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720 lines
32 KiB
Python
720 lines
32 KiB
Python
import PyPDF2
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import re
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from collections import Counter
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from utils.logger import setup_logger
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from xml.dom.minidom import parseString
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logger = setup_logger()
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"""
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This script contains functions used to extract data from retrofit survey files, including EPRs,
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summary reports, etc
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"""
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def is_elmhurst_energy_report(text):
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"""
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Determines if the provided text indicates that the PDF is an Energy Report.
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Returns True if the text contains 'Energy Report'.
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"""
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return text.startswith("ENERGY REPORT")
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def is_elmhurst_summary_report(text):
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"""
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Determines if the provided text indicates that the PDF is a Summary Report.
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"""
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return text.startswith("Summary Information")
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def is_osmosis_condition_report(text):
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"""
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Determines if the provided text indicates that the PDF is a Condition Report.
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"""
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return text.startswith("OsmosisACDNEWPAS2035ConditionReport") or text.startswith("OsmosisACDPAS2035ConditionReport")
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def is_elmhurst_evidence_report(text):
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"""
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Determines if the provided text indicates that the PDF is an Elmhurst Evidence Report.
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"""
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return text.startswith("RdSAP Evidence Report")
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def detect_pdf_report_type(pdf_path):
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"""
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Detects the type of report based on content or filename.
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:param pdf_path: String path to the PDF file
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:param pdf_file: String name of the PDF file
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:return: String type of the report ("epr", "summary", or None)
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"""
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# Attempt to read the first page of the PDF to determine type
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with open(pdf_path, "rb") as file:
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reader = PyPDF2.PdfReader(file)
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first_page_text = reader.pages[0].extract_text() if reader.pages else ""
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if is_elmhurst_energy_report(first_page_text):
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return "elmhurst epr"
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elif is_elmhurst_summary_report(first_page_text):
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return "elmhurst summary report"
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elif is_osmosis_condition_report(first_page_text):
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return "osmosis condition report"
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elif is_elmhurst_evidence_report(first_page_text):
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return "elmhurst evidence report"
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return None
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def detect_xml_report_type(xml_path):
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"""
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Detects the type of XML report based on content or filename.
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:param xml_path: String path to the XML file
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:return: String type of the report ("full sap xml", or None)
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"""
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# Attempt to read the first page of the PDF to determine type
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with open(xml_path, "r") as file:
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contents = file.read()
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contents = parseString(contents)
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product_tag_search = contents.getElementsByTagName("Product")
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if product_tag_search:
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if product_tag_search[0].firstChild.nodeValue == "Sap 2012 Desktop":
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return "full sap xml"
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raise Exception("Not implemented")
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def is_pdf(filename):
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"""
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Determines if the provided filename is a PDF file.
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"""
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return filename.endswith(".pdf")
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def is_xml(filename):
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"""
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Determines if the provided filename is an XML file.
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"""
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return filename.endswith(".xml")
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class ElmhurstEprExtractor:
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"""
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A utility class for extracting specific data from Elmhurst Energy Performance Reports (EPR).
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"""
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def __init__(self, file_path):
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self.file_path = file_path
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@staticmethod
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def extract_window_age_description(windows_text):
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"""
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Extracts the most common window age description and its proportion.
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"""
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windows_text = windows_text.replace("\n", "")
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window_descriptions = [
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"Double post or during 2002",
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"Double pre 2002",
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"Double with unknown install date",
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"Secondary glazing",
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"Triple glazing",
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"Single glazing",
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]
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description_counts = Counter()
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for description in window_descriptions:
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matches = re.findall(re.escape(description), windows_text)
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description_counts[description] = len(matches)
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if not description_counts or not sum(description_counts.values()):
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raise ValueError("Failed to extract window data.")
