Python code to extract HDF5 chunk locations and add them to Zarr metadata.

h5-to-zarr.py

# Requirements:
# HDF5 library version 1.10.5 or later
# h5py version 3.0 or later
# pip install git+https://github.com/HDFGroup/zarr-python.git@hdf5

import logging
from urllib.parse import urlparse, urlunparse
import numpy as np
import h5py
import zarr
from zarr.storage import FileChunkStore
from zarr.meta import encode_fill_value
from numcodecs import Zlib
import fsspec


lggr = logging.getLogger('h5-to-zarr')
lggr.addHandler(logging.NullHandler())


class Hdf5ToZarr:
    """Translate the content of one HDF5 file into Zarr metadata.

    HDF5 groups become Zarr groups. HDF5 datasets become Zarr arrays. Zarr array
    chunks remain in the HDF5 file.

    Parameters
    ----------
    h5f : file-like or str
        Input HDF5 file as a string or file-like Python object.
    store : MutableMapping
        Zarr store.
    xarray : bool, optional
        Produce atributes required by the `xarray <http://xarray.pydata.org>`_
        package to correctly identify dimensions (HDF5 dimension scales) of a
        Zarr array. Default is ``False``.
    """

    def __init__(self, h5f, store, xarray=False):
        # Open HDF5 file in read mode...
        lggr.debug(f'HDF5 file: {h5f}')
        lggr.debug(f'Zarr store: {store}')
        lggr.debug(f'xarray: {xarray}')
        self._h5f = h5py.File(h5f, mode='r')
        self._xr = xarray

        # Create Zarr store's root group...
        self._zroot = zarr.group(store=store, overwrite=True)

        # Figure out HDF5 file's URI...
        if hasattr(h5f, 'name'):
            self._uri = h5f.name
        elif hasattr(h5f, 'url'):
            parts = urlparse(h5f.url())
            self._uri = urlunparse(parts[:3] + ('',) * 3)
        else:
            self._uri = None
        lggr.debug(f'Source URI: {self._uri}')

    def translate(self):
        """Translate content of one HDF5 file into Zarr storage format.

        No data is copied out of the HDF5 file.
        """
        lggr.debug('Translation begins')
        self.transfer_attrs(self._h5f, self._zroot)
        self._h5f.visititems(self.translator)

    def transfer_attrs(self, h5obj, zobj):
        """Transfer attributes from an HDF5 object to its equivalent Zarr object.

        Parameters
        ----------
        h5obj : h5py.Group or h5py.Dataset
            An HDF5 group or dataset.
        zobj : zarr.hierarchy.Group or zarr.core.Array
            An equivalent Zarr group or array to the HDF5 group or dataset with
            attributes.
        """
        for n, v in h5obj.attrs.items():
            if n in ('REFERENCE_LIST', 'DIMENSION_LIST'):
                    continue

            # Fix some attribute values to avoid JSON encoding exceptions...
            if isinstance(v, bytes):
                v = v.decode('utf-8')
            elif isinstance(v, (np.ndarray, np.number)):
                if n == '_FillValue':
                    v = encode_fill_value(v, v.dtype)
                elif v.size == 1:
                    v = v.flatten()[0].tolist()
                else:
                    v = v.tolist()
            if self._xr and v == 'DIMENSION_SCALE':
                continue
            try:
                zobj.attrs[n] = v
            except TypeError:
                print(f'Caught TypeError: {n}@{h5obj.name} = {v} ({type(v)})')

    def translator(self, name, h5obj):
        """Produce Zarr metadata for all groups and datasets in the HDF5 file.
        """
        if isinstance(h5obj, h5py.Dataset):
            lggr.debug(f'Dataset: {h5obj.name}')
            if h5obj.id.get_create_plist().get_layout() == h5py.h5d.COMPACT:
                RuntimeError(
                    f'Compact HDF5 datasets not yet supported: <{h5obj.name} '
                    f'{h5obj.shape} {h5obj.dtype} {h5obj.nbytes} bytes>')
                return
            if (h5obj.scaleoffset or h5obj.fletcher32 or h5obj.shuffle or
                    h5obj.compression in ('szip', 'lzf')):
                raise RuntimeError(
                    f'{h5obj.name} uses unsupported HDF5 filters')
            if h5obj.compression == 'gzip':
                compression = Zlib(level=h5obj.compression_opts)
            else:
                compression = None

            # Get storage info of this HDF5 dataset...
            cinfo = self.storage_info(h5obj)
            if self._xr and h5py.h5ds.is_scale(h5obj.id) and not cinfo:
                return

            # Create a Zarr array equivalent to this HDF5 dataset...
            za = self._zroot.create_dataset(h5obj.name, shape=h5obj.shape,
                                            dtype=h5obj.dtype,
                                            chunks=h5obj.chunks or False,
                                            fill_value=h5obj.fillvalue,
                                            compression=compression,
                                            overwrite=True)
            lggr.debug(f'Created Zarr array: {za}')
            self.transfer_attrs(h5obj, za)

            if self._xr:
                # Do this for xarray...
                adims = self._get_array_dims(h5obj)
                za.attrs['_ARRAY_DIMENSIONS'] = adims
                lggr.debug(f'_ARRAY_DIMENSIONS = {adims}')

            # Store chunk location metadata...
            if cinfo:
                cinfo['source'] = {'uri': self._uri,
                                   'array_name': h5obj.name}
                FileChunkStore.chunks_info(za, cinfo)

        elif isinstance(h5obj, h5py.Group):
            lggr.debug(f'Group: {h5obj.name}')
            zgrp = self._zroot.create_group(h5obj.name)
            self.transfer_attrs(h5obj, zgrp)

    def _get_array_dims(self, dset):
        """Get a list of dimension scale names attached to input HDF5 dataset.

