Address PR review (dancafc):
- introduce UprnMatch NamedTuple (datatypes/address_match.py) for the
(uprn, address, lexiscore, certificate_number) return, replacing the bare
4-tuple in get_uprn_from_epc_df / get_uprn_from_historic_epc /
HistoricEpcResolver.resolve_uprn. Tuple-compatible, so unpacking is unchanged.
- rename get_uprn_with_epc_df -> get_uprn_from_epc_df (+ callers).
- type resolve_group_ambiguity via a GroupDecision NamedTuple and trim its
docstring.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
EpcClientService.search_by_postcode already returns the matched
certificate number alongside the UPRN, but it was dropped before
persistence. Thread it through get_epc_data_with_postcode ->
get_uprn_with_epc_df / get_uprn_from_historic_epc (using the historic
dataset's lmk_key) -> the address2uprn_certificate_number result
column -> PropertyIdentityInsert -> the property table's new
certificate_number column (assessment-model PR #362).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Review ask (dancafc): resolver/repository locals now carry explicit types
(matches: list[ScoredHistoricEpc], records: list[HistoricEpc], df:
pd.DataFrame, ...) so the flow reads without chasing callee signatures.
CLAUDE.md's Type Safety section gains the rule so future sessions enforce it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The row→domain mapper now names all 93 constructor arguments explicitly
instead of splatting a lowercased dict, takes a plain Mapping (a
DataFrame.to_dict("records") row) instead of a pandas Series, and ignores
columns the domain type doesn't know. A missing/renamed CSV column fails
loudly as a KeyError at the row. Both iterrows() call sites move to
to_dict("records") — pandas-stubs types iterrows' Series unparameterized,
which strict mode rejects. pandas-stubs + boto3-stubs[s3] make the stack
check clean: pyright strict is now 0 errors across the PR's files.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
HistoricEpcS3Repository reached into utils/s3.py (read_csv_gz_from_s3 +
parse_s3_uri), the legacy utility that self-constructs boto3 inside free
functions. The other S3 repositories deliberately depend on the
infrastructure/s3 layer instead (UnstandardisedAddressListCsvS3Repository
injects a CsvS3Client). Bring historic EPC into line.
- Add GzipCsvS3Client(S3Client) in infrastructure/s3: read_csv_gz(key) ->
DataFrame (get_object + gzip decode).
- Inject it into HistoricEpcS3Repository; the bucket lives in the client and
the repo only builds the per-postcode key + maps rows (no S3/HTTP code).
Add with_default_s3_client(s3_root) for composition roots.
- Update main.py and the match_addresses_for_postcode seam to the factory.
- Repo tests inject a real GzipCsvS3Client over a controlled boto stub
(exact key assertions + AccessDenied); add a moto-based client test and a
factory test covering s3_root -> bucket+key.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MQE5TsSuQTeNSCSz9A9GQf
The port accepts a normalised Postcode and rejects malformed/empty ones via
Postcode.is_valid() (PostcodeNotFound) — dropping the per-lookup postcodes.io
HTTP call and the cross-module use of the private _sanitise_postcode. Mapper
helper promoted to public.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
