Jyotisha/scripts/research/reported_offset_sweep.py

#!/usr/bin/env python3
"""Reported-centre sensitivity on exposed public cases, without oracle answers."""
from __future__ import annotations

import argparse
import json
import sys
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any

ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from scripts.active_rectification_event_engine import (
    AYANAMSA, NODE_MODE, compute_candidate_static_contexts,
)
from scripts.minute_rectification_blind_eval import _opaque_winner, implementation_sha256
from scripts.minute_rectification_holdout_validator import validate
from scripts.rectification.candidate_contrast import select_signature_representatives
from scripts.rectification.scoring_service import build_event_contribution_matrix, score_from_matrix
from scripts.research.cluster_width_lib import SEPARATION_LEAD, still_valid_public
from scripts.research.probe_supply_after_six import request_from_case
from scripts.research.sealed_holdout_rerun import (
    DATASET, file_sha256, historical_comparison, implementation_identity,
    opaque_order, verify_frozen_record,
)

OFFSETS = (-30, -20, -15, -10, -8, -5, -3, 0, 3, 5, 8, 10, 15, 20, 30)
RADII = (15, 30, 60)
MINUTE_STEP = 1
# 原值: docs/research/reported_offset_upstream_sync8_2026_10_18.{freeze.json,json}
# 新值: docs/research/reported_offset_upstream_sync9b_2026_10_28.{freeze.json,json}
# 原因: sync9b（BUG-1494～1495）改了 scripts/varga.py，它在扩展身份里；报时窗口输出不变，ERR-110 另存新记录
# 原值: docs/research/reported_offset_upstream_sync9b_2026_10_28.{freeze.json,json}
# 新值: docs/research/reported_offset_d60_mapping_2026_10_29.{freeze.json,json}
# 原因: BUG-1500 修正生产 D60 映射，scripts/varga.py 变了（在扩展身份里）；900 条回放不变，ERR-110 另存新记录
FREEZE = ROOT / "docs/research/reported_offset_d60_mapping_2026_10_29.freeze.json"
REPORT = ROOT / "docs/research/reported_offset_d60_mapping_2026_10_29.json"
LEGACY_REPORT = ROOT / "docs/research/reported_offset_2026_09_20.json"


def shifted_window(case: dict[str, Any], offset: int, radius: int) -> tuple[dict[str, Any], list[datetime]]:
    if radius not in (*RADII, 120):
        raise ValueError("unsupported_radius")
    true_at = datetime.fromisoformat(f"{case['birth']['date']}T{case['birth']['time']}:00")
    reported_at = true_at + timedelta(minutes=offset)
    start = reported_at - timedelta(minutes=radius)
    end = reported_at + timedelta(minutes=radius)
    candidates = [start + timedelta(minutes=i) for i in range(0, radius * 2 + 1, MINUTE_STEP)]
    # Reuse only the established event normalization, then replace the window.
    # The ranker sees neither the truth label nor the simulated offset.
    request = request_from_case(case)
    request.update({
        "birth_date": start.date().isoformat(),
        "start_time": start.strftime("%H:%M"),
        "end_time": end.strftime("%H:%M"),
        "minute_step": MINUTE_STEP,
        "ayanamsa": AYANAMSA,
        "node_mode": NODE_MODE,
    })
    return request, candidates


def score_window(request: dict[str, Any], contexts: list[dict[str, Any]]) -> list[dict[str, Any]]:
    """Use the repaired native single matrix, with candidate dates preserved.

