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# PROPERTY_YEAR_BUILT = "Build date"
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# UPRN_COLUMN = "UPRN"
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DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Colchester"
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DATA_FILENAME = "Warmfront data- Colchester Borough Homes (Complete).xlsx"
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SHEET_NAME = "Sheet1"
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POSTCODE_COLUMN = 'Full Address.1'
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FULLADDRESS_COLUMN = "Full Address"
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ADDRESS1_COLUMN = None
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ADDRESS1_METHOD = "first_word"
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ADDRESS_COLS_TO_CONCAT = []
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MISSING_POSTCODES_METHOD = None
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PROPERTY_YEAR_BUILT = "Build_Date"
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PROPERTY_YEAR_BUILT = "Build Date"
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UPRN_COLUMN = None
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# Maps addresses to uprn in problematic cases
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MANUAL_UPRN_MAP = {}
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@ -299,7 +322,10 @@ def app():
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if ADDRESS1_COLUMN is None:
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ADDRESS1_COLUMN = "address1_extracted"
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asset_list = extract_address1(
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asset_list=asset_list, full_address_col=FULLADDRESS_COLUMN, method=ADDRESS1_METHOD
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asset_list=asset_list,
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full_address_col=FULLADDRESS_COLUMN,
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postcode_col=POSTCODE_COLUMN,
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method=ADDRESS1_METHOD
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)
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if FULLADDRESS_COLUMN is None:
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@ -315,6 +341,23 @@ def app():
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asset_list[FULLADDRESS_COLUMN] = asset_list[FULLADDRESS_COLUMN].str.replace('\xa0', ' ', regex=False)
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asset_list[FULLADDRESS_COLUMN] = asset_list[FULLADDRESS_COLUMN].str.replace(' ', ' ', regex=False)
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if UPRN_COLUMN is not None:
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# Check if it's numeric and if so, make sure it's an integer
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def convert_uprn(x):
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if pd.isnull(x):
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return x
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# check if numeric
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if np.isreal(x):
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return str(int(x))
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if str(x).isdigit():
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return str(int(x))
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return x
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asset_list[UPRN_COLUMN] = asset_list[UPRN_COLUMN].apply(convert_uprn)
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# We check for duplicated addresses
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asset_list["deduper"] = asset_list[FULLADDRESS_COLUMN] + asset_list[POSTCODE_COLUMN]
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if asset_list["deduper"].duplicated().sum():
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@ -342,7 +385,8 @@ def app():
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fulladdress_column=FULLADDRESS_COLUMN,
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address1_column=ADDRESS1_COLUMN,
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postcode_column=POSTCODE_COLUMN,
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manual_uprn_map=MANUAL_UPRN_MAP
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manual_uprn_map=MANUAL_UPRN_MAP,
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uprn_column=UPRN_COLUMN
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)
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# We now retrieve any failed properties
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@ -535,6 +579,7 @@ def app():
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# 3) If we have year in the asset list, we flag entries where the built year is different from the
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# EPC Age band
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if PROPERTY_YEAR_BUILT is not None:
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raise Exception("THIS WAS WRONG!")
