Merge pull request #209 from Hestia-Homes/cost-model

Cost model

.idea/Model.iml

@@ -7,7 +7,7 @@
       <sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
       <sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.10 (backend)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (area_data)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,6 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (backend)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (area_data)" project-jdk-type="Python SDK" />
   <component name="PythonCompatibilityInspectionAdvertiser">
     <option name="version" value="3" />
   </component>

model_data/simulation_system/area_data.py

@@ -0,0 +1,314 @@
+"""
+This script produces the dataset used to model the wall area of properties, which is used to estimate the cost
+of insulation measures within homes
+"""
+import os
+import boto3
+import PyPDF2
+import re
+import json
+from epc_api.client import EpcClient
+from io import BytesIO
+from datetime import datetime
+
+bucket = "retrofit-datalake-dev"
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN", None)
+
+
+def list_files_in_s3_folder(bucket_name, folder_name):
+    """
+    List files in a specific S3 bucket and folder.
+
+    Parameters:
+    - bucket_name: Name of the S3 bucket.
+    - folder_name: Name of the folder (prefix) within the bucket.
+
+    Returns:
+    - A list of file names within the specified folder.
+    """
+
+    # Ensure folder name ends with a '/'
+    if not folder_name.endswith('/'):
+        folder_name += '/'
+
+    s3_client = boto3.client('s3')
+
+    # Initialize empty list to store file names
+    files = []
+
+    # Initialize paginator
+    paginator = s3_client.get_paginator('list_objects_v2')
+
+    # Create a PageIterator from the Paginator
+    page_iterator = paginator.paginate(Bucket=bucket_name, Prefix=folder_name)
+
+    for page in page_iterator:
+        # Extract file names from the current page and append to the list
+        files.extend([item['Key'] for item in page.get('Contents', [])])
+
+    return files
+
+
+def fetch_and_parse_pdf_from_s3(bucket_name, filename):
+    """
+    Fetch a PDF from an S3 bucket and parse its content.
+
+    Parameters:
+    - bucket_name: Name of the S3 bucket.
+    - pdf_key: Path (key) of the PDF file within the bucket.
+
+    Returns:
+    - text: Extracted text from the PDF.
+    """
+
+    s3_client = boto3.client('s3')
+    response = s3_client.get_object(Bucket=bucket_name, Key=filename)
+
+    # Create a BytesIO object from the PDF bytes
+    pdf_content = BytesIO(response['Body'].read())
+
+    # Use PyPDF2 to read the PDF content
+    reader = PyPDF2.PdfReader(pdf_content)
+
+    # Extract text from each page
+    pages = []
+    for page_num in range(len(reader.pages)):
+        page = reader.pages[page_num]
+
+        text = page.extract_text()
+        text = remove_excess_newlines(text)
+        pages.append(text.split("\n"))
+
+    return pages
+
+
+def fetch_json_from_s3(bucket_name, file_name):
+    # Create an S3 client
+    s3 = boto3.client('s3')
+
+    # Fetch the file from S3
+    response = s3.get_object(Bucket=bucket_name, Key=file_name)
+
+    # Parse and return the JSON data
+    return json.loads(response['Body'].read().decode('utf-8'))
+
+
+def write_json_to_s3(bucket_name, file_name, json_data):
+    """
+    Write JSON data to a file in an S3 bucket.
+
+    Parameters:
+    - bucket_name: Name of the S3 bucket.
+    - file_name: Path (key) of the file within the bucket.
+    - json_data: JSON data to be saved.
+    """
+
+    s3_client = boto3.client('s3')
+
+    # Convert the JSON data to a string
+    json_string = json.dumps(json_data)
+
+    # Upload the JSON string to S3
+    s3_client.put_object(Bucket=bucket_name, Key=file_name, Body=json_string)
+
+
+def check_s3_file_exists(bucket_name, file_name):
+    """
+    Check if a file exists in an S3 bucket.
+
+    Parameters:
+    - bucket_name: Name of the S3 bucket.
+    - file_name: Path (key) of the file within the bucket.
+
+    Returns:
+    - bool: True if the file exists, False otherwise.
+    """
+
+    s3_client = boto3.client('s3')
+
+    try:
+        # Check if the object exists by attempting to retrieve its metadata
+        s3_client.head_object(Bucket=bucket_name, Key=file_name)
+        return True
+    except s3_client.exceptions.ClientError as e:
+        # If the error code is 404 (Not Found), then the file doesn't exist
+        if e.response['Error']['Code'] == '404':
+            return False
+        # If there's any other exception, raise it
+        raise
+
+
+def remove_excess_newlines(text):
+    return re.sub('\n+', '\n', text).strip()
+
+
+def search_pages(pages, search_term) -> (
+    str | None, int | None, int | None
+):
+    """
+    This method looks for a search term in the EPR and returns the first instance of it
+    :param pages: list of pages to search through
+    :param search_term: The term to search for
+    :return: The text, page number and page index of the first instance of the search term
+    """
+
+    to_page = len(pages)
+    from_page = 0
+    from_index = 0
+
+    for page_num in range(from_page, to_page + 1):
+
+        page_to_index = len(pages[page_num])
+
+        for page_index in range(from_index, page_to_index):
+            if search_term in pages[page_num][page_index]:
+                return pages[page_num][page_index], page_num, page_index
+
+    return None, None, None
+
+
+def check_page(pages, page_num, page_index):
+    if page_num > len(pages):