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most_common_description, window_count = description_counts.most_common(1)[0]
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window_proportion = window_count / sum(description_counts.values()) * 100
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if window_proportion == 100:
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second_most_common_description = None
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second_most_common_proportion = 0
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else:
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second_most_common_description, second_window_count = description_counts.most_common(2)[1]
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second_most_common_proportion = second_window_count / sum(description_counts.values()) * 100
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return {
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"Window Age Description": most_common_description,
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"Window Age Description Proportion (%)": window_proportion,
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"Secondary Window Age Description": second_most_common_description,
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"Secondary Window Age Description Proportion (%)": second_most_common_proportion,
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"Number of Windows": sum(description_counts.values())
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}
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@staticmethod
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def extract_building_parts(text):
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"""
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Extracts building parts and associated dimensions from the provided text.
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"""
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data = []
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building_part_pattern = re.compile(
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r"Construction details: Building part: (.*?)\nFloor Area \[m2\] Room Height \[m\] Perimeter \[m\] Party "
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r"Wall Length \[m\]\n(.*?)(?=Construction details|Data inputs|$)",
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re.DOTALL
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)
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for match in building_part_pattern.finditer(text):
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part_name = match.group(1).strip()
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floor_data = match.group(2)
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room_in_roof_match = re.search(r"Room\(s\) in Roof area:\s*([\d.]+)", part_name)
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if room_in_roof_match:
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floor_area = float(room_in_roof_match.group(1))
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cleaned_part_name = re.sub(r" - built in.*|Room\(s\) in Roof area:.*", "", part_name).strip()
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data.append({
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"Building Part": cleaned_part_name,
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"Floor Level": "Room in Roof",
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"Floor Area (m2)": floor_area,
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"Room Height (m)": None,
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"Perimeter (m)": None,
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"Party Wall Length (m)": None
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})
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else:
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cleaned_part_name = re.sub(r" - built in.*", "", part_name).strip()
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floor_pattern = re.compile(
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r"(Lowest floor|First floor|Second floor)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)\s+([\d.]+)"
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)
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for floor_match in floor_pattern.finditer(floor_data):
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floor_level = floor_match.group(1)
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floor_area = float(floor_match.group(2))
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room_height = float(floor_match.group(3))
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perimeter = float(floor_match.group(4))
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party_wall_length = float(floor_match.group(5))
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data.append({
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"Building Part": cleaned_part_name,
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"Floor Level": floor_level,
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"Floor Area (m2)": floor_area,
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"Room Height (m)": room_height,
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"Perimeter (m)": perimeter,
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"Party Wall Length (m)": party_wall_length
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})
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return data
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@staticmethod
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def extract_roof_details(text):
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"""
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Extracts roof details for each building part in the provided text.
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"""
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roof_data = []
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building_part_pattern = re.compile(
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r"Construction details: Building part: (.*?)\n(.*?)(?=Conservatory|Construction details|$)",
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re.DOTALL
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)
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for match in building_part_pattern.finditer(text):
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part_name = match.group(1).strip()
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cleaned_part_name = re.sub(r" - built in.*|Room\(s\) in Roof area:.*", "", part_name).strip()
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part_details = match.group(2)
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roof_type_match = re.search(r"Roof Type:\s*(.*?)(?=\n|$)", part_details)
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roof_insulation_match = re.search(r"Roof Insulation:\s*(.*?)(?=\n|$)", part_details)
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roof_insulation_thickness_match = re.search(r"Roof Insulation Thickness:\s*(.*?)(?=\n|$)", part_details)
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roof_data.append({
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"Building Part": cleaned_part_name,
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"Roof Type": roof_type_match.group(1).strip() if roof_type_match else None,
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"Roof Insulation": roof_insulation_match.group(1).strip() if roof_insulation_match else None,
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"Roof Insulation Thickness": roof_insulation_thickness_match.group(
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1).strip() if roof_insulation_thickness_match else None,
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})
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return roof_data
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@staticmethod
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def extract_wall_details(text):
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"""
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Extracts wall details for each building part in the provided text.
