CUDA: C++17 cleanup of ClusterFinderCUDA

- std::array for per-slot state; std::exchange in BatchView::steal
- [[nodiscard]]/noexcept on the public API and release path
- cuda_check moved to aare::cuda::detail (was reserved __cuda_check)
- _v type traits; drop redundant std::move and `using namespace aare`
- Add pytest for BatchView slot release

python/src/bind_ClusterFinderCUDA.hpp

@@ -14,8 +14,6 @@
 namespace py = pybind11;
 using pd_type = double;
 
-using namespace aare;
-
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 
@@ -170,7 +168,7 @@ sqrt(max(sum2/n - mean^2, 0)). Counterpart to `noise` for the device pedestal.)"
         .def(
             "steal_clusters",
             [](CF &self, bool realloc_same_capacity) {
-                return std::move(self.steal_clusters(realloc_same_capacity));
+                return self.steal_clusters(realloc_same_capacity);
             },
             py::arg("realloc_same_capacity") = true)
 

python/src/bind_ClusterFinderCUDAGraph.hpp

@@ -13,8 +13,6 @@
 namespace py = pybind11;
 using pd_type = double;
 
-using namespace aare;
-
 #pragma GCC diagnostic push
 #pragma GCC diagnostic ignored "-Wunused-parameter"
 
@@ -65,7 +63,7 @@ void define_ClusterFinderCUDAGraph(py::module &m, const std::string &typestr) {
         .def(
             "steal_clusters",
             [](CF &self, bool realloc_same_capacity) {
-                return std::move(self.steal_clusters(realloc_same_capacity));
+                return self.steal_clusters(realloc_same_capacity);
             },
             py::arg("realloc_same_capacity") = true)
 

python/tests/test_ClusterFinderCUDA.py

@@ -0,0 +1,80 @@
+# SPDX-License-Identifier: MPL-2.0
+import numpy as np
+import pytest
+
+from aare import ClusterFinderCUDA, _cuda_available, find_cluster_views_batched_iter
+
+pytestmark = pytest.mark.skipif(
+    not _cuda_available(), reason="aare built without AARE_CUDA")
+
+SHAPE = (32, 32)
+BATCH = 4
+# Pixel that gets a photon in every data frame, away from the border.
+HIT = (16, 16)
+
+
+@pytest.fixture
+def cf():
+    cf = ClusterFinderCUDA(SHAPE, n_streams=2, min_pedestal_samples=10)
+    rng = np.random.default_rng(42)
+    for _ in range(20):
+        cf.push_pedestal_frame(
+            rng.normal(1000, 5, SHAPE).astype(np.uint16))
+    return cf
+
+
+@pytest.fixture
+def frames():
+    rng = np.random.default_rng(7)
+    f = rng.normal(1000, 5, (BATCH, *SHAPE)).astype(np.uint16)
+    f[:, HIT[0], HIT[1]] = 3000
+    return f
+
+
+def test_collect_view_exposes_batch(cf, frames):
+    tok = cf.submit_batch(frames, first_frame=100)
+    with cf.collect_view(tok) as v:
+        assert v.valid
+        assert v.n_frames == BATCH
+        assert v.first_frame == 100
+        assert all(v.count(i) >= 1 for i in range(BATCH))
+    assert not v.valid
+
+
+def test_released_view_frees_its_slot(cf, frames):
+    # Cycles both slots twice. pybind11 move-constructs every returned
+    # BatchView, so a move that drops the slot index leaves the slot held
+    # forever and the third submit_batch() throws.
+    for i in range(2 * 2):
+        tok = cf.submit_batch(frames, first_frame=i * BATCH)
+        v = cf.collect_view(tok)
+        v.release()
+        assert not v.valid
+
+
+def test_dropped_view_frees_its_slot(cf, frames):
+    # The destructor releases, so a view that is never bound is harmless.
+    for i in range(2 * 2):
+        tok = cf.submit_batch(frames, first_frame=i * BATCH)
+        cf.collect_view(tok)
+
+
+def test_held_view_blocks_its_slot(cf, frames):
+    held = cf.collect_view(cf.submit_batch(frames))
+    # The other slot is still free.
+    with cf.collect_view(cf.submit_batch(frames)):
+        pass
+    with pytest.raises(RuntimeError, match="held by a BatchView"):
+        cf.submit_batch(frames)
+    held.release()
+    with cf.collect_view(cf.submit_batch(frames)):
+        pass
+
+
+def test_views_batched_iter_pipelines_all_chunks(cf, frames):
+    data = np.concatenate([frames] * 3)
+    seen = []
+    for v in find_cluster_views_batched_iter(cf, data, first_frame=10,
+                                             chunk=BATCH):
+        seen.append((v.first_frame, v.n_frames))
+    assert seen == [(10, BATCH), (10 + BATCH, BATCH), (10 + 2 * BATCH, BATCH)]