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cleaning roof extraction
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Khalim Conn-Kowlessar
parent
294506853d
commit
377d9929e4
1 changed files with 72 additions and 28 deletions
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@ -291,26 +291,11 @@ def extract_summary_report(pdf_path):
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data["Number of LEL Fittings"] = int(re.search(r"Total number of L.E.L. fittings\s*(\d+)", text).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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roof_section = re.search(r"8\.0 Roofs:\n(.*?)\n9\.0 Floors:", text, re.DOTALL)
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roof_text = roof_section.group(1).strip()
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roof_type_match = re.search(r"Type\s*([A-Za-z0-9\s]+)", roof_text)
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data["Main Roof Type"] = roof_type_match.group(1).strip() if roof_type_match else None
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# Check if "Insulation" exists between Type and Insulation Thickness
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insulation_search = re.search(
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r"Type\s+.*?\n(Insulation\s+(.*?)\n)?(Insulation Thickness\s+(.*?)\n)", roof_text, re.DOTALL
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)
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if insulation_search:
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# Insulation match will be present if it exists, otherwise it will be None
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insulation_match = insulation_search.group(2) # Optional group for Insulation
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insulation_thickness_match = insulation_search.group(4) # Required group for Insulation Thickness
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# Populate insulation fields
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data["Main Roof Insulation"] = insulation_match.strip() if insulation_match else None
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data["Main Roof Insulation Thickness"] = (
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insulation_thickness_match.strip() if insulation_thickness_match else None
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)
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extracted_roof_data = extract_roof_details_summary(text)
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main_roof_data = [roof for roof in extracted_roof_data if "Main" in roof["Building Part"]][0]
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data["Main Roof Type"] = main_roof_data["Roof Type"]
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data["Main Roof Insulation"] = main_roof_data["Roof Insulation"]
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data["Main Roof Insulation Thickness"] = main_roof_data["Roof Insulation Thickness"]
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walls_data = extract_wall_details_summary(text)
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# Get the main building wall data
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@ -593,6 +578,54 @@ def extract_roof_details_epr(text):
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return roof_data
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def extract_roof_details_summary(text):
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"""
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Extracts roof type, insulation, and insulation thickness for each building part
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in the 8.0 Roofs section of the summary report.
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"""
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# Define data structure to hold results
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roof_data = []
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# Locate the entire 8.0 Roofs section
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roof_section_match = re.search(r"8\.0 Roofs:\n(.*?)(?=\n9\.0 Floors:|$)", text, re.DOTALL)
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if not roof_section_match:
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return roof_data # Return empty if no roof section is found
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# Extract the roof section and append "9.0 Floors:" as the boundary
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roof_section = roof_section_match.group(1).strip() + "\n9.0 Floors:"
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# Define pattern to match each building part's roof entry
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building_part_pattern = re.compile(
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r"(Main Property|1st Extension|2nd Extension|[\w\s]+)\n" # Matches each building part label
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r"Type\s+(.*?)(?=\n(?:Insulation|9\.0 Floors:|[A-Z]))" # Matches Roof Type until the next field, label, or end
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r"(?:\nInsulation\s+(.*?)(?=\n(?:Insulation Thickness|9\.0 Floors:|[A-Z])))?" # Optional Insulation
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r"(?:\nInsulation Thickness\s+(.*?)(?=\n(?:9\.0 Floors:|[A-Z])))?", # Optional Insulation Thickness
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re.DOTALL
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)
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# Extract each building part's data
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for match in building_part_pattern.finditer(roof_section):
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part_name = match.group(1).strip() # Building part label
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roof_type = match.group(2).strip() # Roof Type
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roof_insulation = match.group(3).strip() if match.group(3) else None # Optional Insulation
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roof_insulation_thickness = match.group(4).strip() if match.group(4) else None # Optional Thickness
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# Cleaning to handle annoying cases when it comes out like this:
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# 'A Another dwelling above\n1st Extension'
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if roof_type.startswith("A Another dwelling above"):
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roof_type = "A Another dwelling above"
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# Store results for this building part
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roof_data.append({
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"Building Part": part_name,
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"Roof Type": roof_type,
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"Roof Insulation": roof_insulation,
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"Roof Insulation Thickness": roof_insulation_thickness,
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})
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return roof_data
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def extract_wall_details_epr(text):
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"""
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Extracts wall type, insulation, dry-lining, and thickness for each building part
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@ -1691,21 +1724,21 @@ def propsed_wave_3_sample():
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]
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].rename(
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columns={
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"Existing Primary Heating System": "Surveyed Primary Heating System"
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"Existing Primary Heating System": "Survey: Primary Heating System"
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}
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)
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# Concatenate from the wall information
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survey_results["Surveyed: Wall Type"] = survey_results["Main Wall Type"] + ": " + survey_results[
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"Main Wall Insulation Type"]
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survey_results["Survey: Main Wall Type"] = survey_results["Main Wall Type"].astype(str) + ": " + survey_results[
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"Main Wall Insulation Type"].astype(str)
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# Alternative wall
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survey_results["Survey: Main Alternative Wall"] = (
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survey_results["Main Building Alternative Wall Type"] + ": " + survey_results[
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"Main Building Alternative Wall Insulation"]
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survey_results["Main Building Alternative Wall Type"].astype(str) + ": " + survey_results[
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"Main Building Alternative Wall Insulation"].astype(str)
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)
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# Roof information
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survey_results["Survey: Type"] = survey_results["Main Roof Type"] + ": " + survey_results[
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"Main Roof Insulation"] + ": " + survey_results["Main Roof Insulation Thickness"].astype(str)
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survey_results["Survey: Main Roof Type"] = survey_results["Main Roof Type"].astype(str) + ": " + survey_results[
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"Main Roof Insulation"].astype(str) + ": " + survey_results["Main Roof Insulation Thickness"].astype(str)
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# Drop the individual columns:
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survey_results = survey_results.drop(
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@ -1834,6 +1867,11 @@ def propsed_wave_3_sample():
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return surveyed
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survey_attribute_columns = [
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"Survey: Main Wall Type", 'Survey: Main Alternative Wall', 'Survey: Main Roof Type',
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'Survey: Primary Heating System'
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]
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results = []
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for region in tqdm(unique_postal_regions):
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# Take all of the properties in that region
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@ -1845,7 +1883,8 @@ def propsed_wave_3_sample():
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]
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region_assets = region_assets.merge(
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exact_surveyed[["Address ID", "Current EPC Band"]],
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exact_surveyed[
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["Address ID", "Current EPC Band", "Current SAP Rating"] + survey_attribute_columns],
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on="Address ID",
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how="left"
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)
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@ -2286,6 +2325,11 @@ def propsed_wave_3_sample():
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results = pd.concat(results)
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# Check if there are missings in current epc band, current sap rating or any of the survey attributes
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for c in ["Current EPC Band", "Current SAP Rating"] + survey_attribute_columns:
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if pd.isnull(results[c]).sum():
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raise Exception("Something went wrong")
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# home = results[results["Confidence Tier"] == "5 - EPC C or above"].sample(1)
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# region = home["Postal Region"].values[0]
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