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asset_list["Does Age Match EPC Age Band?"] = asset_list.apply(
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lambda x: process_age_band(x, PROPERTY_YEAR_BUILT), axis=1
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)
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@ -32,6 +32,15 @@ def generate_html_report(template_path, output_path, data):
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print(f"HTML report generated successfully: {output_path}")
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def stringify_number(num: int, rounding: bool = True) -> str:
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if num < 100000: # 5 figures or fewer
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rounded_num = ((num + 99) // 100) * 100 if rounding else num
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return f"{rounded_num:,}"
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else: # More than 5 figures
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rounded_num = ((num + 999) // 1000) * 1000 if rounding else num
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return f"{rounded_num // 1000}k"
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class PlacidApi:
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# Errors as defined by docs: https://placid.app/docs/2.0/rest/errors
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ERROR_CODES = {
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@ -89,7 +98,8 @@ class PlacidApi:
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)
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response_body = response.json()
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pdf_id = response_body["id"]
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return response_body
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def get_pdf(self, pdf_id: str):
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"""
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@ -106,20 +116,22 @@ class PlacidApi:
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url = response_body["pdf_url"]
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# Download the PDF form this uurl
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pdf_download = requests.get(url)
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with open("output.pdf", "wb") as f:
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with open("survey_report/example_data/output.pdf", "wb") as f:
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f.write(pdf_download.content)
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def handle():
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def handler():
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"""
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Performs the data extraction process for the survey report
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:return:
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"""
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PLACID_API_KEY = "placid-mpkwidzer2mens9h-hifa3dmbxpfeghpa"
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TEMPLATE_UUID = "hnwqgtumckfbf"
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TEMPLATE_UUID = "5bst9mh1q9lk9"
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placid_api = PlacidApi(PLACID_API_KEY)
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current_property_value = 250000 # Needs to be an input
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EPC_COLOURS = {
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"A": "#117d58",
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"B": "#2da55c",
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@ -136,26 +148,27 @@ def handle():
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"WILLIS ROAD FLAT 1 PRE EPR SITE NOTES.pdf",
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"epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 1/3 WILLIS "
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"ROAD FLAT 1 PRE EPR PDF.pdf",
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"scenario_epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 1/3 "
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"WILLIS ROAD FLAT 1 POST EPR PDF.pdf"
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"scenario_site_notes": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data"
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"/Flat 1/3 WILLIS ROAD FLAT 1 POST EPR SITE NOTES.pdf"
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},
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{
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"site_notes": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 2/3 "
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"WILLIS ROAD FLAT 2 PRE EPR SITE NOTES.pdf",
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"epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 2/3 WILLIS "
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"ROAD FLAT 2 PRE EPR PDF.pdf",
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"scenario_epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 2/3 "
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"WILLIS ROAD FLAT 2 POST EPR PDF.pdf"
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"scenario_site_notes": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data"
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"/Flat 2/3 WILLIS ROAD FLAT 2 POST EPR SITE NOTES.pdf"
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},
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{
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"site_notes": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 3/3 "
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"WILLIS ROAD FLAT 3 PRE EPR SITE NOTES.pdf",
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"epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 3/3 WILLIS "
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"ROAD FLAT 3 PRE EPR PDF.pdf",
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"scenario_epr": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data/Flat 3/3 "
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"WILLIS ROAD FLAT 3 POST EPR PDF.pdf"
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"scenario_site_notes": "/Users/khalimconn-kowlessar/Documents/hestia/Model/survey_report/example_data"
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"/Flat 3/3 WILLIS ROAD FLAT 3 POST EPR SITE NOTES.pdf"
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},
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]
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data = []
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for data_config in folders:
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@ -181,26 +194,61 @@ def handle():
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epr_extractor = EPRExtractor(file_mapping["epr"])
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epr = epr_extractor.extract_all()
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scenario_epr = EPRExtractor(file_mapping["scenario_epr"])
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scenario_epr = scenario_epr.extract_all()
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# Valuation simulation
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scenario_site_notes_extractor = SiteNotesExtractor(file_mapping["scenario_site_notes"])
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scenario_site_notes = scenario_site_notes_extractor.extract_all()
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from backend.ml_models.Valuation import PropertyValuation
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valuation_uplift = PropertyValuation.estimate_valuation_improvement(
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current_value=current_property_value,
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current_epc=site_notes["Current EPC Band"],