+        return False
+
+    if page_index > len(pages[page_num]):
+        return False
+
+    return True
+
+
+def extract_areas():
+    """
+    This function extracts the net and gross wall areas from the pdf sap calculation files
+    """
+    files = list_files_in_s3_folder(bucket, "full_sap_calculations")
+
+    # get pdfs
+    sap_calulation_pdfs = [file for file in files if file.endswith(".pdf")]
+
+    # For each pdf, we pull out the net & gross wall areas
+    if check_s3_file_exists(bucket_name=bucket, file_name="wall-area-data/wall-area.json"):
+        data = fetch_json_from_s3(bucket_name=bucket, file_name="wall-area-data/wall-area.json")
+        data = json.loads(data)
+    else:
+        data = []
+
+    used_files = [x["filename"] for x in data]
+
+    sap_calulation_pdfs = [filename for filename in sap_calulation_pdfs if filename.split("/")[-1] not in used_files]
+
+    for sap_calculation_file in sap_calulation_pdfs:
+
+        # Download pdf
+        pdf_pages = fetch_and_parse_pdf_from_s3(bucket, sap_calculation_file)
+
+        # We search for net and gross wall areas
+        result = search_pages(pdf_pages, "External walls Main")[0]
+        # This is a row in a table where the columns are:
+        # Element, Gross, Openings, NetArea, U-value, A x U, K-value, A x K
+        # The values we're interested in are Gross and NetArea
+        values = result.split("External walls Main")[1].strip().split(" ")
+        # Remove the empty white space - we should now have the fields we want
+        values = [v for v in values if v]
+        gross_area = float(values[0])
+        net_area = float(values[2])
+
+        # Search for property identifiers
+        _, pagenum, page_idx = search_pages(pdf_pages, 'Prop Type Ref')
+        if pagenum != 0:
+            raise ValueError("Property reference not found on the first page")
+        # the reference will be on the next line
+        property_reference = pdf_pages[pagenum][page_idx + 1]
+        property_reference_number = pdf_pages[pagenum][page_idx + 2]
+        address = pdf_pages[pagenum][page_idx + 4]
+
+        # Search for issued date - the date appears in the field before
+        _, date_pagenum, date_page_idx = search_pages(pdf_pages, 'Issued on Date')
+        issued_date = pdf_pages[date_pagenum][date_page_idx + -1]
+
+        data.append(
+            {
+                "property_reference": property_reference,
+                "reference_number": property_reference_number,
+                "address": address,
+                "gross_area": gross_area,
+                "net_area": net_area,
+                "filename": sap_calculation_file,
+                "issued_date": issued_date,
+            }
+        )
+
+    write_json_to_s3(bucket_name=bucket, file_name="wall-area-data/wall-area.json", json_data=json.dumps(data))
+
+    return data
+
+
+def combine_area_data(area_data=None):
+    """
+    This function will merge the area data onto additional features which are
+    :param area_data:  list of dictionaries, containing the areas and the
+    """
+
+    epc_client = EpcClient(auth_token=EPC_AUTH_TOKEN)
+
+    model_data = []
+    for area_config in area_data:
+        address = area_config["address"]
+
+        # The addresses seems to have this structure:
+        # <house number>, <street name>, <area (optional)>, <postcode>"
+        # Where area is not always in the address
+        address_destructured = address.split(",")
+        house_number = address_destructured[0].strip()
+        street_name = address_destructured[1].strip()
+        postcode = address_destructured[-1].strip()
+
+        # Fetch epc data
+        epc_response = epc_client.domestic.search(
+            params={
+                "postcode": postcode,
+                "address": ", ".join([house_number, street_name]),
+            }
+        )
+
+        epc_data = epc_response["rows"]
+        if len(epc_data) == 0:
+            raise ValueError("No EPC data - investigate me")
+
+        if len(epc_data) > 1:
+            issued_date = datetime.strptime(area_config["issued_date"], '%d/%m/%Y')
+            # We get the epc data closest to the issued date. On the edge case that we have two EPC records that are
+            # equally far away from the issued_date, we take the most recent EPC record
+            # We sort on lodgement date
+            epc_data = sorted(
+                epc_data, key=lambda x: datetime.strptime(x['lodgement-date'], '%Y-%m-%d'), reverse=True
+            )
+
+            days_since = [
+                abs((datetime.strptime(x["lodgement-date"], '%Y-%m-%d') - issued_date).days) for x in epc_data
+            ]
+            # find the locaton of the closest
+            closest_index = [i for i, days in enumerate(days_since) if days == min(days_since)][0]
+            # Take just that epc record
+            epc_data = [epc_data[closest_index]]
+
+        model_data.append(
+            dict(
+                gross_area=area_config["gross_area"],
+                net_area=area_config["net_area"],
+                **epc_data[0]
+            )
+        )
+
+    # Save data
+    write_json_to_s3(
+        bucket_name=bucket,
+        file_name="wall-area-data/wall-area-model-data.json",
+        json_data=json.dumps(model_data)
+    )
+
+
+def handler():
+    area_data = extract_areas()
+
+    combine_area_data(area_data)

model_data/simulation_system/requirements/area_data.txt

@@ -0,0 +1,4 @@
+boto3==1.28.38
+PyPDF2==3.0.1
+pydantic==1.10.11
+epc-api-python==1.0.2