feat: scaffold cosma-log-analyzer with 5 deterministic rules + fake MCAP e2e test

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/test_ingest.py

@@ -0,0 +1,58 @@
+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