    The historical grouped adapter is archived, not a second active scoring
    path. This is a native scoring replay, still not a production Q&A replay.
    """
    built = build_event_contribution_matrix(request, static_contexts=contexts)
    return score_from_matrix(request, built)


def delivery_moments(public: list[dict[str, Any]], candidates: list[datetime]) -> list[datetime]:
    """Initial delivery envelope in date-aware order; no truth-based narrowing."""
    valid = still_valid_public(public, {}, lead=SEPARATION_LEAD)
    clocks = {str(value)[:5] for row in valid for value in (row.get("cluster_times") or [row["time"]])}
    return [candidate for candidate in candidates if candidate.strftime("%H:%M") in clocks]


def reveal_metrics(
    rows: list[dict[str, Any]], candidates: list[datetime], delivery: list[datetime],
    truth: datetime, benchmark_id: str, case_id: str,
) -> dict[str, Any]:
    ordered = opaque_order(benchmark_id, case_id, rows)
    predicted_clock = _opaque_winner(benchmark_id, case_id, rows)
    by_clock = {candidate.strftime("%H:%M"): candidate for candidate in candidates}
    if len(by_clock) != len(candidates):
        raise ValueError("ambiguous_candidate_clock")
    predicted = by_clock[predicted_clock]
    within = min(candidates) <= truth <= max(candidates)
    in_candidates = truth in candidates
    rank = next((i for i, row in enumerate(ordered, 1) if by_clock[row["time"]] == truth), None)
    covered = bool(delivery) and min(delivery) <= truth <= max(delivery)
    return {
        "truth_in_window": within,
        "truth_in_candidates": in_candidates,
        "true_rank": rank,
        "top_1_hit": predicted == truth,
        "top_1_minute_error": abs((predicted - truth).total_seconds()) / 60,
        "delivery_covers_truth": covered,
        "candidate_count": len(candidates),
        "delivery_width_minutes": int((max(delivery) - min(delivery)).total_seconds() / 60) + 1 if delivery else 0,
    }


def summarize(trials: list[dict[str, Any]], radii: tuple[int, ...], offsets: tuple[int, ...]) -> list[dict[str, Any]]:
    result = []
    for radius in radii:
        for offset in offsets:
            group = [row for row in trials if row["radius_minutes"] == radius and row["offset_minutes"] == offset]
            count = len(group)
            result.append({
                "radius_minutes": radius, "offset_minutes": offset, "trial_count": count,
                **{key: round(sum(row[source] for row in group) / count, 4) if count else None for key, source in (
                    ("truth_in_window_rate", "truth_in_window"),
                    ("top_1_rate", "top_1_hit"),
                    ("delivery_coverage_rate", "delivery_covers_truth"),
                    ("mean_absolute_minute_error", "top_1_minute_error"),
                )},
            })
    return result


def freeze_record(dataset: Path = DATASET, radii: tuple[int, ...] = RADII, offsets: tuple[int, ...] = OFFSETS) -> dict[str, Any]:
    manifest = json.loads(dataset.read_text(encoding="utf-8"))
    legacy_hash = implementation_sha256(manifest["frozen_scoring"]["files"])
    return {
        "record_version": "reported-offset-native-candidate-date-v3",
        "frozen_at_utc": datetime.now(timezone.utc).isoformat(),
        "ayanamsa": AYANAMSA, "node_mode": NODE_MODE,
        "radii_minutes": list(radii), "offsets_minutes": list(offsets), "minute_step": MINUTE_STEP,
        "dataset": dataset.relative_to(ROOT).as_posix(), "dataset_sha256": file_sha256(dataset),
        "dataset_benchmark_id": manifest["benchmark_id"],
        "implementation_sha256": legacy_hash,
        "implementation_sha256_prefix": legacy_hash[:16],
        **implementation_identity(dataset),
        "scorer": "native_event_contribution_matrix_not_shadow_fact_ranker",
        "delivery": "initial_signature_clusters_peak_gap_lt_8_envelope_no_answers_no_elimination",
        "rank": "score_desc_then_sha256(benchmark_id:case_id:candidate_time)",
        "grid": "every_minute_inclusive_not_production_two_minute_sampling",
        "cross_midnight": "date_aware_candidates_distance_and_native_single_matrix",
        "evaluator_sha256": file_sha256(Path(__file__)),
        "replay_revision": "native_candidate_date_v3",
        "supersedes": "candidate_date_grouped_v2_identity_and_path_not_assumed_numerically_wrong",
        "is_blind_evaluation": False, "truth_hidden_from_ranker": True,
        "official_valid_independent_blind": False, "official_blind_trial_count": 0,
        "results_previously_seen": True, "must_not_use_for_tuning": True,
    }


