Example/docs closure fixes

- README: close the Manual stream control code fence before the new
  section; snippet now builds rest-equilibrium inputs (runnable as shown).
- Example: diagnostics use the known fluid-leaf masks (uncovered coarse
  minus slot-mode wall rows; fine ACTIVE minus solids) and raise on any
  non-finite population or rho <= 0 there instead of filtering by
  finiteness. No solver changes.

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

README.md

@@ -90,6 +90,7 @@ sim.run(100, stream=stream, upload_act=False, sync_obs=False)
 # Later: sync and read
 stream.synchronize()
 obs = sim.read_bodies(stream=stream)  # sync already done, just read pinned buffer
+```
 
 ### Fixed 2:1 refinement patch (experimental backend)
 
@@ -99,10 +100,12 @@ normal surface velocity (blowing/suction). It is a standalone backend, not part
 of `Simulation`. Runnable demo: `examples/fixed_patch_circle.py`.
 
 ```python
+import numpy as np
 from CelerisLab.body.geometry.circle import CircleGeometry
 from CelerisLab.body.objects import SimObject, SurfaceActuation
 from CelerisLab.config import LBMConfig
 from CelerisLab.cuda.context import CudaContext
+from CelerisLab.lbm.equilibrium import compute_feq_d2q9
 from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
 
 cfg = LBMConfig(nx=32, ny=24, viscosity=.1)
@@ -110,7 +113,10 @@ body = SimObject(0, geometry=CircleGeometry(16.0, 12.0, 2.15), center=(16.0, 12.
                  surface_actuation=SurfaceActuation(0.7, 0.85, 0.002))  # angle, half-width, speed
 with CudaContext(0) as ctx, FixedPatchStepper(cfg, (8, 6, 24, 18), context=ctx,
                                               fine_objects=[body], fine_block=(16, 20)) as run:
-    run.initialize(coarse_g, fine_g)   # (9, ny, nx) postcollision populations per level
+    rest = np.asarray(compute_feq_d2q9(1.0, 0.0, 0.0), np.float32)[:, None, None]
+    # (9, ny, nx) postcollision populations per level; non-leaf cells are discarded.
+    run.initialize(np.broadcast_to(rest, (9, *run.patch.coarse_shape)),
+                   np.broadcast_to(rest, (9, *run.patch.fine_shape)))
     run.step(128)                      # coarse steps; fine level takes two per step
     coarse_g, fine_g = run.download()  # NaN on covered, ghost and solid cells
 ```