feat(format): read chunked datasets indexed by a version-2 B-tree

With libver='latest', a chunked dataset with two or more unlimited dimensions
indexes its chunks with a v2 B-tree (layout v4, index type 5). Reading one
failed with "unsupported chunked layout version=4, index_type=Some(5)".

read_btree_v2_chunks decodes record types 10 (address + scaled offsets) and 11
(address, stored size, filter mask, scaled offsets). The width of the
stored-size field is taken from the record size the tree header declares
rather than re-deriving the library's formula. Scaled offsets are multiplied
back by the chunk dimensions with overflow checks.

The chunk-index dispatch existed four times (uncached, cached, sweep and
indexed readers). The three copies outside list_chunks now call it, so every
read path — and fill-value handling and partial reads — supports every index
type from one place.

h5py interop test: plain, gzip+shuffle, a 2500-chunk tree with internal nodes,
a sparse dataset with a fill value, and a strided hyperslab.

Co-Authored-By: Claude Fable 5.1 <[email protected]>

crates/clawhdf5/tests/h5py_interop_tests.rs

@@ -913,3 +913,69 @@ with h5py.File("{dst_str}", "r") as f:
         "[(1, 2.5), (3, 4.5)] ('a', 'b') [18446744073709551615, 0, 9223372036854775808] uint64"
     );
 }
+
+// ---------------------------------------------------------------------------
+// h5py writes datasets indexed by a version-2 B-tree -> clawhdf5 reads
+// ---------------------------------------------------------------------------
+
+/// With `libver='latest'`, a chunked dataset with two or more unlimited
+/// dimensions indexes its chunks with a version-2 B-tree (layout v4, index
+/// type 5). These used to fail with "unsupported chunked layout".
+#[test]
+fn h5py_btree_v2_chunk_index_clawhdf5_reads() {
+    skip_if_no_python!();
+    let dir = tempfile::tempdir().unwrap();
+    let path = dir.path().join("bt2.h5");
+    let path_str = path.display().to_string();
+    let script = format!(
+        r#"
+import h5py, numpy as np
+with h5py.File("{path_str}", "w", libver="latest") as f:
+    a = np.arange(60 * 45, dtype="<i4").reshape(60, 45)
+    f.create_dataset("plain", data=a, chunks=(7, 8), maxshape=(None, None))
+    f.create_dataset("gz", data=a, chunks=(7, 8), maxshape=(None, None), compression="gzip", shuffle=True)
+    # Enough chunks (2500) that the tree has internal nodes.
+    big = np.arange(200 * 200, dtype="<i4").reshape(200, 200)
+    f.create_dataset("deep", data=big, chunks=(4, 4), maxshape=(None, None))
+    s = f.create_dataset("sparse", shape=(30, 30), dtype="<i4", chunks=(5, 5), maxshape=(None, None), fillvalue=-9)
+    s[10:15, 20:25] = 4
+    s[29, 29] = 1
+with h5py.File("{path_str}", "r") as f:
+    print("sparse", f["sparse"][...].ravel().tolist())
+    print("slab", f["deep"][37:141:13, 5:190:31].ravel().tolist())
+"#
+    );
+    let out = run_python_output(&script);
+    let expected: std::collections::HashMap<&str, Vec<i32>> = out
+        .lines()
+        .map(|l| {
+            let (name, list) = l.split_once(' ').unwrap();
+            (name, parse_int_list(list))
+        })
+        .collect();
+
+    let file = File::open(&path).unwrap();
+    let small: Vec<i32> = (0..60 * 45).collect();
+    assert_eq!(file.dataset("plain").unwrap().read_i32().unwrap(), small);
+    assert_eq!(file.dataset("gz").unwrap().read_i32().unwrap(), small);
+    let deep: Vec<i32> = (0..200 * 200).collect();
+    assert_eq!(file.dataset("deep").unwrap().read_i32().unwrap(), deep);
+    assert_eq!(
+        file.dataset("sparse").unwrap().read_i32().unwrap(),
+        expected["sparse"]
+    );
+    // Partial read through the same index: rows 37,50,..,128 x cols 5,36,..,160.
+    let slab = clawhdf5_format::selection::Selection::Hyperslab {
+        start: vec![37, 5],
+        stride: vec![13, 31],
+        count: vec![8, 6],
+        block: vec![1, 1],
+    };
+    assert_eq!(
+        file.dataset("deep")
+            .unwrap()
+            .read_i32_selection(&slab)
+            .unwrap(),
+        expected["slab"]
+    );
+}