research: Dütsch analyzer — horizon-limited coverage/events, Parseval HF weights

Coverage and no_donor/eq_only totals now stop at the horizon; HF energy
uses one-sided Parseval weights (interior bins x2, DC/Nyquist x1). Focused
regressions for both. run.py, src and thresholds unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

tests/unit/test_duetsch_protocol.py

@@ -108,3 +108,45 @@ def test_generated_config_passes_the_real_loader(tmp_path):
     assert cfg.collision == "MRT" and cfg.streaming == "double_buffer" and not cfg.ddf_shifting
     assert cfg.velocity == 0.0 and cfg.viscosity == pytest.approx(u_max * 20 / 100)
     assert cfg.y_wall_bc == "bounce_back"
+
+
+def _analyze():
+    import sys
+    sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "research/duetsch_re100_kc5"))
+    try:
+        import analyze
+    finally:
+        sys.path.pop(0)
+    return analyze
+
+
+def test_hf_energy_is_parseval_weighted():
+    a = _analyze()
+    n = 360
+    t = np.arange(n)
+    x = 1.0 + np.cos(2 * np.pi * 5 * t / n) + 2.0 * np.cos(2 * np.pi * 30 * t / n) + 0.5 * np.cos(np.pi * t)
+    e = a.one_sided_energy(np.fft.rfft(x), n) / n
+    assert e.sum() == pytest.approx(np.sum(x ** 2))               # Parseval
+    expected = (2.0 ** 2 / 2 + 0.5 ** 2) / np.mean(x ** 2)       # bins >= 21 (30 and Nyquist)
+    assert e[21:].sum() / e.sum() == pytest.approx(expected)
+
+
+def test_horizon_excludes_future_coverage_and_events(monkeypatch):
+    a = _analyze()
+    T, horizon = 360, 4
+    steps = round((0.25 + 6) * T)                                 # cycles 1..6 recorded
+    diag = np.zeros((steps, 15))
+    diag[round((0.25 + horizon) * T):, 13] = 1.0                  # synthetic future no_donor
+    rows = [(s, p.cycle(s, T), 180.0, 0.0, 0.0, 0.1, 0.0, 1.0) for s in range(steps)
+            if p.bin_phase(s, T) is not None and p.cycle(s, T) > p.DISCARD]
+    case = dict(path="c", D=20, T=T, box=10, n=200, u_max=.04, nu=.008, tau=.524, A=1.0,
+                steps=steps, cycles=6, F_ref=1.0, force=np.zeros((steps, 2)), diag=diag,
+                samples=np.asarray(rows, float))
+    monkeypatch.setattr(a, "load", lambda path: case)
+    out = []
+    monkeypatch.setattr("builtins.print", lambda text: out.append(text))
+    a.main(["c", "--horizon", str(horizon)])
+    import json
+    entry = json.loads(out[0])["cases"][0]
+    assert sorted(int(k) for k in entry["coverage"]) == [3, 4]   # nothing from cycles 5, 6
+    assert entry["no_donor"] == 0.0