"""Rebuild run-level outcomes from saved run artefacts (no re-simulation): used after metric changes and for
threshold-sensitivity grids. Every recomputed number is still traceable to the saved parquet files."""
from __future__ import annotations

import json
from dataclasses import dataclass
from pathlib import Path

import pandas as pd

from ..experiments.config import RunConfig
from ..metrics.outcomes import run_outcomes


@dataclass
class SavedRun:
    config: RunConfig
    ticks: pd.DataFrame
    trades: pd.DataFrame
    agents: pd.DataFrame
    llm: pd.DataFrame
    manifest: dict
    run_dir: Path


def load_run(run_dir: str | Path) -> SavedRun:
    rd = Path(run_dir)
    cfg = RunConfig.from_yaml(rd / "config.yaml")
    with open(rd / "manifest.json") as f:
        man = json.load(f)
    llm_p = rd / "llm_decisions.parquet"
    return SavedRun(cfg, pd.read_parquet(rd / "ticks.parquet"), pd.read_parquet(rd / "trades.parquet"), pd.read_parquet(rd / "agents.parquet"),
                    pd.read_parquet(llm_p) if llm_p.exists() else pd.DataFrame(columns=["t", "agent_id", "provider", "model", "valid"]), man, rd)


def recompute_design(design_dir: str | Path, write: bool = True, **thresholds) -> pd.DataFrame:
    design_dir = Path(design_dir)
    rows = []
    for man in sorted(design_dir.glob("*/seed*/manifest.json")):
        r = load_run(man.parent)
        oc = run_outcomes(r, **thresholds)
        oc["run_dir"] = str(man.parent)
        rows.append(oc)
    df = pd.DataFrame(rows)
    if write and not thresholds:
        df.to_parquet(design_dir / "outcomes.parquet", index=False)
        for r in rows:
            with open(Path(r["run_dir"]) / "outcomes.json", "w") as f:
                json.dump(r, f, indent=1, default=float)
    return df