227116fe2d
The import corpus is uniform: every PDF is named <index>.pdf, so the file column (the spreadsheet's datei value) is redundant. Remove file from CanonicalDocument, RawRow, _FIELDS, to_canonical, and DOC_COLUMNS, plus the now-moot index_file_mismatch review flag/CSV/stat and the datei header mapping. date_end and the tree person_id are kept. Refs #686 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
44 lines
2.3 KiB
Markdown
44 lines
2.3 KiB
Markdown
# Import Normalizer
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Transforms the raw family-archive spreadsheets in `../../import/` into a clean canonical
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dataset (`out/`) plus review reports (`review/`). See the spec:
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`../../docs/import-migration/02-normalization-spec.md`.
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## Setup
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Requires **Python 3.12** (uses `StrEnum`).
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```bash
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python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
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```
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## Run
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```bash
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.venv/bin/python normalize.py
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```
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Outputs:
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- `out/canonical-documents.xlsx`, `out/canonical-persons.xlsx`
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- `review/*.csv` (residue to fix), `review/summary.txt` (grouped run stats incl. unknown-date rate)
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## Iteration loop
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1. **Run.** Read `review/summary.txt` for the health snapshot.
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2. **Fix the residue** by editing the version-controlled overrides files, then re-run. Repeat.
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| Review file | What to do |
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| --- | --- |
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| `unparsed-dates.csv` | For each `raw` (sorted by frequency), fill `suggested_iso` + `suggested_precision`, then paste `raw,suggested_iso,suggested_precision` into `overrides/dates.csv` (header `raw,iso,precision`). |
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| `unresolved-names.csv` | Names whose value is itself problematic, grouped by `category`: `unknown` (`?`/illegible), `single_token` (first OR last name only), `relational` (`Tante …`), `collective` (`Familie …`), `prose` (a description landed in a name column), `ambiguous_pair` (two given names → likely two people, not auto-split). Review highest-impact categories first; add decisions to `overrides/names.csv` (look up valid ids in `out/canonical-persons.xlsx`). |
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| `duplicate-index.csv`, `blank-index-rows.csv`, `skipped-x-suffix.csv` | Inspect; fix in the source spreadsheet if needed. |
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> `unresolved-names.csv` is the focused "names that need a human" list. Non-family
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> correspondents that simply aren't in the register are NOT reported — they just become
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> provisional persons in `out/canonical-persons.xlsx` (the `unmatched_name_strings` count in
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> `summary.txt` tracks how many). The given-name set that drives `ambiguous_pair` detection is
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> the register's first names plus `config.EXTRA_GIVEN_NAMES` — add names there if a real
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> two-person cell isn't being flagged.
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**Valid `person_id` values** all come from the `person_id` column of `out/canonical-persons.xlsx`.
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## Tests
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```bash
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.venv/bin/python -m pytest tests/test_dates.py -v # run files individually (never the whole suite at once)
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```
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