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target_epc=scenario_site_notes["Current EPC Band"],
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)
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# TODO - should convert this, when it's more than 5 figures and we should certainly stringify this
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valuation_difference = round(valuation_uplift["average_increased_value"] - current_property_value)
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# Prepare the data for output
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bill_savings = round(
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site_notes['Estimated Annual Energy Cost (£)'] - scenario_site_notes['Estimated Annual Energy Cost (£)']
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)
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carbon_savings = round(
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site_notes["Current Carbon Emissions (TCO2)"] - scenario_site_notes["Current Carbon Emissions (TCO2)"],
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2
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)
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payback_period = None
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if payback_period is None:
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raise NotImplementedError("Implement me")
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# We extract the measures from the site notes
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report_data = {
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"template_uuid": TEMPLATE_UUID,
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"current_epc_rating": site_notes["Current EPC Band"],
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"current_epc_rating_colour": EPC_COLOURS[site_notes["Current EPC Band"]],
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post_retrofit_epc_rating: str,
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post_retrofit_epc_rating_colour: str,
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"post_retrofit_epc_rating": scenario_site_notes["Current EPC Band"],
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"post_retrofit_epc_rating_colour": EPC_COLOURS[scenario_site_notes["Current EPC Band"]],
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"bill_savings": stringify_number(bill_savings),
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"valuation_improvement": stringify_number(valuation_difference),
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"carbon_savings": carbon_savings,
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}
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# We now produce the combined data sheet which is the starting figure:
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data_sheet = {**epr, **site_notes}
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del data_sheet['Building Dimensions']
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# We unnest the Total Building Dimensions
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data_sheet["Total Building Floor Area (m2)"] = data_sheet["Total Building Dimensions"]["floor_area"]
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data_sheet["Total Building Heat Loss Area (m2)"] = data_sheet["Total Building Dimensions"]["heat_loss_area"]
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del data_sheet["Total Building Dimensions"]
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# data_sheet = {**epr, **site_notes}
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# del data_sheet['Building Dimensions']
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# # We unnest the Total Building Dimensions
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# data_sheet["Total Building Floor Area (m2)"] = data_sheet["Total Building Dimensions"]["floor_area"]
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# data_sheet["Total Building Heat Loss Area (m2)"] = data_sheet["Total Building Dimensions"]["heat_loss_area"]
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# del data_sheet["Total Building Dimensions"]
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data.append(data_sheet)
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create_pdf_response = placid_api.create_pdf(
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template_uuid=TEMPLATE_UUID, **report_data
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)
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# {'id': 769832, 'type': 'pdf', 'status': 'queued', 'pdf_url': None, 'transfer_url': None, 'passthrough': None}
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# Download locally
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placid_api.get_pdf(create_pdf_response["id"])
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data = pd.DataFrame(data)
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@ -108,8 +108,98 @@ class SiteNotesExtractor:
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self.extract_carbon_emissions()
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self.extract_bills_estimate()
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self.extract_building_dimensions()
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# Extract specific measures
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# Primary wall
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# Secondary wall
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# Roof
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# Floor
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# Heating system
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# Hot water system
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# Windows
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# Doors
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# Lighting
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# Ventilation
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# Solar
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return self.data
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def extract_walls(self):
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"""
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Extracts wall type, insulation, dry-lining, and thickness for each building part,
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including any alternative wall details within the 7.0 Walls section of the summary PDF text.
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"""
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text = self.text
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wall_data = []
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# Isolate the 7.0 Walls section
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wall_section_match = re.search(r"7\.0 Walls\n(.*?)\n8\.0 Roofs", text, re.DOTALL)
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if not wall_section_match:
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raise ValueError("Failed to locate the walls section in the text.")
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wall_section = wall_section_match.group(1)
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# Define patterns to match walls for each building part
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wall_pattern = re.compile(
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r"(?P<section>Main Property(?: Alternative)?|Extension \d+)\s*\n"
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r"(?:Construction\s*(?P<construction>[^\n]*)\n)?"
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r"(?:Insulation\s*(?P<insulation>[^\n]*)\n)?"
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r"(?:Insulation Thickness\(mm\)\s*(?P<insulation_thickness>[^\n]*)\n)?"
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r"(?:Wall Thickness Measured\?\s*(?P<thickness_measured>[^\n]*)\n)?"
