09ba7e74e3
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>
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""Document row extraction, triage, and the canonical document record."""
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from dataclasses import dataclass, field
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from enum import Enum, auto
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import dates as _dates
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import tags as _tags
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class Triage(Enum):
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OK = auto()
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EMPTY = auto()
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BLANK_INDEX = auto()
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X_SUFFIX = auto()
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@dataclass
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class RawRow:
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source_row: int
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index: str = ""
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box: str = ""
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folder: str = ""
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sender: str = ""
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receivers: str = ""
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date: str = ""
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location: str = ""
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tags: str = ""
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summary: str = ""
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@dataclass
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class CanonicalDocument:
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index: str
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box: str = ""
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folder: str = ""
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sender_person_id: str = ""
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sender_name: str = ""
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receiver_person_ids: list = field(default_factory=list)
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receiver_names: list = field(default_factory=list)
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date_iso: str = ""
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date_raw: str = ""
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date_precision: str = ""
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date_end: str = ""
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location: str = ""
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tags: list = field(default_factory=list)
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summary: str = ""
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source_row: int = 0
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needs_review: list = field(default_factory=list)
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_FIELDS = ["index", "box", "folder", "sender", "receivers", "date", "location", "tags", "summary"]
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def extract_row(cells: list[str], header: dict[str, int], source_row: int) -> RawRow:
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def get(field_name):
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idx = header.get(field_name)
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if idx is None or idx >= len(cells):
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return ""
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return (cells[idx] or "").strip()
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return RawRow(source_row=source_row, **{f: get(f) for f in _FIELDS})
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def triage(cells: list[str], index_col: int = 0) -> Triage:
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nonempty = [c for c in cells if c and str(c).strip()]
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if not nonempty:
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return Triage.EMPTY
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index = (cells[index_col] or "").strip() if 0 <= index_col < len(cells) else ""
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if not index:
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return Triage.BLANK_INDEX
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if index.endswith("x"):
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return Triage.X_SUFFIX
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return Triage.OK
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def classify_blank_index(cells: list[str], header: dict[str, int]) -> str:
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"""REQ-TRIAGE-02: 'section_banner' if only name columns are populated, else 'data_no_index'."""
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name_cols = {header.get("sender"), header.get("receivers")} - {None}
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populated = {i for i, c in enumerate(cells) if c and str(c).strip()}
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if populated and populated <= name_cols:
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return "section_banner"
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return "data_no_index"
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def to_canonical(raw, ctx, date_overrides: dict, approved_themes: frozenset = frozenset()) -> CanonicalDocument:
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pd = _dates.parse_date(raw.date, date_overrides)
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flags = []
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sender_id, sender_name, sender_matched, sender_multi = ctx.resolve_sender(raw.sender, raw.source_row)
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if raw.sender.strip() and not sender_matched:
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flags.append("unmatched_sender")
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if sender_multi:
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flags.append("multi_sender")
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receivers = ctx.resolve_receivers(raw.receivers, raw.source_row)
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if any(not matched for _, _, matched in receivers):
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flags.append("unmatched_receiver")
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if raw.date.strip() and pd.precision == _dates.Precision.UNKNOWN:
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flags.append("unparsed_date")
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if pd.needs_review:
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flags.append("range_end_unparsed")
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return CanonicalDocument(
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index=raw.index, box=raw.box, folder=raw.folder,
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sender_person_id=sender_id, sender_name=sender_name,
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receiver_person_ids=[r[0] for r in receivers],
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receiver_names=[r[1] for r in receivers],
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date_iso=pd.iso or "", date_raw=raw.date, date_precision=str(pd.precision),
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date_end=pd.end or "",
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location=raw.location, tags=_tags.generate_tags(raw.tags, raw.summary, approved_themes), summary=raw.summary,
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source_row=raw.source_row, needs_review=flags,
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)
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