feat(lib): add sample_duration, truncnorm_sample (no scipy)

tests/unit/test_postprocess.py

@@ -104,3 +104,40 @@ def test_build_timestamps_total_close_to_target() -> None:
     ts = build_timestamps(log_dt, total_duration_ms=300.0)
     # Last timestamp should be roughly total - one slot
     assert abs(ts[-1] - 270) < 60  # tolerant of clipping
+
+
+from ai_mouse._postprocess import sample_duration, truncnorm_sample
+
+
+def test_truncnorm_sample_within_bounds() -> None:
+    rng = np.random.default_rng(0)
+    samples = [
+        truncnorm_sample(80.0, 30.0, 20.0, 300.0, rng) for _ in range(500)
+    ]
+    arr = np.array(samples)
+    assert arr.min() >= 20.0
+    assert arr.max() <= 300.0
+    # Mean roughly close to mu
+    assert abs(arr.mean() - 80.0) < 5.0
+
+
+def test_truncnorm_sample_far_outside_falls_back_to_clip() -> None:
+    rng = np.random.default_rng(0)
+    # mu far outside [low, high] — rejection will fail
+    v = truncnorm_sample(mu=1000.0, sigma=1.0, low=20.0, high=30.0, rng=rng)
+    assert 20.0 <= v <= 30.0
+
+
+def test_sample_duration_uses_correct_bin() -> None:
+    dist_dict = {
+        "bins": [0, 50, 100, 200, 400, 600, 800, 1200, float("inf")],
+        "params": [
+            {"mu_log": 4.0, "sigma_log": 0.01},  # bin 0: dist < 50
+            {"mu_log": 5.0, "sigma_log": 0.01},  # bin 1: 50 <= dist < 100
+            {"mu_log": 6.0, "sigma_log": 0.01},  # bin 2: 100 <= dist < 200
+        ] + [{"mu_log": 7.0, "sigma_log": 0.01}] * 5,
+    }
+    rng = np.random.default_rng(0)
+    v = sample_duration(dist_dict, 150.0, rng)
+    # exp(6) ~ 403, with tiny sigma we should land near there
+    assert 350 < v < 460