import csv
from pathlib import Path

import pytest

from src.config_loader import FaceLoggingSettings
from src.face_logger import FaceLogger
from src.models import BoundingBox, FaceROI, HeadPoseResult
from src.yaw_noise_report import (
    ema, load_tracks, main, median_then_ema, one_euro, percentile, summarize,
)


def test_percentile_interpolates() -> None:
    assert percentile([0.0, 10.0], 50) == pytest.approx(5.0)
    assert percentile([1.0, 2.0, 3.0], 0) == 1.0
    assert percentile([1.0, 2.0, 3.0], 100) == 3.0


def test_summarize_constant_and_alternating_series() -> None:
    constant = summarize([2.0] * 5)
    assert constant is not None and constant["sd"] == 0.0 and constant["p2p"] == 0.0
    alternating = summarize([0.0, 2.0, 0.0, 2.0])
    assert alternating is not None
    assert alternating["p2p"] == 2.0 and alternating["mean"] == 1.0
    assert summarize([1.0]) is None


def test_filters_preserve_length_and_constant_signal() -> None:
    values = [3.0] * 6
    samples = [(i * 0.05, 3.0) for i in range(6)]
    assert ema(values, 0.4) == pytest.approx(values)
    assert median_then_ema(values, 0.4) == pytest.approx(values)
    assert one_euro(samples, 1.0, 0.0) == pytest.approx(values)
    assert ema([1.0, 5.0], 1.0) == [1.0, 5.0]


def test_ema_reduces_alternating_noise() -> None:
    noisy = [0.0, 4.0] * 20
    raw_sd = summarize(noisy)["sd"]  # type: ignore[index]
    assert summarize(ema(noisy, 0.2))["sd"] < raw_sd  # type: ignore[index]


def _result(frame: int, t: float, yaw: float, raw: float | None) -> HeadPoseResult:
    roi = FaceROI(1, 10, 10, 110, 110, BoundingBox(0, 0, 200, 400), .9)
    return HeadPoseResult(1, frame, "now", t, True, True, 0.0, yaw, 0.0, 468, roi, .9,
                          raw_yaw=raw, reprojection_error=1.5, interocular_px=60.0)


def test_logger_writes_diagnostic_columns_and_report_reads_them(tmp_path: Path, capsys: pytest.CaptureFixture[str]) -> None:
    logger = FaceLogger(FaceLoggingSettings(True, True, True, 1, str(tmp_path)), "exp", "20260101_000000")
    logger.open()
    raws = [0.0, 2.0, -2.0, 1.0, -1.0, 0.5]
    logger.write_results([_result(i + 1, i * 0.1, r * 0.5, r) for i, r in enumerate(raws)])
    logger.close()
    path = next(tmp_path.glob("*face_frames.csv"))
    with path.open(encoding="utf-8-sig", newline="") as stream:
        rows = list(csv.DictReader(stream))
    assert rows[1]["raw_yaw"] == "2.000000" and rows[1]["interocular_px"] == "60.000000"
    assert rows[0]["bbox_center_x"] == "100" and rows[0]["reprojection_error"] == "1.500000"

    tracks = load_tracks(path)
    assert len(tracks[1]["raw"]) == 6
    trimmed = load_tracks(path, track_id=1, t_start=0.2)
    assert len(trimmed[1]["raw"]) == 4
    assert main([str(path), "--track-id", "1", "--csv", str(tmp_path / "out.csv")]) == 0
    assert "raw + OneEuro" in capsys.readouterr().out
    assert (tmp_path / "out.csv").is_file()


def test_report_handles_old_logs_without_raw_columns(tmp_path: Path) -> None:
    path = tmp_path / "old_face_frames.csv"
    path.write_text(
        "track_id,pose_estimated,elapsed_time_sec,yaw\n1,True,0.0,1.0\n1,True,0.1,2.0\n1,False,0.2,\n",
        encoding="utf-8-sig",
    )
    tracks = load_tracks(path)
    assert len(tracks[1]["logged"]) == 2 and tracks[1]["raw"] == []
    assert main([str(path)]) == 0
    assert main([str(tmp_path / "missing.csv")]) == 2