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"""
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wall_data = []
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building_part_pattern = re.compile(
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r"Construction details: Building part: (.*?)\n(.*?)(?=Conservatory|Construction details|$)",
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re.DOTALL
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)
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for match in building_part_pattern.finditer(text):
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part_name = match.group(1).strip()
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cleaned_part_name = re.sub(r" - built in.*|Room\(s\) in Roof area:.*", "", part_name).strip()
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part_details = match.group(2)
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wall_type_match = re.search(r"Wall Type:\s*(.*?)(?=\n|$)", part_details)
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wall_insulation_match = re.search(r"Wall Insulation:\s*(.*?)(?=\n|$)", part_details)
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wall_drylining_match = re.search(r"Wall Dry-lining:\s*(.*?)(?=\n|$)", part_details)
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wall_thickness_match = re.search(r"Wall Thickness:\s*(\d+)(?=\n|$)", part_details)
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wall_data.append({
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"Building Part": cleaned_part_name,
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"Wall Type": wall_type_match.group(1).strip() if wall_type_match else None,
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"Wall Insulation": wall_insulation_match.group(1).strip() if wall_insulation_match else None,
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"Wall Dry-lining": wall_drylining_match.group(1).strip() if wall_drylining_match else None,
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"Wall Thickness": int(wall_thickness_match.group(1)) if wall_thickness_match else None,
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})
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return wall_data
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@staticmethod
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def extract_conservatory(text):
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"""
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Extracts conservatory data from the provided text.
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The section is located between "Conservatory" and "Doors".
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Args:
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text (str): The full text of the EPR PDF.
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Returns:
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dict: A dictionary with conservatory details:
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- "Conservatory Present"
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- "Conservatory Separated"
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- "Conservatory Floor Area"
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- "Conservatory Double Glazed"
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- "Conservatory Glazed Perimeter"
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- "Heated Conservatory Height"
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"""
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conservatory_match = re.search(r"Conservatory\s*(.*?)\s*Doors", text, re.DOTALL)
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if not conservatory_match:
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logger.error("Failed to extract conservatory data.")
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raise ValueError("Could not extract conservatory data.")
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conservatory_text = conservatory_match.group(1)
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# Check if conservatory is present
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present_match = re.search(r"Conservatory Present:\s*(Yes|No)", conservatory_text)
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if not present_match or present_match.group(1).strip() == "No":
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logger.info("Conservatory not present.")
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return {
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"Conservatory Present": "No",
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"Conservatory Separated": "",
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"Conservatory Floor Area": 0,
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"Conservatory Double Glazed": "",
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"Conservatory Glazed Perimeter": 0,
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"Heated Conservatory Height": "",
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}
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# Extract conservatory details
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separated_match = re.search(r"Conservatory Separated:\s*(Yes|No)", conservatory_text)
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floor_area_match = re.search(r"Conservatory Floor Area:\s*([\d.]+)", conservatory_text)
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double_glazed_match = re.search(r"Conservatory Double Glazed:\s*(Yes|No)", conservatory_text)
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glazed_perimeter_match = re.search(r"Conservatory Glazed Perimeter:\s*([\d.]+)", conservatory_text)
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height_match = re.search(r"Heated Conservatory Height:\s*(.*?)(?=\n|$)", conservatory_text)
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return {
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"Conservatory Present": "Yes",
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"Conservatory Separated": separated_match.group(1).strip() if separated_match else "",
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"Conservatory Floor Area": float(floor_area_match.group(1)) if floor_area_match else 0,
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"Conservatory Double Glazed": double_glazed_match.group(1).strip() if double_glazed_match else "",
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"Conservatory Glazed Perimeter": float(glazed_perimeter_match.group(1)) if glazed_perimeter_match else 0,
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"Heated Conservatory Height": height_match.group(1).strip() if height_match else "",
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}
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@staticmethod
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def _extract_heating_details(section_text, default_value=""):
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"""
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Extracts heating details from a given section of text.
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Args:
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section_text (str): The section of text containing heating details.
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default_value (str, optional): The default value to return for missing fields. Defaults to "".
