Initial SQL-only 1C adapter baseline
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#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import json
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from collections import Counter
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from pathlib import Path
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from typing import Any
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SCALAR_VALUE_TYPES = {"integer_atom", "scalar"}
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SCALAR_REASONS = {"value_type_not_smoke_safe", "identity_or_binding_property"}
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PROPERTY_ALIASES = {
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"group": "Группа",
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"id": "Идентификатор",
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"name": "Имя",
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"view": "Вид",
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"title": "Заголовок",
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"command_bar_location": "ПоложениеВКоманднойПанели",
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}
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MANUAL_ONLY_PROPERTIES = {"Идентификатор", "Имя", "ПутьКДанным", "Данные", "Вид"}
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REFERENCE_PROPERTIES = {"Группа", "group"}
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def load_entries(report: dict[str, Any]) -> list[dict[str, Any]]:
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matrix = report.get("matrix") if isinstance(report.get("matrix"), dict) else {}
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entries = matrix.get("entries") if isinstance(matrix.get("entries"), list) else []
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return [entry for entry in entries if isinstance(entry, dict)]
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def normalize_property_name(prop: dict[str, Any]) -> str:
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raw = str(prop.get("semantic_name") or prop.get("canonical_property") or prop.get("property") or "")
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return PROPERTY_ALIASES.get(raw, raw)
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def scalar_bucket(entry: dict[str, Any]) -> str:
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prop = entry.get("property") if isinstance(entry.get("property"), dict) else {}
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target = entry.get("requested_target") if isinstance(entry.get("requested_target"), dict) else {}
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name = normalize_property_name(prop)
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marker = str(target.get("marker") or "")
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parameter_index = prop.get("parameter_index")
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semantic_status = str(prop.get("semantic_status") or "")
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if name in MANUAL_ONLY_PROPERTIES or "маркер" in name.casefold():
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return "manual_only_identity_or_structural"
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if name in REFERENCE_PROPERTIES:
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return "reference_or_container_rule_required"
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if semantic_status and semantic_status != "unknown":
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return "semantic_scalar_needs_allowed_values"
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if parameter_index is not None:
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return f"learn_marker_{marker}_parameter_{parameter_index}"
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return "learn_named_scalar_semantics"
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def recommended_action(bucket: str) -> str:
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if bucket == "manual_only_identity_or_structural":
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return "do_not_generic_write"
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if bucket == "reference_or_container_rule_required":
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return "learn_reference_write_rule"
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if bucket == "semantic_scalar_needs_allowed_values":
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return "collect_allowed_values_and_smoke"
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if bucket.startswith("learn_marker_"):
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return "run_before_after_learning_for_parameter"
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return "run_before_after_learning_for_named_scalar"
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def make_key(entry: dict[str, Any]) -> tuple[str, str, str, str, str]:
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prop = entry.get("property") if isinstance(entry.get("property"), dict) else {}
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target = entry.get("requested_target") if isinstance(entry.get("requested_target"), dict) else {}
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effective = entry.get("effective_target") if isinstance(entry.get("effective_target"), dict) else {}
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return (
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scalar_bucket(entry),
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normalize_property_name(prop),
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str(target.get("marker") or effective.get("marker") or ""),
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str(prop.get("parameter_index") if prop.get("parameter_index") is not None else ""),
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str(prop.get("value_type") or ""),
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)
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def main() -> int:
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parser = argparse.ArgumentParser(description="Build scalar learning registry from 1C write matrix gaps.")
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parser.add_argument("--matrix-report", type=Path, required=True, help="Report produced by scripts/smoke_1c_write_matrix.py.")
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parser.add_argument("--output", type=Path, required=True, help="Output scalar registry JSON path.")
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parser.add_argument("--sample-limit", type=int, default=8, help="Examples per scalar group.")
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args = parser.parse_args()
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report = json.loads(args.matrix_report.read_text(encoding="utf-8"))
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groups: dict[tuple[str, str, str, str, str], dict[str, Any]] = {}
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skipped = Counter()
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for entry in load_entries(report):
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prop = entry.get("property") if isinstance(entry.get("property"), dict) else {}
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if entry.get("can_smoke"):
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skipped["can_smoke"] += 1
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continue
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reason = str(entry.get("reason") or "")
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value_type = str(prop.get("value_type") or "")
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if reason not in SCALAR_REASONS:
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skipped[f"reason:{reason}"] += 1
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continue
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if value_type not in SCALAR_VALUE_TYPES:
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skipped[f"value_type:{value_type}"] += 1
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continue
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key = make_key(entry)
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bucket, name, marker, parameter_index, _ = key
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row = groups.setdefault(
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key,
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{
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"bucket": bucket,
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"property": name,
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"marker": marker or None,
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"parameter_index": parameter_index or None,
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"value_type": value_type,
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"recommended_action": recommended_action(bucket),
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"observed_values": Counter(),
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"reasons": Counter(),
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"sections": Counter(),
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"type_names": Counter(),
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"examples": [],
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},
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)
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target = entry.get("requested_target") if isinstance(entry.get("requested_target"), dict) else {}
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effective = entry.get("effective_target") if isinstance(entry.get("effective_target"), dict) else {}
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old = "" if prop.get("old") is None else str(prop.get("old"))
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row["observed_values"][old] += 1
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row["reasons"][reason] += 1
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row["sections"][str(effective.get("section") or target.get("section") or "")] += 1
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row["type_names"][str(effective.get("type_name") or target.get("type_name") or "")] += 1
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if len(row["examples"]) < args.sample_limit:
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row["examples"].append(
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{
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"target": target.get("name") or target.get("path"),
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"requested_section": target.get("section"),
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"effective_section": effective.get("section"),
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"type_name": effective.get("type_name") or target.get("type_name"),
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"presentation": prop.get("presentation"),
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"semantic_name": prop.get("semantic_name"),
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"semantic_group": prop.get("semantic_group"),
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"old": old,
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"write_path": prop.get("write_path"),
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"reason": reason,
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}
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)
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scalars = []
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for row in groups.values():
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scalars.append(
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{
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**{key: value for key, value in row.items() if key not in {"observed_values", "reasons", "sections", "type_names"}},
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"observed_values": dict(row["observed_values"].most_common()),
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"reasons": dict(row["reasons"]),
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"sections": dict(row["sections"]),
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"type_names": dict(row["type_names"].most_common()),
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"counts": {
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"entries": sum(row["observed_values"].values()),
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"observed_values": len(row["observed_values"]),
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"examples": len(row["examples"]),
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},
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}
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)
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scalars.sort(
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key=lambda item: (
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str(item.get("recommended_action")),
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-int(item["counts"]["entries"]),
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str(item.get("marker")),
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str(item.get("parameter_index")),
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str(item.get("property")),
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)
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)
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result = {
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"schema": "onec_form_write_scalar_registry.v1",
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"status": "ok",
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"source_report": str(args.matrix_report),
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"counts": {
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"groups": len(scalars),
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"entries": sum(int(item["counts"]["entries"]) for item in scalars),
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"by_action": dict(Counter(str(item.get("recommended_action")) for item in scalars)),
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"by_bucket": dict(Counter(str(item.get("bucket")) for item in scalars)),
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"skipped": dict(skipped),
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},
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"scalars": scalars,
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}
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args.output.parent.mkdir(parents=True, exist_ok=True)
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args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
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print(json.dumps({"schema": result["schema"], "status": "ok", "counts": result["counts"], "path": str(args.output)}, ensure_ascii=False, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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