b2h5py 0.4.0

Usage

This optimized access works for slices with step 1 on Blosc2-compressed datasets using the native byte order. It is enabled by monkey-patching the h5py.Dataset class to extend the slicing operation. The easiest way to do this is:

import b2h5py.auto

After that, optimization will be attempted for any slicing of a dataset (of the form dataset[...] or dataset.__getitem__(...)). If the optimization is not possible in a particular case, normal h5py slicing code will be used (which performs HDF5 filter-based access, backed by hdf5plugin to support Blosc2).

You may instead just import b2h5py and explicitly enable the optimization globally by calling b2h5py.enable_fast_slicing(), and disable it again with b2h5py.disable_fast_slicing(). You may also enable it temporarily by using a context manager:

with b2h5py.fast_slicing():
    # ... code that will use Blosc2 optimized slicing ...

Finally, you may explicitly enable optimizations for a given h5py dataset by wrapping it in a B2Dataset instance:

b2dset = b2h5py.B2Dataset(dset)
# ... slicing ``b2dset`` will use Blosc2 optimization ...

Please note that, for the moment, plain iteration in B2Dataset instances is not optimized (as it falls back to plain Dataset slicing). This does not affect the other approaches further above. Instead of for row in b2dset: loops, you may prefer to use slicing like:

for i in range(len(b2dset)):
    # ... operate with ``b2dset[i]`` or ``b2dset[i, ...]`` ...

We recommend that you test which approach works better for your datasets. This limitation may be fixed in the future.

Building

Just install PyPA build (e.g. pip install build), enter the source code directory and run pyproject-build to get a source tarball and a wheel under the dist directory.

Installing

To install as a wheel from PyPI, run pip install b2h5py.

You may also install the wheel that you built in the previous section, or enter the source code directory and run pip install . from there.

Running tests

If you have installed b2h5py, just run python -m unittest discover b2h5py.tests.

Otherwise, just enter its source code directory and run python -m unittest.

You can also run the h5py tests with the patched Dataset class to check that patching does not break anything. You may install the h5py-test extra (e.g. pip install b2h5py[h5py-test] and run python -m b2h5py.tests.test_patched_h5py.