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Returns:
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dict: A dictionary containing heating system details.
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"""
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system_search = re.search(r"Main Heating Code\s*(.*?)\n", section_text)
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pcdf_search = re.search(r"PCDF boiler Reference\s*(\d+)", section_text)
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controls_search = re.search(r"Main Heating Controls\s*(.*?)\n", section_text)
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heat_search = re.search(r"Percentage of Heat\s*(\d+)\s*%?", section_text)
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return {
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"System": system_search.group(1).strip() if system_search else default_value,
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"PCDF Reference": pcdf_search.group(1) if pcdf_search else default_value,
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"Controls": controls_search.group(1).strip() if controls_search else default_value,
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"% of Heat": int(heat_search.group(1)) if heat_search else 0,
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}
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def extract_primary_heating(self, text):
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# Extract Primary Heating Section (Main Heating 1)
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primary_heating_section1 = re.search(r"Main\s*Heating\s*1\s*(.*?)\s*Main\s*Heating\s*2", text, re.DOTALL)
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# We may not have a secondary heating
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primary_heating_section2 = re.search(r"Main\s*Heating\s*1\s*(.*?)\s*Secondary\s*Heating", text, re.DOTALL)
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primary_heating_section = primary_heating_section1 if primary_heating_section1 else primary_heating_section2
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primary_text = primary_heating_section.group(1)
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return self._extract_heating_details(primary_text)
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def extract_secondary_heating_details(self, text):
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# Extract Secondary Heating Section (Main Heating 2)
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secondary_heating_section = re.search(r"Main\s*Heating\s*2\s*(.*?)\s*Secondary Heating", text, re.DOTALL)
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output = {}
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if secondary_heating_section is None:
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output["System"] = ""
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output["PCDF Reference"] = ""
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output["Controls"] = ""
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output["% of Heat"] = 0
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else:
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secondary_text = secondary_heating_section.group(1)
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output.update(
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**self._extract_heating_details(secondary_text)
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)
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output["Heating Code"] = (
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re.search(r"Secondary Heating Code\s*(.*?)\n", text).group(1).strip()
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if output["System"] and re.search(r"Secondary Heating Code\s*(.*?)\n", text)
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else ""
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)
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return output
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def extract(self):
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"""
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Extracts all relevant data from the EPR PDF.
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Returns:
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dict: A dictionary containing extracted data, including:
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- Address and Postcode
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- SAP Rating and Primary Energy Use
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- Lighting, Doors, Windows, Roof, and Wall Details
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- Heating systems (Primary and Secondary)
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- Building Parts
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"""
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data = {}
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with open(self.file_path, "rb") as file:
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reader = PyPDF2.PdfReader(file)
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text = "".join(page.extract_text() for page in reader.pages)
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data["Assessor Name"] = re.search(r"Created by:\s*(.*?)\n", text).group(1).strip()
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data["Assessment Date"] = re.search(r"\nAssessment Date\s*(.*?)\n", text).group(1).strip()
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# Extracting individual components
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address_match = re.search(r"ENERGY REPORT\nDwelling Address\s*(.*?)\s*\nReference", text, re.DOTALL)
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if not address_match:
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logger.error("Failed to extract address.")
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raise ValueError("Failed to extract address.")
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data["Address"] = address_match.group(1).strip()
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data["Postcode"] = data["Address"].split(",")[-1].strip()
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sap_match = re.search(r"GG \(1-20\)\s*(\d{1,2})\s*(\d{1,2})", text)
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if not sap_match:
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logger.error("Failed to extract SAP rating.")
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raise ValueError("Failed to extract SAP rating.")
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data["Current SAP Rating"] = int(sap_match.group(1))
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energy_match = re.search(r"Additional ratings for your home\s*([\d.]+)", text)
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if not energy_match:
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logger.error("Failed to extract primary energy use.")
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raise ValueError("Failed to extract primary energy use.")