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r"(?:Wall Thickness\(mm\)\s*(?P<thickness>\d+))?",
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re.MULTILINE
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)
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# TODO: We aren't effectively picking up alternative walls
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# alt_wall_pattern = re.compile(
|
||||
# r"Alternative Wall Sheltered\s*.*?\n"
|
||||
# r".*?Construction\s*(?P<alt_construction>[^\n]*)\n"
|
||||
# r"Insulation\s*(?P<alt_insulation>[^\n]*)\n"
|
||||
# r"Insulation Thickness\(mm\)\s*(?P<alt_insulation_thickness>[^\n]*)\n"
|
||||
# r"Wall Thickness Measured\?\s*(?P<alt_thickness_measured>[^\n]*)\n"
|
||||
# r"Wall Thickness\(mm\)\s*(?P<alt_thickness>\d+)?",
|
||||
# re.MULTILINE
|
||||
# )
|
||||
|
||||
for match in wall_pattern.finditer(wall_section):
|
||||
building_part = match.group("section")
|
||||
# has_alternative_wall = "Alternative" in building_part
|
||||
building_part = "Main Property" if "Main Property" in building_part else building_part
|
||||
|
||||
wall_entry = {
|
||||
"Building Part": building_part,
|
||||
"Wall Type": match.group("construction") or "Unknown",
|
||||
"Wall Insulation": match.group("insulation") or "Unknown",
|
||||
"Insulation Thickness (mm)": match.group("insulation_thickness") or "Unknown",
|
||||
"Wall Thickness Measured": match.group("thickness_measured") or "Unknown",
|
||||
"Wall Thickness (mm)": int(match.group("thickness")) if match.group("thickness") and match.group(
|
||||
"thickness").isdigit() else None,
|
||||
"Alternative Wall Type": None,
|
||||
"Alternative Wall Insulation": None,
|
||||
"Alternative Insulation Thickness (mm)": None,
|
||||
"Alternative Wall Thickness Measured": None,
|
||||
"Alternative Wall Thickness (mm)": None,
|
||||
}
|
||||
|
||||
# Check if an alternative wall section exists
|
||||
# if has_alternative_wall:
|
||||
# alt_match = alt_wall_pattern.search(wall_section, match.end())
|
||||
# if alt_match:
|
||||
# wall_entry["Alternative Wall Type"] = alt_match.group("alt_construction") or "Unknown"
|
||||
# wall_entry["Alternative Wall Insulation"] = alt_match.group("alt_insulation") or "Unknown"
|
||||
# wall_entry["Alternative Insulation Thickness (mm)"] = alt_match.group(
|
||||
# "alt_insulation_thickness") or "Unknown"
|
||||
# wall_entry["Alternative Wall Thickness Measured"] = alt_match.group(
|
||||
# "alt_thickness_measured") or "Unknown"
|
||||
# wall_entry["Alternative Wall Thickness (mm)"] = int(
|
||||
# alt_match.group("alt_thickness")) if alt_match.group("alt_thickness") and alt_match.group(
|
||||
# "alt_thickness").isdigit() else None
|
||||
|
||||
wall_data.append(wall_entry)
|
||||
|
||||
return wall_data
|
||||
|
||||
|
||||
class EPRExtractor:
|
||||
"""
|
||||
|
|
@ -123,7 +213,7 @@ class EPRExtractor:
|
|||
self.text = pdf_text
|
||||
self.data = {}
|
||||
|
||||
def extract_heating_data(self):
|
||||
def extract_heating_consumption(self):
|
||||
"""
|
||||
Extracts space heating and water heating values from the report.
|
||||
"""
|
||||
|
|
@ -162,5 +252,5 @@ class EPRExtractor:
|
|||
Runs all extraction methods and returns a dictionary with extracted data.
|
||||
"""
|
||||
self.extract_address()
|
||||
self.extract_heating_data()
|
||||
self.extract_heating_consumption()
|
||||
return self.data
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue