from __future__ import annotations

from pathlib import Path

from cosma_log_analyzer.ingest import (
    KNOWN_TOPICS,
    TOPIC_BATTERY,
    TOPIC_IMU,
    TOPIC_USBL,
    iter_mcap_messages,
    load_csv_nav,
    load_mcap,
)


def test_load_mcap_returns_dataframes(fake_mcap: Path) -> None:
    data = load_mcap(fake_mcap)
    assert set(data.keys()) == set(KNOWN_TOPICS)
    imu = data[TOPIC_IMU]
    usbl = data[TOPIC_USBL]
    bat = data[TOPIC_BATTERY]
    assert not imu.empty
    assert not usbl.empty
    assert not bat.empty
    # IMU ~50 Hz over 60s minus a 3s gap => ~2850 samples
    assert 2700 < len(imu) < 3000
    # USBL 2Hz over 60s => ~121 samples
    assert 115 < len(usbl) < 130
    # Battery 1Hz over 60s => 61 samples
    assert len(bat) == 61
    assert {"ts", "ax", "ay", "az", "gx", "gy", "gz"}.issubset(imu.columns)
    assert {"ts", "distance_m", "snr_db"}.issubset(usbl.columns)
    assert {"ts", "voltage_v"}.issubset(bat.columns)


def test_load_mcap_sorted_by_ts(fake_mcap: Path) -> None:
    data = load_mcap(fake_mcap)
    for df in data.values():
        assert df["ts"].is_monotonic_increasing


def test_iter_mcap_messages_filter(fake_mcap: Path) -> None:
    msgs = list(iter_mcap_messages(fake_mcap, topics=[TOPIC_BATTERY]))
    assert len(msgs) == 61
    assert all(t == TOPIC_BATTERY for t, _, _ in msgs)


def test_load_csv_nav_missing_file(tmp_path: Path) -> None:
    df = load_csv_nav(tmp_path / "nope.csv")
    assert df.empty


def test_load_csv_nav_parses(tmp_path: Path) -> None:
    p = tmp_path / "nav.csv"
    p.write_text("ts,lat,lon\n1.0,48.0,2.0\n2.0,48.1,2.1\n")
    df = load_csv_nav(p)
    assert len(df) == 2
    assert df["ts"].is_monotonic_increasing