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data["Primary Energy Use Intensity (kWh/m2/yr)"] = float(energy_match.group(1))
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storeys_match = re.search(r"Number of Storeys:\s*(\d+)", text)
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if not storeys_match:
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logger.error("Failed to extract the number of storeys.")
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raise ValueError("Failed to extract the number of storeys.")
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data["Number of Storeys"] = int(storeys_match.group(1))
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fuel_match = re.search(r"TOTAL\s*£(\d+)", text)
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if not fuel_match:
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logger.error("Failed to extract fuel bill.")
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raise ValueError("Failed to extract fuel bill.")
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data["Fuel Bill"] = f"£{fuel_match.group(1)}"
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total_doors_match = re.search(r"Total Doors:\s*(\d+)", text)
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if not total_doors_match:
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logger.error("Failed to extract total doors.")
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raise ValueError("Failed to extract total doors.")
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data["Total Number of Doors"] = int(total_doors_match.group(1))
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# Extract Number of Insulated Doors
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insulated_doors_match = re.search(r"Insulated Doors:\s*(\d+)", text)
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if not insulated_doors_match:
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logger.error("Failed to extract insulated doors.")
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raise ValueError("Failed to extract insulated doors.")
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data["Number of Insulated Doors"] = int(insulated_doors_match.group(1))
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# Get number of lighting outlets and number of fittings needing LEL
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lighting_fittings_match = re.search(r"Total number of light fittings\s*(\d+)", text)
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if not lighting_fittings_match:
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logger.error("Failed to extract lighting.")
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raise ValueError("Failed to extract lighting")
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data["Number of Light Fittings"] = int(lighting_fittings_match.group(1))
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lel_fittings_match = re.search(r"Total number of L.E.L. fittings\s*(\d+)", text)
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if not lel_fittings_match:
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logger.error("Failed to extract LEL fittings.")
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raise ValueError("Failed to extract LEL fittings.")
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data["Number of LEL Fittings"] = int(lel_fittings_match.group(1))
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data["Number of fittings needing LEL"] = data["Number of Light Fittings"] - data["Number of LEL Fittings"]
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|
windows_section = re.search(r"Windows\s*(.*?)\s*Draught Proofing", text, re.DOTALL)
|
|
if not windows_section:
|
|
logger.error("Failed to extract window data.")
|
|
raise ValueError("Failed to extract window data.")
|
|
data["Windows"] = self.extract_window_age_description(windows_section.group(1))
|
|
|
|
data["Primary Heating"] = self.extract_primary_heating(text)
|
|
data["Secondary Heating"] = self.extract_secondary_heating_details(text)
|
|
data["Building Parts"] = self.extract_building_parts(text)
|
|
data["Roof Details"] = self.extract_roof_details(text)
|
|
data["Wall Details"] = self.extract_wall_details(text)
|
|
data["Conservatory"] = self.extract_conservatory(text)
|
|
|
|
water_heating_code_match = re.search(r"Water Heating Code\s*(.*?)\n", text)
|
|
if not water_heating_code_match:
|
|
logger.error("Failed to extract water heating code.")
|
|
raise ValueError("Failed to extract water heating code.")
|
|
data["Water Heating Code"] = water_heating_code_match.group(1).strip()
|
|
|
|
return data
|
|
|
|
|
|
class ElmhurstSummaryReportExtractor:
|
|
"""
|
|
A utility class for extracting specific data from Elmhurst Energy Performance Reports (EPR).
|
|
"""
|
|
|
|
def __init__(self, file_path):
|
|
self.file_path = file_path
|
|
|
|
@staticmethod
|
|
def extract_window_age_description(windows_text):
|
|
"""
|
|
Extracts the most common window age description and its proportion.
|
|
|
|
Parameters:
|
|
windows_text (str): The text section containing window data.
|
|
|
|
Returns:
|
|
dict: A dictionary with the most common window age description and its proportion.
|
|
"""
|
|
# Clean up windows_text by removing line breaks for better pattern matching
|
|
windows_text = windows_text.replace("\n", "")
|
|
|
|
# Define possible window age descriptions
|
|
window_descriptions = [
|
|
"Double post or during 2002",
|
|
"Double pre 2002",
|
|
"Double with unknown install date",
|
|
"Secondary glazing",
|
|
"Triple glazing",
|
|
"Single glazing",
|
|
]
|
|
|
|
# Count occurrences of each description
|
|
description_counts = Counter()
|
|
for description in window_descriptions:
|
|
matches = re.findall(re.escape(description), windows_text)
|
|
description_counts[description] = len(matches)
|
|
|
|
if not description_counts or not sum(description_counts.values()):
|
|
raise ValueError("Failed to extract window data.")
|
|
|
|
# Determine the most common description and calculate its proportion
|
|
most_common_description, window_count = description_counts.most_common(1)[0]
|
|
window_proportion = window_count / sum(description_counts.values()) * 100
|
|
|
|
# Get the second most common and the proportion
|
|
if window_proportion == 100:
|
|
second_most_common_description = None
|
|
second_most_common_proportion = 0
|
|
else:
|
|
second_most_common_description, second_window_count = description_counts.most_common(2)[1]
|
|
second_most_common_proportion = second_window_count / sum(description_counts.values()) * 100
|
|
|
|
return {
|
|
"Window Age Description": most_common_description,
|
|
"Window Age Description Proportion (%)": window_proportion,
|
|
"Secondary Window Age Description": second_most_common_description,
|
|
"Secondary Window Age Description Proportion (%)": second_most_common_proportion,
|
|
"Number of Windows": sum(description_counts.values())
|
|
}
|
|
|
|
@staticmethod
|
|
def extract_primary_heating(text):
|
|
primary_heating_section1 = re.search(r"Main\s*Heating1\s*(.*?)\s*Main\s*Heating2", text, re.DOTALL)
|
|
primary_heating_section2 = re.search(r"Main\s*Heating1\s*(.*?)\s*Water\s*Heating", text, re.DOTALL)
|
|
primary_heating_section = primary_heating_section1 if primary_heating_section1 else primary_heating_section2
|
|
if primary_heating_section is None:
|
|
raise ValueError("Failed to extract primary heating data.")
|
|
|
|
primary_text = primary_heating_section.group(1)
|
|
|
|
output = {
|
|
'System': re.search(r"Main Heating Code\s*(.*?)\n", primary_text).group(1).strip(),
|
|
'PCDF Reference': re.search(r"PCDF boiler Reference\s*(\d+)", primary_text).group(1),
|
|
'Controls': re.search(r"Main Heating Controls\s*(.*?)\n", primary_text).group(1).strip(),
|
|
'% of Heat': int(re.search(r"Percentage of Heat\s*(\d+)\s*%", primary_text).group(1))
|
|
}
|
|
return output
|
|
|
|
@staticmethod
|
|
def extract_secondary_heating_details(text):
|
|
secondary_heating_section = re.search(r"Main\s*Heating2\s*(.*?)\s*Water\s*Heating", text, re.DOTALL)
|
|
|
|
# Defaults
|
|
output = {
|
|
"System": "",
|
|
"PCDF Reference": "",
|
|
"Controls": "",
|
|
"% of Heat": 0,
|
|
"Heating Code": ""
|
|
}
|
|
if secondary_heating_section is not None:
|
|
# Overwrite defaults
|
|
secondary_text = secondary_heating_section.group(1)
|
|
|
|
main_heating_code_match_secondary = re.search(
|
|
r"Main Heating Code\s*(.*?)(?=\n|Percentage of Heat)", secondary_text
|
|
)
|
|
output["System"] = main_heating_code_match_secondary.group(1).strip()
|
|
output["PCDF Reference"] = re.search(r"PCDF boiler Reference\s*(\d+)", secondary_text).group(1)
|
|
|
|
second_heating_controls_match = re.search(r"Main Heating Controls\s*(.*?)\n", secondary_text)
|
|
output["Heating Controls"] = (
|
|
second_heating_controls_match.group(1).strip() if second_heating_controls_match else ""
|
|
)
|
|
output["% of Heat"] = int(
|
|
re.search(r"Percentage of Heat\s*(\d+)\s*%", secondary_text).group(1)
|
|
)
|
|
|
|
secondary_heating_code_match = re.search(r"Secondary Heating Code\s*(.*?)\n", text)
|
|
if output["System"] != "":
|
|
output["Heating Code"] = (
|
|
secondary_heating_code_match.group(1).strip() if secondary_heating_code_match else ""
|
|
)
|
|
|
|
return output
|
|
|
|
def extract(self):
|
|
"""
|
|
Extracts specific data from the provided PDF file.