def run(dataset: Path = DATASET, radii: tuple[int, ...] = RADII, offsets: tuple[int, ...] = OFFSETS,
        freeze_path: Path = FREEZE) -> dict[str, Any]:
    frozen = json.loads(freeze_path.read_text(encoding="utf-8"))
    verify_frozen_record(frozen, freeze_record(dataset, radii, offsets))
    replay_started_at = datetime.now(timezone.utc).isoformat()
    manifest = json.loads(dataset.read_text(encoding="utf-8"))
    validation = validate(dataset)
    invalid = validation["invalid_cases"]
    trials = []
    for index, case in enumerate(manifest["cases"], 1):
        if case["case_id"] in invalid:
            continue
        # Reuse static chart calculations, not window-dependent scores or ranks.
        contexts_by_moment: dict[datetime, dict[str, Any]] = {}
        for radius in radii:
            for offset in offsets:
                request, candidates = shifted_window(case, offset, radius)
                missing = [moment for moment in candidates if moment not in contexts_by_moment]
                if missing:
                    contexts_by_moment.update(zip(missing, compute_candidate_static_contexts(request, candidates=missing), strict=True))
                contexts = [contexts_by_moment[moment] for moment in candidates]
                rows = score_window(request, contexts)
                public = select_signature_representatives(rows, contexts)
                delivery = delivery_moments(public, candidates)
                # All scores and the delivery envelope are locked before reveal.
                truth = datetime.fromisoformat(f"{case['birth']['date']}T{case['birth']['time']}:00")
                trials.append({
                    "case_ordinal": index, "offset_minutes": offset, "radius_minutes": radius,
                    **reveal_metrics(rows, candidates, delivery, truth, manifest["benchmark_id"], case["case_id"]),
                })
    verify_frozen_record(frozen, freeze_record(dataset, radii, offsets))
    return {
        "scope": "reported_offset_sensitivity_not_product_accuracy",
        "replay_started_at_utc": replay_started_at,
        "replay_finished_at_utc": datetime.now(timezone.utc).isoformat(),
        "specification": frozen,
        "frozen_record": frozen,
        "freeze_record_path": freeze_path.relative_to(ROOT).as_posix(),
        "implementation_hash_matches_at_replay": True,
        "dataset_hash_matches_at_replay": True,
        "historical_comparison": historical_comparison(LEGACY_REPORT, trials, ("case_ordinal", "radius_minutes", "offset_minutes")),
        "excluded_cases": invalid, "case_count": validation["valid_public_aa_cases"],
        "trial_count": len(trials), "trials": trials,
        "summary": summarize(trials, radii, offsets),
        "widening_geometry": {
            "scored_radii": list(radii),
            "radius_120_scored": 120 in radii,
            "truth_in_window_condition": "abs(reported_offset_minutes) <= radius_minutes",
            "radius_15_first_integer_minute_outside": 16,
            "all_tested_offsets_within_radius_30_60_120": max(map(abs, offsets)) <= 30,
            "boundary": "Geometry rescues candidate inclusion only, not ranking or delivery coverage; no population frequency without a reported-offset distribution.",
        },
    }


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--json", action="store_true")
    parser.add_argument("--freeze", action="store_true", help="Exclusively create the record before replay")
    parser.add_argument("--freeze-path", type=Path, default=FREEZE)
    args = parser.parse_args()
    if args.freeze:
        with args.freeze_path.open("x", encoding="utf-8", newline="\n") as handle:
            json.dump(freeze_record(), handle, ensure_ascii=False, indent=2)
            handle.write("\n")
        print(args.freeze_path.relative_to(ROOT).as_posix())
    else:
        print(json.dumps(run(freeze_path=args.freeze_path), ensure_ascii=False, indent=2))