"""Monte Carlo statistical-envelope evaluator for NFT-RES-03 (AZ-434 / AC-NEW-4).

The SUT promises an *honest* covariance: across many runs with seeded
perturbations (gain noise, IMU bias, frame-drop pattern, outlier
injection), the actual error distribution should stay within the
``1.96 × cov_semi_major_m`` envelope at the 95th percentile. The
runner drives N iterations and feeds this module the per-iteration
per-frame ``(error_m, cov_semi_major_m)`` pairs.

ACs evaluated (per AZ-434):

* AC-1 — iteration_count == ``MIN_ITERATION_COUNT`` (100). Partial
  completion is a hard failure; the runner is responsible for
  re-running missing iterations rather than passing a short list.
* AC-2 — determinism check: re-running with the same master_seed
  produces bit-identical iteration outcomes. This module records the
  master_seed and a SHA-256 of the per-iteration ``(error_m,
  cov_semi_major_m)`` tuples; the scenario harness compares the seed
  + hash across two runs (the comparison itself is not a method on
  this evaluator — it's a scenario-level check).
* AC-3 — global aggregate envelope: across all 100 × N_frames
  samples, ``count(error_m ≤ 1.96 × cov_semi_major_m) / total ≥
  0.95``.

Public-boundary discipline: does NOT import any
``src/gps_denied_onboard`` symbol.
"""

from __future__ import annotations

import csv
import hashlib
from dataclasses import dataclass
from pathlib import Path
from typing import Sequence

MIN_ITERATION_COUNT = 100
ENVELOPE_MULTIPLIER = 1.96  # 95th-percentile envelope on a normal cov_semi_major
ENVELOPE_RATIO_BUDGET = 0.95


@dataclass(frozen=True)
class FrameSample:
    """One per-frame ``(error_m, cov_semi_major_m)`` pair.

    ``error_m`` is the WGS84 Vincenty distance between the SUT's
    estimate and ground truth at that frame; ``cov_semi_major_m`` is
    the SUT's self-reported uncertainty semi-major-axis (m) at the
    same frame.
    """

    error_m: float
    cov_semi_major_m: float


@dataclass(frozen=True)
class IterationOutcome:
    """One Monte Carlo iteration: ordered per-frame samples + iteration seed."""

    iteration_id: str
    iteration_seed: int
    samples: tuple[FrameSample, ...]

    @property
    def frame_count(self) -> int:
        return len(self.samples)


@dataclass(frozen=True)
class MonteCarloReport:
    """Aggregate NFT-RES-03 result over N iterations."""

    iterations: tuple[IterationOutcome, ...]
    master_seed: int
    iteration_count: int
    total_samples: int
    covered_samples: int
    envelope_ratio: float | None
    min_iteration_count: int
    envelope_ratio_budget: float

    @property
    def passes_iteration_count(self) -> bool:
        return self.iteration_count >= self.min_iteration_count

    @property
    def passes_envelope(self) -> bool:
        return (
            self.envelope_ratio is not None
            and self.envelope_ratio >= self.envelope_ratio_budget
        )

    @property
    def passes(self) -> bool:
        return self.passes_iteration_count and self.passes_envelope


def iteration_hash(iteration: IterationOutcome) -> str:
    """SHA-256 of the iteration's ``(error_m, cov_semi_major_m)`` tuples.

    Used to certify AC-2 determinism — two runs of the same iteration
    with the same iteration_seed must produce the same hash.
    """
    h = hashlib.sha256()
    h.update(f"{iteration.iteration_id}\n{iteration.iteration_seed}\n".encode("ascii"))
    for s in iteration.samples:
        h.update(f"{s.error_m!r}|{s.cov_semi_major_m!r}\n".encode("ascii"))
    return h.hexdigest()


def determinism_fingerprint(report: MonteCarloReport) -> str:
    """One-shot fingerprint of an entire MC run — for AC-2 cross-run comparison."""
    h = hashlib.sha256()
    h.update(f"master_seed={report.master_seed}\n".encode("ascii"))
    for it in report.iterations:
        h.update(f"{iteration_hash(it)}\n".encode("ascii"))
    return h.hexdigest()


def evaluate(
    iterations: Sequence[IterationOutcome],
    *,
    master_seed: int,
    min_iteration_count: int = MIN_ITERATION_COUNT,
    envelope_ratio_budget: float = ENVELOPE_RATIO_BUDGET,
    envelope_multiplier: float = ENVELOPE_MULTIPLIER,
) -> MonteCarloReport:
    """Compute the AC-1 + AC-3 verdict.

    AC-2 (determinism) is a scenario-level check: the scenario calls
    this twice with the same master_seed and compares
    ``determinism_fingerprint(report1) == determinism_fingerprint(report2)``.
    """
    total = 0
    covered = 0
    for it in iterations:
        for s in it.samples:
            total += 1
            if s.error_m <= envelope_multiplier * s.cov_semi_major_m:
                covered += 1
    ratio: float | None
    if total == 0:
        ratio = None
    else:
        ratio = covered / total
    return MonteCarloReport(
        iterations=tuple(iterations),
        master_seed=master_seed,
        iteration_count=len(iterations),
        total_samples=total,
        covered_samples=covered,
        envelope_ratio=ratio,
        min_iteration_count=min_iteration_count,
        envelope_ratio_budget=envelope_ratio_budget,
    )


def write_csv_evidence(out_path: Path, report: MonteCarloReport) -> Path:
    """Aggregate-summary CSV (one row per run)."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(
            [
                "master_seed",
                "iteration_count",
                "min_iteration_count",
                "total_samples",
                "covered_samples",
                "envelope_ratio",
                "envelope_ratio_budget",
                "ac1_iteration_count_passes",
                "ac3_envelope_passes",
                "passes",
                "fingerprint_sha256",
            ]
        )
        writer.writerow(
            [
                report.master_seed,
                report.iteration_count,
                report.min_iteration_count,
                report.total_samples,
                report.covered_samples,
                "" if report.envelope_ratio is None else f"{report.envelope_ratio:.6f}",
                f"{report.envelope_ratio_budget:.6f}",
                "true" if report.passes_iteration_count else "false",
                "true" if report.passes_envelope else "false",
                "true" if report.passes else "false",
                determinism_fingerprint(report),
            ]
        )
    return out_path


def write_per_iteration_csv(out_path: Path, report: MonteCarloReport) -> Path:
    """One row per iteration — used during AC-3 envelope-breach investigation."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(
            [
                "iteration_id",
                "iteration_seed",
                "frame_count",
                "covered_count",
                "envelope_ratio",
                "iteration_hash_sha256",
            ]
        )
        for it in report.iterations:
            covered = sum(
                1
                for s in it.samples
                if s.error_m <= ENVELOPE_MULTIPLIER * s.cov_semi_major_m
            )
            ratio = (covered / it.frame_count) if it.frame_count else None
            writer.writerow(
                [
                    it.iteration_id,
                    it.iteration_seed,
                    it.frame_count,
                    covered,
                    "" if ratio is None else f"{ratio:.6f}",
                    iteration_hash(it),
                ]
            )
    return out_path