|
|
Data includes:
|
|
- Current SAP rating
|
|
- Fuel Bill
|
|
- Address
|
|
"""
|
|
|
|
# Expected keys:
|
|
# dict_keys([
|
|
# 'Primary Heating', 'Secondary Heating', 'Building Parts', 'Roof Details', 'Wall Details', 'Conservatory',
|
|
# 'Water Heating Code'])
|
|
|
|
data = {}
|
|
|
|
with (open(self.file_path, "rb") as file):
|
|
reader = PyPDF2.PdfReader(file)
|
|
text = ""
|
|
for page in reader.pages:
|
|
text += page.extract_text()
|
|
|
|
# Match and extract
|
|
name_match = re.search(r"Name:\s*([A-Za-z\s]+)\s*Title:\s*([A-Za-z\.]+)", text)
|
|
if not name_match:
|
|
raise ValueError("Couldn't extract surveyor name")
|
|
data["Assessor Name"] = name_match.group(2).strip() + " " + name_match.group(1).strip()
|
|
data["Assessment Date"] = re.search(r"Inspection Date:\s*(.*?)\n", text).group(1).strip()
|
|
|
|
# Address and postcode
|
|
postcode = re.search(r"Postcode:\s*(.*?)\nRegion:", text)
|
|
region = re.search(r"Region:\s*(.*?)\nHouse Name:", text)
|
|
house_name = re.search(r"House Name:\s*(.*?)\nHouse No:", text)
|
|
house_no = re.search(r"House No:\s*(.*?)\nStreet:", text)
|
|
street = re.search(r"Street:\s*(.*?)\nLocality:", text)
|
|
locality = re.search(r"Locality:\s*(.*?)\nTown:", text)
|
|
town = re.search(r"Town:\s*(.*?)\nCounty:", text)
|
|
county = re.search(r"County:\s*(.*?)\nProperty Tenure:", text)
|
|
|
|
# Clean extracted values and remove any prefixes
|
|
address_parts = [
|
|
house_no.group(1).strip() if house_no else "",
|
|
house_name.group(1).strip() if house_name else "",
|
|
street.group(1).strip() if street else "",
|
|
locality.group(1).strip() if locality else "",
|
|
town.group(1).strip() if town else "",
|
|
county.group(1).strip() if county else "",
|
|
region.group(1).strip() if region else "",
|
|
postcode.group(1).strip() if postcode else ""
|
|
]
|
|
|
|
# Join non-empty parts with a comma
|
|
data["Address"] = ", ".join([part for part in address_parts if part])
|
|
data["Postcode"] = postcode.group(1).strip()
|
|
|
|
# Extract Current SAP rating
|
|
sap_match = re.search(r"Current SAP rating:\s*([A-Z] \d+)", text)
|
|
if not sap_match:
|
|
raise ValueError("Could not extract SAP rating")
|
|
data["Current SAP Rating"] = sap_match.group(1).split(" ")[1]
|
|
|
|
# We don't have primary energy in the summary report
|
|
data['Primary Energy Use Intensity (kWh/m2/yr)'] = None
|
|
|
|
# Number of storeys
|
|
storeys_match = re.search(r"Number of Storeys:\s*(\d+)", text)
|
|
if not storeys_match:
|
|
raise ValueError("Could not extract number of storeys")
|
|
data["Number of Storeys"] = int(storeys_match.group(1))
|
|
|
|
# Extract Fuel Bill
|
|
fuel_bill_match = re.search(r"Fuel Bill:\s*£(\d+)", text)
|
|
if not fuel_bill_match:
|
|
raise ValueError("Could not extract fuel bill")
|
|
data["Fuel Bill"] = f"£{fuel_bill_match.group(1)}"
|
|
|
|
# Extract Total Number of Doors
|
|
