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https://github.com/Hestia-Homes/Model.git
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optionally inject uprn lookup into parser method, for testing purposes 🟩
This commit is contained in:
Daniel Roth
parent
d26ea2863b
commit
f253bbbf8b
4 changed files with 167 additions and 155 deletions
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@ -1,4 +1,4 @@
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from typing import BinaryIO, Any, Dict, Iterator, List, Tuple
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from typing import BinaryIO, Any, Dict, Iterator, List, Optional, Tuple
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from openpyxl import Workbook, load_workbook
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from collections import defaultdict
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@ -15,7 +15,11 @@ logger = setup_logger()
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class LbwfParser(Parser):
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def parse(self, file_stream: BinaryIO) -> Any:
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def parse(
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self,
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file_stream: BinaryIO,
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location_ref_to_uprn_map: Optional[Dict[str, int]] = None,
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) -> Any:
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wb: Workbook = load_workbook(file_stream)
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address_to_uprn_map: Dict[str, int] = LbwfParser._generate_address_to_uprn_dict(
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wb
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@ -1,9 +1,13 @@
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from abc import ABC, abstractmethod
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from typing import BinaryIO, Any
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from typing import BinaryIO, Any, Dict, Optional
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class Parser(ABC):
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@abstractmethod
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def parse(self, file_stream: BinaryIO) -> Any:
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def parse(
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self,
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file_stream: BinaryIO,
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location_ref_to_uprn_map: Optional[Dict[str, int]] = None,
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) -> Any:
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pass
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@ -1,6 +1,6 @@
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import csv
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from pathlib import Path
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from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, DefaultDict
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from typing import Any, BinaryIO, Dict, List, Optional, Tuple, DefaultDict
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from openpyxl import Workbook, load_workbook
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from collections import defaultdict
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@ -15,11 +15,17 @@ logger = setup_logger()
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class PeabodyParser(Parser):
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def parse(self, file_stream: BinaryIO) -> Any:
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def parse(
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self,
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file_stream: BinaryIO,
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location_ref_to_uprn_map: Optional[Dict[str, int]] = None,
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) -> Any:
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wb: Workbook = load_workbook(file_stream)
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location_ref_to_uprn_map: Dict[str, int] = (
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PeabodyParser._build_location_ref_to_uprn_map()
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)
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if location_ref_to_uprn_map is None:
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location_ref_to_uprn_map: Dict[str, int] = (
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PeabodyParser._build_location_ref_to_uprn_map()
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)
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assets = PeabodyParser._parse_assets(wb)
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@ -128,37 +134,6 @@ class PeabodyParser(Parser):
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condition_survey_date=row[header_indexes["condition_survey_date"]],
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)
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@staticmethod
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def _generate_address_to_uprn_dict(wb: Workbook) -> Dict[str, int | None]:
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sheet = wb["Survey Records - D & Lower"]
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rows: Iterator[Tuple[object | None, ...]] = sheet.iter_rows(values_only=True)
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headers = next(rows)
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header_indexes: Dict[str, int] = PeabodyParser._get_column_indexes_by_name(
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headers
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)
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address_idx = header_indexes["full_address"]
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address_to_uprn: Dict[str, int] = {}
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# Generate random UPRNs for now
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next_uprn = 1 # TODO: get real UPRNs
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for row in rows:
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address = row[address_idx]
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if address is None:
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continue
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address = address.strip()
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if address not in address_to_uprn:
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address_to_uprn[address] = next_uprn
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next_uprn += 1
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return address_to_uprn
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@staticmethod
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def _get_column_indexes_by_name(
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headers: Tuple[object | None, ...],
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@ -1,127 +1,141 @@
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import pytest
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from typing import Any
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from typing import Any, Dict
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from io import BytesIO
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from openpyxl import Workbook
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from datetime import datetime
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from backend.condition.parsing.peabody_parser import PeabodyParser
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from backend.condition.parsing.records.peabody.peabody_asset_condition import PeabodyAssetCondition
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from backend.condition.parsing.records.peabody.peabody_asset_condition import (
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PeabodyAssetCondition,
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)
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from backend.condition.parsing.records.peabody.peabody_property import PeabodyProperty
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@pytest.fixture
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def peabody_assets_xlsx_bytes() -> BytesIO:
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wb = Workbook()
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survey_records_d_and_lower = wb.active
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survey_records_d_and_lower.title = "Survey Records - D & Lower"
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survey_records_d_and_lower.append([
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"Lo_Reference",
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"full_address",
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"location_type_code",
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"Parent_Lo_Reference",
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"Element_Code",
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"Element",
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"Sub_Element_Code",
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"Sub_Element",
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"Material_Code",
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"material_or_answer",
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"Renewal_Quantity",
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"Renewal_Year",
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"Renewal_Cost",
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"cloned",
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"lo_type_code",
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"condition_survey_date",
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])
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survey_records_d_and_lower.append([
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"B000RAND",
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"1 RANDOM HOUSE LONDON",
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3,
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"RAND2EST",
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110,
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"ROOFS",
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1,
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"Primary Roof",
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9,
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"Other",
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3,
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2054,
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330,
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"N",
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3,
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datetime(2025,12,4,9,17,0)
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])
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survey_records_d_and_lower.append([
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"B000BLOCK",
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"1100 BLOCK",
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3,
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"RAND2EST",
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110,
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"ROOFS",
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1,
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"Primary Roof",
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9,
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"Other",
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3,
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2054,
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330,
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"N",
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3,
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datetime(2025,12,4,9,17,0)
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])
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survey_records_d_and_lower.append([
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"B000FAKE",
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"3 FAKE CLOSE LONDON",
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3,
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"FAKEEST",
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100,
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"GENERAL",
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15,
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"External Decoration",
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2,
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"Normal",
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1,
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2035,
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1500.7,
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"N",
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3,
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datetime(2025,7,5,0,0,0)
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])
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survey_records_d_and_lower.append([
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"B000MIS",
