Merge branch 'feat/p2-python-bindings' into feat/p2-perf-coverage

# Conflicts:
#	CHANGELOG.md
#	README.md

README.md

@@ -73,8 +73,9 @@ breaking change, are in [CHANGELOG.md](CHANGELOG.md).
 - Default fusion weights are now the measured 0.4 / 0.6 (v2.5.0). Re-ranking had
   been discarding the retrieval score, costing the Markdown backend 40.6pp of
   Hit@1; fixed in v2.6.0.
-- Selection reads decode only the chunks they touch (a 64×64 window: 105 ms to
-  0.39 ms), and full reads are 1.2–1.9× faster (v2.5.0).
+- Selection reads whose bounding box covers at most half the dataset decode
+  only the chunks they touch (a 64×64 window: 105 ms to 0.39 ms), and full
+  reads are 1.2–1.9× faster (v2.5.0).
 
 **Memory**
 - A loaded store holds ~30% less (embeddings stored once, v2.6.0), and the
@@ -432,6 +433,54 @@ b.write("groups.h5")?;
 A group holds at most 65 535 links; more is an error, as is a link over
 65 515 bytes (a very long soft-link target) in a group of more than 8 links.
 
+### Python
+
+`crates/clawhdf5-py` is a Python package (PyO3 + numpy) that reads HDF5 with
+an h5py-shaped API and no libhdf5. It is not on PyPI; build it with
+[maturin](https://www.maturin.rs) into a virtualenv:
+
+```bash
+python -m venv .venv && . .venv/bin/activate
+pip install maturin numpy
+maturin develop --release -m crates/clawhdf5-py/Cargo.toml
+python -c "import clawhdf5; print(clawhdf5.__version__)"
+```
+
+```python
+import numpy as np
+import clawhdf5
+
+with clawhdf5.File("data.h5", "r") as f:
+    print(list(f.keys()))              # sorted member names, like h5py
+    ds = f["group/temperatures"]       # relative or absolute ("/group/...") paths
+    print(ds.shape, ds.dtype)          # dtype is the numpy dtype h5py reports
+    block = ds[100:200, ::4]           # a small selection reads only its chunks
+    row = ds[-1]                       # integers drop the axis
+    picked = ds[[1, 5, 9], :]          # one increasing index list per key
+    units = ds.attrs["units"]          # attributes come back as h5py returns them
+    everything = np.asarray(ds)
+
+    records = f["table"]               # compound -> numpy structured array
+    ids = records["id"]                # one field
+```
+
+Reads cover integers and IEEE floats of every width in either byte order,
+`bool`, enums, complex, fixed and variable-length strings, variable-length
+sequences, opaque, HDF5 array types and compounds; other types (references,
+bitfields, ...) raise `TypeError` instead of returning guessed data. Keys
+follow h5py (negative steps, `None` and boolean masks are refused). The
+read itself runs with the GIL released, so Python threads read in parallel.
+A selection whose bounding box covers at most half the dataset decodes only
+the chunks (or contiguous rows) that box overlaps; a larger one — including
+a strided slice across the whole dataset — decodes the whole dataset, as
+do datasets that are compact, virtual, unwritten, or chunked with a
+non-default fill value (`docs/known-issues.md`). An index list is read one
+group of neighbouring chunks at a time.
+Writing (`File(path, "w")`, `create_dataset`, `create_group`, `attrs[...] =`)
+covers `float64`, `float32`, `int64`, `int32` and `uint8` arrays. The tests
+in `crates/clawhdf5-py/tests` compare every read with h5py; run them with
+`pip install pytest h5py && pytest crates/clawhdf5-py/tests`.
+
 ### Agent Memory
 
 ```rust