total_doors_match = re.search(r"Total Number of Doors\s*(\d+)", text)
|
|
if not total_doors_match:
|
|
raise ValueError("Could not extract total number of doors")
|
|
data["Total Number of Doors"] = int(total_doors_match.group(1))
|
|
|
|
# Extract Number of Insulated Doors
|
|
insulated_doors_match = re.search(r"Number of Insulated Doors\s*(\d+)", text)
|
|
if not insulated_doors_match:
|
|
raise ValueError("Could not extract number of insulated doors")
|
|
data["Number of Insulated Doors"] = int(insulated_doors_match.group(1))
|
|
|
|
# lighting
|
|
data["Number of Light Fittings"] = int(re.search(r"Total number of light fittings\s*(\d+)", text).group(1))
|
|
data["Number of LEL Fittings"] = int(re.search(r"Total number of L.E.L. fittings\s*(\d+)", text).group(1))
|
|
data["Number of fittings needing LEL"] = data["Number of Light Fittings"] - data["Number of LEL Fittings"]
|
|
|
|
windows_section = re.search(r"Windows\s*(.*?)\s*Draught Proofing", text, re.DOTALL)
|
|
if not windows_section:
|
|
raise ValueError("Failed to extract window data.")
|
|
data["Windows"] = self.extract_window_age_description(windows_section.group(1))
|
|
|
|
data["Primary Heating"] = self.extract_primary_heating(text)
|
|
data["Secondary Heating"] = self.extract_secondary_heating_details(text)
|
|
|
|
# Extract Secondary Heating Section
|
|
|
|
# Extract Secondary Heating and Water Heating Codes
|
|
|
|
water_heating_code_match = re.search(r"Water Heating Code\s*(.*?)\n", text)
|
|
|
|
data["Water Heating Code"] = water_heating_code_match.group(1).strip()
|
|
|
|
dimensions = extract_building_parts_summary(text)
|
|
data.update(dimensions)
|
|
|
|
extracted_roof_data = extract_roof_details_summary(text)
|
|
main_roof_data = [roof for roof in extracted_roof_data if "Main" in roof["Building Part"]][0]
|
|
data["Main Roof Type"] = main_roof_data["Roof Type"]
|
|
data["Main Roof Insulation"] = main_roof_data["Roof Insulation"]
|
|
data["Main Roof Insulation Thickness"] = main_roof_data["Roof Insulation Thickness"]
|
|
|
|
walls_data = extract_wall_details_summary(text)
|
|
# Get the main building wall data
|
|
main_building_walls = [wall for wall in walls_data if "Main" in wall["Building Part"]][0]
|
|
data["Main Wall Type"] = main_building_walls["Wall Type"]
|
|
data["Main Wall Insulation"] = main_building_walls["Wall Insulation"]
|
|
data["Main Wall Dry-lining"] = main_building_walls["Wall Dry-lining"]
|
|
data["Main Wall Thickness"] = main_building_walls["Wall Thickness (mm)"]
|
|
data["Main Building Alternative Wall Type"] = main_building_walls["Alternative Wall Type"]
|
|
data["Main Building Alternative Wall Insulation"] = main_building_walls["Alternative Wall Insulation"]
|
|
data["Main Building Alternative Wall Dry-lining"] = main_building_walls["Alternative Wall Dry-lining"]
|
|
data["Main Building Alternative Wall Thickness"] = main_building_walls["Alternative Wall Thickness (mm)"]
|
|
|
|
return data
|