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"99 MISC ROAD LONDON",
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3,
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"300828",
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54,
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"HHSRS",
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29,
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"HHSRS Structural Collapse & Falling Elements",
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4,
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"HHSRS Moderate",
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2,
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2027,
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None,
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"N",
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3,
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None
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])
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survey_records_d_and_lower.append([
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"B000MIS",
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"99 MISC ROAD LONDON",
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3,
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"300828",
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53,
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"External",
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2,
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"Chimney",
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2,
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"Present",
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33,
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2053,
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3531,
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"N",
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3,
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None
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])
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survey_records_d_and_lower.append(
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[
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"Lo_Reference",
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"full_address",
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"location_type_code",
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"Parent_Lo_Reference",
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"Element_Code",
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"Element",
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"Sub_Element_Code",
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"Sub_Element",
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"Material_Code",
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"material_or_answer",
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"Renewal_Quantity",
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"Renewal_Year",
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"Renewal_Cost",
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"cloned",
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"lo_type_code",
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"condition_survey_date",
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]
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)
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survey_records_d_and_lower.append(
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[
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"B000RAND",
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"1 RANDOM HOUSE LONDON",
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3,
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"RAND2EST",
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110,
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"ROOFS",
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1,
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"Primary Roof",
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9,
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"Other",
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3,
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2054,
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330,
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"N",
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3,
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datetime(2025, 12, 4, 9, 17, 0),
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]
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)
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survey_records_d_and_lower.append(
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[
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"B000BLOCK",
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"1100 BLOCK",
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3,
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"RAND2EST",
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110,
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"ROOFS",
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1,
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"Primary Roof",
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9,
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"Other",
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3,
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2054,
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330,
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"N",
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3,
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datetime(2025, 12, 4, 9, 17, 0),
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]
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)
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survey_records_d_and_lower.append(
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[
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"B000FAKE",
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"3 FAKE CLOSE LONDON",
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3,
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"FAKEEST",
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100,
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"GENERAL",
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15,
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"External Decoration",
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2,
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"Normal",
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1,
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2035,
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1500.7,
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"N",
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3,
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datetime(2025, 7, 5, 0, 0, 0),
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]
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)
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survey_records_d_and_lower.append(
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[
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"B000MIS",
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"99 MISC ROAD LONDON",
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3,
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"300828",
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54,
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"HHSRS",
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29,
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"HHSRS Structural Collapse & Falling Elements",
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4,
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"HHSRS Moderate",
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2,
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2027,
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None,
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"N",
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3,
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None,
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]
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)
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survey_records_d_and_lower.append(
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[
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"B000MIS",
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"99 MISC ROAD LONDON",
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3,
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"300828",
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53,
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"External",
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2,
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"Chimney",
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2,
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"Present",
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33,
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2053,
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3531,
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"N",
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3,
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None,
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]
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)
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stream = BytesIO()
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wb.save(stream)
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@ -129,18 +143,32 @@ def peabody_assets_xlsx_bytes() -> BytesIO:
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return stream
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def test_peabody_parser_parses_conditions(peabody_assets_xlsx_bytes):
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@pytest.fixture
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def location_ref_to_uprn_map() -> Dict[str, int]:
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return {
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"B000RAND": 1,
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"B000BLOCK": 2,
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"B000FAKE": 3,
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"B000MIS": 4,
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}
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def test_peabody_parser_parses_conditions(
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peabody_assets_xlsx_bytes, location_ref_to_uprn_map
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):
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# arrange
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parser = PeabodyParser()
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# act
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result: Any = parser.parse(peabody_assets_xlsx_bytes)
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result: Any = parser.parse(peabody_assets_xlsx_bytes, location_ref_to_uprn_map)
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# assert
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assert len(result) == 3
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assert all(isinstance(item, PeabodyProperty) for item in result)
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@pytest.fixture
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def asset_condition_factory():
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def _factory(full_address: str) -> PeabodyAssetCondition:
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@ -165,6 +193,7 @@ def asset_condition_factory():
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return _factory
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@pytest.mark.parametrize(
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"full_address, expected_block_level",
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[
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@ -175,7 +204,7 @@ def asset_condition_factory():
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("81A-B GORE ROAD LONDON", True),
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("73 & 74 HARVEST COURT ST. ALBANS", True),
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("25 HAVERSHAM COURT GREENFORD", False),
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("FLAT 10 SPARROW COURT SOUTHMERE DRIVE LONDON SE2 9ES", False)
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("FLAT 10 SPARROW COURT SOUTHMERE DRIVE LONDON SE2 9ES", False),
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],
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)
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def test_peabody_asset_is_block_level(
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@ -187,4 +216,4 @@ def test_peabody_asset_is_block_level(
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asset_condition = asset_condition_factory(full_address)
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# act + assert
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assert asset_condition.is_block_level == expected_block_level
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assert asset_condition.is_block_level == expected_block_level
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