feat(obs): unified zero_obs control and time-normalised readback

- Replace split zero_force_segment / zero_sensor_segment with unified
  zero_obs_async() — a single memset covers all three obs segments
  (force, torque, sensor), resetting the step accumulator.
- Add _obs_accum_steps counter so read_*(normalize=True) returns the
  physically meaningful per-step average for all telemetry fields.
- Sensor now always applies area-normalisation internally; the normalize
  parameter only controls the additional time-normalisation step.
- run() gains zero_obs=True parameter (default) to control reset-on-step.
- 7 new integration tests covering accumulation, zeroing, and normalise.
- Fix bug in test_sensor_accuracy.py (undefined loop variable i).
- Bump version to 0.4.0 for the API change.

Co-authored-by: Cursor <cursoragent@cursor.com>

tests/validation/run_kan99b_rotating_cylinder.py

@@ -314,7 +314,7 @@ def _run_one(
             stream.synchronize()
             sim.bodies.download_obs_full_async(stream)
             stream.synchronize()
-            force = sim.bodies.read_force(0)
+            force = sim.bodies.read_force(0, normalize=False)
             fx = float(force[0])
             fy = float(force[1])
             if not np.isfinite(fx) or not np.isfinite(fy):

tests/validation/run_sah04_st_matrix.py

@@ -321,7 +321,7 @@ def run_one_simulation(
             stream.synchronize()
             sim.bodies.download_obs_full_async(stream)
             stream.synchronize()
-            fvec = sim.bodies.read_force(0)
+            fvec = sim.bodies.read_force(0, normalize=False)
             lift = float(fvec[1])
             drag = float(fvec[0])
             if not np.isfinite(lift) or not np.isfinite(drag):

tests/validation/test_sensor_accuracy.py

@@ -65,10 +65,8 @@ def test_sensor_accuracy() -> dict:
     # Get macroscopic field after one more step (with sensor accumulation)
     import pycuda.driver as cuda
     stream = cuda.Stream()
-    sim.bodies.zero_sensor_segment_async(stream)
-    sim.stepper.step(1, action_gpu=sim.bodies.action_gpu,
-                     obs_gpu=sim.bodies.obs_gpu, stream=stream)
-    stream.synchronize()
+    sim.run(1, zero_obs=True, upload_act=False, sync_obs=True, stream=stream)
+    # stream.synchronize() is called inside run()
 
     macro = sim.get_macroscopic()
     ux = macro["ux"]
@@ -76,7 +74,8 @@ def test_sensor_accuracy() -> dict:
 
     results = {}
     all_pass = True
-    for sid in sensor_ids:
+    for idx, sid in enumerate(sensor_ids):
+        pos = positions[idx]
         cells_arr, _ = sim.bodies.get(sid).get_sensor_list(
             sim.lbm_cfg.nx, sim.lbm_cfg.ny
         )
@@ -97,7 +96,7 @@ def test_sensor_accuracy() -> dict:
         if not passed:
             all_pass = False
 
-        results[f"sensor_{sid}_pos{positions[i]}"] = {
+        results[f"sensor_{sid}_pos{pos}"] = {
             "sensor_reading": [sensor_reading_x, sensor_reading_y],
             "manual_average": [sensor_ux_mean, sensor_uy_mean],
             "diff": [float(diff_ux), float(diff_uy)],
@@ -106,7 +105,7 @@ def test_sensor_accuracy() -> dict:
         }
         status = "PASS" if passed else "FAIL"
         print(
-            f"  Sensor {sid} @ {positions[sid]}: "
+            f"  Sensor {sid} @ {pos}: "
             f"reading=({sensor_reading_x:.8f},{sensor_reading_y:.8f}) "
             f"manual=({sensor_ux_mean:.8f},{sensor_uy_mean:.8f}) "
             f"diff=({diff_ux:.2e},{diff_uy:.2e}) "