        This is required by the xarray package to work with Zarr arrays. Only
        one dimension scale per dataset dimension is allowed. If dataset is
        dimension scale, it will be considered as the dimension to itself.

        Parameters
        ----------
        dset : h5py.Dataset
            HDF5 dataset.

        Returns
        -------
        list
            List with HDF5 path names of dimension scales attached to input
            dataset.
        """
        dims = list()
        rank = len(dset.shape)
        if rank:
            for n in range(rank):
                num_scales = len(dset.dims[n])
                if num_scales == 1:
                    dims.append(dset.dims[n][0].name[1:])
                elif h5py.h5ds.is_scale(dset.id):
                    dims.append(dset.name[1:])
                elif num_scales > 1:
                    raise RuntimeError(
                        f'{dset.name}: {len(dset.dims[n])} '
                        f'dimension scales attached to dimension #{n}')
        return dims

    def storage_info(self, dset):
        """Get storage information of an HDF5 dataset in the HDF5 file.

        Storage information consists of file offset and size (length) for every
        chunk of the HDF5 dataset.

        Parameters
        ----------
        dset : h5py.Dataset
            HDF5 dataset for which to collect storage information.

        Returns
        -------
        dict
            HDF5 dataset storage information. Dict keys are chunk array offsets
            as tuples. Dict values are pairs with chunk file offset and size
            integers.
        """
        # Empty (null) dataset...
        if dset.shape is None:
            return dict()

        dsid = dset.id
        if dset.chunks is None:
            # Contiguous dataset...
            if dsid.get_offset() is None:
                # No data ever written...
                return dict()
            else:
                key = (0,) * (len(dset.shape) or 1)
                return {key: {'offset': dsid.get_offset(),
                              'size': dsid.get_storage_size()}}
        else:
            # Chunked dataset...
            num_chunks = dsid.get_num_chunks()
            if num_chunks == 0:
                # No data ever written...
                return dict()

            # Go over all the dataset chunks...
            stinfo = dict()
            chunk_size = dset.chunks
            for index in range(num_chunks):
                blob = dsid.get_chunk_info(index)
                key = tuple(
                    [a // b for a, b in zip(blob.chunk_offset, chunk_size)])
                stinfo[key] = {'offset': blob.byte_offset,
                               'size': blob.size}
            return stinfo


if __name__ == '__main__':
    lggr.setLevel(logging.DEBUG)
    lggr_handler = logging.StreamHandler()
    lggr_handler.setFormatter(logging.Formatter(
        '%(levelname)s:%(name)s:%(funcName)s:%(message)s'))
    lggr.addHandler(lggr_handler)

    with fsspec.open('s3://pangeo-data-uswest2/esip/adcirc/adcirc_01d.nc',
                     mode='rb', anon=False, requester_pays=True,
                     default_fill_cache=False) as f:
        store = zarr.DirectoryStore('../adcirc_01d.nc.chunkstore')
        h5chunks = Hdf5ToZarr(f, store, xarray=True)
        h5chunks.translate()

# Consolidate Zarr metadata...
lggr.info('Consolidating Zarr dataset metadata')
zarr.convenience.consolidate_metadata(store)
lggr.info('Done')

Thank you so much for the help, I will try that.

Building it from master didn't help :( and the version is still 2.10
https://github.com/h5py/h5py/blob/master/setup.py#L30
still getting the same exact error

What is your HDF5 library's version?

`In [1]: import h5py

In [2]: h5py.h5.get_libversion()
Out[2]: (1, 10, 4)`

I will try install 1.10.5but I thoughtget_num_chunksnot implemented in the version2.10``

Installing h5py from the master branch is what matters, don't worry about the version number. Try installing HDF5-1.10.6 if you can since it's the latest official 1.10 release.

I have implemented fsspec/filesystem_spec#464 to use the files generated by this, at the filesystem layer rather than in zarr (i.e., you could use the offset reference idea for other uses too).

Here's a gist that demonstrates Cloud-performant access, reading both the Zarr dataset and the NetCDF4/HDF5 file in comparable times.

You can run this test yourself on the Pangeo AWS binder (it take about 5 minutes for the cluster to spin up at the beginning):

Hello @rsignell-usgs. Thank you very much for sharing the binder. It is great to be able too read chunked netcdf file as its zarr.
I tried the pangeo binder, but at the line
cluster = gateway.new_cluster()
I get following err;
GatewayClusterError: Cluster 'prod.1716d3af703f48bbb8c7428b13823e65' failed to start, see logs for more information
Sorry, I could not find the logs so I couldn't fix by myself.
I skipped the usage of Dask Gateway cluster, I shortened the computation as
#max_var = ds['zeta'].max(dim='time')
max_var = ds['zeta'].sel(node=slice(0,425919)).max(dim='time').compute()
and I could verify so it is ok for me.
If I should report this somewhere else (pangeo Gitter?), plz let me know

@ajelenak. Hello, I try to use your code, and after by passing the initial errors (use of the modified zarr package etc ), I get the following error :
module 'zarr' has no attribute 'DirectoryStore'
Thanks in advance

@ajelenak. Hello, I try to use your code, and after by passing the initial errors (use of the modified zarr package etc ), I get the following error : module 'zarr' has no attribute 'DirectoryStore' Thanks in advance

It was solved, re-installation of git+https://github.com/HDFGroup/zarr-python.git@hdf5

@Haris-auth Glad that you finally made it to work. This idea/code now lives in the kerchunk package. It also enables using official zarr package releases.