clawhdf5/crates/clawhdf5-py

clawhdf5-py

Python bindings for clawhdf5 — a pure-Rust HDF5 library. The package is clawhdf5 (import clawhdf5); it needs numpy and no libhdf5.

Install

Not on PyPI yet. Build it into a virtualenv with maturin:

pip install maturin numpy
cd crates/clawhdf5-py
maturin develop --release
python -c "import clawhdf5; print(clawhdf5.__version__)"

Reading

The read API follows h5py:

import numpy as np
import clawhdf5

with clawhdf5.File("data.h5", "r") as f:
    f.keys(), f["group"].items(), "group/data" in f
    ds = f["group/data"]           # or f["/group/data"], f["group"]["data"]
    ds.shape, ds.dtype, ds.attrs["units"]
    ds[10:20, ::2]                 # a small selection reads only its chunks
    ds[-1], ds[..., 0], ds[[1, 4, 7]]
    np.asarray(ds)
    f["table"]["id"]               # a compound field

• Dataset.dtype is the numpy dtype h5py reports: integers and IEEE floats of every width in either byte order, bool, enums (with dtype.metadata['enum']), complex, S<n> fixed strings, object for variable-length strings (bytes values) and sequences (array values), V<n> opaque, array types, and compounds as structured dtypes. Other types raise TypeError.
• Keys are h5py's: integers, slices with a positive step, ..., one increasing list of integers, compound field names. Each maps onto a hyperslab selection. None, negative steps and boolean masks are refused with h5py's errors.
• What is read from the file: a selection whose bounding box covers at most half the dataset decodes only the chunks (or contiguous rows) the box overlaps. The library decodes the whole dataset for a larger box (including a strided slice such as ds[::100] across a chunked dataset), and for compact, virtual and unwritten datasets and chunked ones with a non-default fill value. An index list is read one group of neighbouring chunks at a time (a new group only past a chunk with no selected index), so each chunk is decoded once. ds[()], ds[...] and np.asarray(ds) use the file's chunk cache; other selections do not.
• The bytes the library reads become the numpy array's buffer without a copy, and the read runs with the GIL released, so threads read in parallel. A bug in the library (a Rust panic) raises clawhdf5.InternalError, a RuntimeError.
• Attributes return what h5py returns; clawhdf5.Empty stands for a null dataspace (h5py's Empty).

Remote files

A URL instead of a path reads the file where it is, through clawhdf5-remote: HTTP range requests through a block cache (1 MiB blocks, 64 MiB budget by default), fetching only the blocks a read needs. The whole read API works the same, and the GIL is released while waiting on the network.

f = clawhdf5.File("http://host/data.h5")          # default options
f = clawhdf5.File.open_url(
    "http://host/data.h5",
    block_size=256 * 1024, cache_size=128 << 20,  # the block cache
    headers={"Authorization": "Bearer ..."},      # sent to this origin only
    retries=3, timeout=30.0, max_redirects=5, max_parallel=8,
    allow_full_download=False,  # a server without Range support: refuse
    require_validator=False,    # refuse servers without ETag/Last-Modified
)
f.remote_stats   # {'requests': ..., 'bytes_fetched': ..., 'hits': ..., ...}

• The file is pinned when opened (ETag or Last-Modified, and length): if it changes on the server, reads raise OSError instead of mixing versions. Network failures are OSError too.
• Remote files are read-only.
• Schemes: the default build (no C) reads http://. https:// needs maturin develop --release --features https (rustls with ring, which compiles C); s3://, gs:// and az:// need the s3, gcs and azure features (credentials from the environment; aws-lc-rs, C).

Writing

clawhdf5.File(path, "w") with create_dataset(name, data=array, chunks=..., compression="gzip"), create_group and attrs[...] = ... writes float64, float32, int64, int32 and uint8 arrays; the file is written on close().

Editing a file in place

clawhdf5.File(path, "r+") (or "a" on an existing file) edits the file where it is, through clawhdf5's FileEditor; the file is locked until close(), and every edit is written and synced before the statement returns.

with clawhdf5.File("data.h5", "r+") as f:
    ds = f["grid"]
    ds[10:20, ::2] = 0                  # h5py keys and broadcasting
    ds[[1, 4, 7], 3] = [1.5, 2.5, 3.5]  # one index list: exact shape
    f["series"].resize((5000, 3))       # or .resize(5000, axis=0)
    f["series"].attrs["units"] = "K"
    f.attrs.create("version", 2, dtype="u1")

• Values: a numpy array is converted to the dataset's dtype as libhdf5 converts it (integers saturate at the target's limits; floats are truncated toward zero and clipped); anything else goes through numpy.asarray(value, dtype=ds.dtype), as in h5py. Writing NaN into an integer dataset raises ValueError (libhdf5 would store an arbitrary value). A few libhdf5 edge cases differ on purpose; see docs/known-issues.md.
• Shapes: ds.resize grows or shrinks chunked datasets within their maxshape, as h5py; datasets and attrs objects taken before an edit see its result.
• Attributes: numeric, bool, complex, bytes and str data of any shape. str is stored as a fixed-length UTF-8 string (h5py stores a variable-length one), so h5py reads it back as bytes.
• Not supported (NotImplementedError, nothing written): creating or deleting datasets, groups and attributes, writing compound fields by name, variable-length data, HDF5 array types, and whatever FileEditor refuses (listed in docs/known-issues.md).

Tests

pip install pytest h5py
pytest crates/clawhdf5-py/tests

tests/test_read_vs_h5py.py compares every read with h5py on a file h5py writes, opened locally and over HTTP (an in-process range server, tests/conftest.py); tests/test_remote.py checks remote reads (requests, failures, the GIL); tests/test_edit.py applies every edit through h5py and clawhdf5 to copies of a file and compares them through h5py (and h5dump, and h5rs check when CLAWHDF5_H5RS names it). scripts/ci-test.sh builds the wheel and runs these in CI.

License

MIT