1.x updates

tests/unit/test_race_plan.py

@@ -0,0 +1,116 @@
+"""Tests for race-plan helpers: `personal_records` and `calibrate_tl_per_km`."""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+from openrun.model import calibrate_tl_per_km, personal_records
+
+
+def _act(aid: int, dist_km: float, pace: float, when: str = "2026-01-01") -> dict:
+    """Build a single-activity dict matching `load_activities` output columns."""
+    duration_s = pace * 60.0 * dist_km
+    return {
+        "activity_id": aid,
+        "start_time_local": pd.Timestamp(when),
+        "distance_km": dist_km,
+        "pace_min_per_km": pace,
+        "moving_duration_s": duration_s,
+    }
+
+
+# ---------------------------------------------------------------------------
+# personal_records
+# ---------------------------------------------------------------------------
+
+def test_personal_records_picks_fastest_in_bin() -> None:
+    """Three 10K candidates: returned row must be the one with the lowest pace."""
+    acts = pd.DataFrame([
+        _act(1, 10.0, pace=5.0),
+        _act(2, 10.2, pace=4.5),   # winner
+        _act(3,  9.8, pace=4.8),
+    ])
+    pr = personal_records(acts, distance_bins_km=(10.0,))
+    assert len(pr) == 1
+    assert pr.loc[0, "activity_id"] == 2
+    assert pr.loc[0, "pace_min_per_km"] == 4.5
+
+
+def test_personal_records_respects_tolerance_band() -> None:
+    """±5% band on 10K = [9.5, 10.5]. An 11K activity is too far out to qualify."""
+    acts = pd.DataFrame([
+        _act(1, 10.0, pace=5.0),
+        _act(2, 11.0, pace=3.5),   # faster but outside tolerance
+    ])
+    pr = personal_records(acts, distance_bins_km=(10.0,))
+    assert pr.loc[0, "activity_id"] == 1
+
+
+def test_personal_records_skips_bins_with_no_qualifying_activity() -> None:
+    acts = pd.DataFrame([_act(1, 5.0, pace=5.0)])
+    pr = personal_records(acts, distance_bins_km=(5.0, 10.0, 21.0975))
+    assert list(pr["bin_km"]) == [5.0]
+
+
+def test_personal_records_ignores_nan_pace() -> None:
+    acts = pd.DataFrame([
+        {**_act(1, 10.0, pace=5.0), "pace_min_per_km": np.nan},  # masked by loader
+        _act(2, 10.0, pace=5.5),
+    ])
+    pr = personal_records(acts, distance_bins_km=(10.0,))
+    assert pr.loc[0, "activity_id"] == 2
+
+
+def test_personal_records_empty_input_returns_empty_frame() -> None:
+    pr = personal_records(pd.DataFrame())
+    assert pr.empty
+    assert set(pr.columns) >= {"bin_km", "activity_id", "pace_min_per_km"}
+
+
+# ---------------------------------------------------------------------------
+# calibrate_tl_per_km
+# ---------------------------------------------------------------------------
+
+def _insert_activity(conn, aid: int, distance_m: float, training_load: float,
+                     activity_type: str = "running", when: str = "2026-05-01") -> None:
+    conn.execute(
+        """INSERT INTO activities
+           (activity_id, start_time_local, activity_type, distance_m, training_load, raw, fetched_at)
+           VALUES (?, ?, ?, ?, ?, '{}', 'now')""",
+        (aid, when, activity_type, distance_m, training_load),
+    )
+
+
+def test_calibrate_tl_per_km_returns_median_iqr(tmp_conn) -> None:
+    """Median of synthetic tl/km values equals the constructed truth."""
+    # 5 runs, each 10 km, with training loads chosen so tl/km = 8, 9, 10, 11, 12.
+    for i, tl_per_km in enumerate([8.0, 9.0, 10.0, 11.0, 12.0], start=1):
+        _insert_activity(tmp_conn, aid=i, distance_m=10_000, training_load=10 * tl_per_km)
+    tmp_conn.commit()
+
+    result = calibrate_tl_per_km(tmp_conn, lookback_days=None)
+    assert result["n"] == 5
+    assert result["median"] == 10.0
+    assert result["q1"] == 9.0
+    assert result["q3"] == 11.0
+
+
+def test_calibrate_tl_per_km_excludes_short_distance(tmp_conn) -> None:
+    _insert_activity(tmp_conn, aid=1, distance_m=10_000, training_load=100)
+    _insert_activity(tmp_conn, aid=2, distance_m=500,    training_load=50)   # too short
+    tmp_conn.commit()
+    assert calibrate_tl_per_km(tmp_conn, lookback_days=None)["n"] == 1
+
+
+def test_calibrate_tl_per_km_excludes_other_activity_types(tmp_conn) -> None:
+    _insert_activity(tmp_conn, aid=1, distance_m=10_000, training_load=100, activity_type="running")
+    _insert_activity(tmp_conn, aid=2, distance_m=10_000, training_load=100, activity_type="cycling")
+    tmp_conn.commit()
+    assert calibrate_tl_per_km(tmp_conn, lookback_days=None)["n"] == 1
+
+
+def test_calibrate_tl_per_km_returns_nan_when_empty(tmp_conn) -> None:
+    result = calibrate_tl_per_km(tmp_conn, lookback_days=None)
+    assert result["n"] == 0
+    assert np.isnan(result["median"])