The READMEs said ds[...] reads only the selected elements, and the
facade's read_selection docs that only intersecting chunks are
decompressed. The bounding-box path runs only when the box covers at
most half the dataset; larger boxes (any strided slice across the
dataset), compact, virtual and unwritten datasets and chunked ones with
a non-default fill value decode the whole dataset. The READMEs, the
facade and format docs, the bindings' docstrings and known-issues now
say so, and how index lists are read.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
ds[np.array(1)] went down the index-list path, where tolist() returns a
scalar and extracting a list of indices raised a confusing TypeError.
h5py treats it as an integer index; so do we now. The h5py comparison
keys include 0-d arrays (signed and unsigned) on each axis; they failed
before.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
Each run of consecutive indices was its own uncached hyperslab read, so
a list over a compressed chunked dataset decoded the same chunk once per
run (d[range(0, 200000, 40)] over 20 gzip chunks: 8 s, h5py 0.014 s).
Plan::reads now groups the indices — a group ends only where a whole
chunk holds no selected index, or, unchunked, at a gap over 64 KiB — and
the selected rows are gathered from each group's block in Rust. Now
3.8 ms (h5py 4.1 ms, release, tank). The new test (1-D, 2-D and
contiguous, compared with h5py, 2 s bound) took 5.8 s before.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
np.concatenate copies structured dtypes field by field into np.empty, so
the padding of ds[[0, 3, 6]] held process memory. The runs' bytes are
joined in Rust, whole elements at a time, before anything becomes numpy:
the padding is the file's bytes (h5py's) and the result is still a view
of the Rust buffer. The h5py comparisons now compare every byte of
structured values; the new test failed on the padding before.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
ds[key] read the whole dataset and sliced it in numpy, and knew six
dtypes. Keys (ints, positive-step slices, Ellipsis, one increasing index
list, compound field names) now map onto hyperslab selections, and the
facade's read_selection bytes become the numpy buffer without a copy
(PyArray::from_vec viewed as the dtype). dtype mapping follows h5py for
all integer/IEEE float widths and byte orders, bool, enum, complex, fixed
and variable-length strings, vlen sequences, opaque, array types and
(nested, padded) compounds; anything it cannot describe exactly is a
TypeError. Attributes return what h5py returns; groups and files gain
the rest of the h5py mapping interface. Reads run under py.detach.
tests/test_read_vs_h5py.py compares >500 reads with h5py 3.16 on an
h5py-written file, checks errors match, that a damaged chunk outside the
selection is never touched, and 8 threads reading at once.
Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]>
