Advanced HDF5 I/O

The HDF5 storage backend supports a broad range of advanced dataset I/O options, such as, chunking and compression. Here we demonstrate how to use these features from PyNWB. Wrapping data arrays with H5DataIO ———————————————————————————

In order to customize the I/O of datasets using the HDF I/O backend we simply need to wrap our datasets using H5DataIO. Using H5DataIO allows us to keep the Container classes independent of the I/O backend while still allowing us to customize HDF5-specific I/O features.

Before we get started, lets create an NWBFile for testing so that we can add our data to it.

from datetime import datetime
from dateutil.tz import tzlocal
from pynwb import NWBFile

start_time = datetime(2017, 4, 3, 11, tzinfo=tzlocal())
create_date = datetime(2017, 4, 15, 12, tzinfo=tzlocal())

nwbfile = NWBFile(session_description='demonstrate advanced HDF5 I/O features',
                  identifier='NWB123',
                  session_start_time=start_time,
                  file_create_date=create_date)

Normally if we create a timeseries we would do

from pynwb import TimeSeries
import numpy as np

data = np.arange(100, 200, 10)
timestamps = np.arange(10)
test_ts = TimeSeries(name='test_regular_timeseries',
                     data=data,
                     unit='SIunit',
                     timestamps=timestamps)
nwbfile.add_acquisition(test_ts)

Now let’s say we want to compress the recorded data values. We now simply need to wrap our data with H5DataIO. Everything else remains the same

from hdmf.backends.hdf5.h5_utils import H5DataIO
wrapped_data = H5DataIO(data=data, compression=True)     # <----
test_ts = TimeSeries(name='test_compressed_timeseries',
                     data=wrapped_data,                  # <----
                     unit='SIunit',
                     timestamps=timestamps)
nwbfile.add_acquisition(test_ts)

This simple approach gives us access to a broad range of advanced I/O features, such as, chunking and compression. For a complete list of all available settings see H5DataIO

Chunking

By default, data arrays are stored contiguously. This means that on disk/in memory the elements of a multi-dimensional, such as, `[[1 2] [3 4]]` are actually stored in a one-dimensional buffer `1 2 3 4`. Using chunking, allows us to break up our array into chunks so that our array will be stored not in one but multiple buffers, e.g., [1 2] [3 4]. Using this approach allows optimization of data locality for I/O operations and enables the application of filters (e.g., compression) on a per-chunk basis.

Tip

For an introduction to chunking and compression in HDF5 and h5py in particular see also the online book Python and HDF5 by Andrew Collette.

To use chunking we again, simply need to wrap our dataset via H5DataIO. Using chunking then also allows to also create resizable arrays simply by defining the maxshape of the array.

data = np.arange(10000).reshape((1000, 10))
wrapped_data = H5DataIO(data=data,
                        chunks=True,          # <---- Enable chunking
                        maxshape=(None, 10)   # <---- Make the time dimension unlimited and hence resizeable
                        )
test_ts = TimeSeries(name='test_chunked_timeseries',
                     data=wrapped_data,                  # <----
                     unit='SIunit',
                     starting_time=0.0,
                     rate=10.0)
nwbfile.add_acquisition(test_ts)

Hint

By also specifying fillvalue we can define the value that should be used when reading uninitialized portions of the dataset. If no fill value has been defined, then HDF5 will use a type-appropriate default value.

Note

Chunking can help improve data read/write performance by allowing us to align chunks with common read/write operations. The following blog post provides an example http://geology.beer/2015/02/10/hdf-for-large-arrays/. for this. But you should also know that, with great power comes great responsibility! I.e., if you choose a bad chunk size e.g., too small chunks that don’t align with our read/write operations, then chunking can also harm I/O performance.

Compression and Other I/O Filters

HDF5 supports I/O filters, i.e, data transformation (e.g, compression) that are applied transparently on read/write operations. I/O filters operate on a per-chunk basis in HDF5 and as such require the use of chunking. Chunking will be automatically enabled by h5py when compression and other I/O filters are enabled.

To use compression, we can wrap our dataset using H5DataIO and define the approbriate opions:

wrapped_data = H5DataIO(data=data,
                        compression='gzip',              # <---- Use GZip
                        compression_opts=4,              # <---- Optional GZip aggression option
                        )
test_ts = TimeSeries(name='test_gzipped_timeseries',
                     data=wrapped_data,                  # <----
                     unit='SIunit',
                     starting_time=0.0,
                     rate=10.0)
nwbfile.add_acquisition(test_ts)

Hint

In addition to compression, H5DataIO also allows us to enable the shuffle and fletcher32 HDF5 I/O filters.

Note

h5py (and HDF5 more broadly) support a number of different compression algorithms, e.g., GZIP, SZIP, or LZF (or even custom compression filters). However, only GZIP is built by default with HDF5, i.e., while data compressed with GZIP can be read on all platforms and installation of HDF5, other compressors may not be installed everywhere so that not all users may be able to access those files.

Writing the data

Writing the data now works as usual.

from pynwb import NWBHDF5IO

io = NWBHDF5IO('advanced_io_example.nwb', 'w')
io.write(nwbfile)
io.close()

Reading the data

Again, nothing has changed for read. All of the above advanced I/O features are handled transparently.

io = NWBHDF5IO('advanced_io_example.nwb', 'r')
nwbfile = io.read()

Now lets have a look to confirm that all our I/O settings where indeed used.

for k, v in nwbfile.acquisition.items():
    print("name=%s, chunks=%s, compression=%s, maxshape=%s" % (k,
                                                               v.data.chunks,
                                                               v.data.compression,
                                                               v.data.maxshape))
name=test_regular_timeseries, chunks=None, compression=None, maxshape=(10,)
name=test_compressed_timeseries, chunks=(10,), compression=gzip, maxshape=(10,)
name=test_chunked_timeseries, chunks=(250, 5), compression=None, maxshape=(None, 10)
name=test_gzipped_timeseries, chunks=(250, 5), compression=gzip, maxshape=(1000, 10)

As we can see, the datasets have been chunked and compressed correctly. Alos, as expected, chunking was automatically enabled for the compressed datasets.

Wrapping h5py.Datasets with H5DataIO

Just for completeness, H5DataIO also allows us to customize how h5py.Dataset objects should be handled on write by the PyNWBs HDF5 backend via the link_data parameter. If link_data is set to True then a SoftLink or ExternalLink will be created to point to the HDF5 dataset On the other hand, if link_data is set to False then the dataset be copied using h5py.Group.copy without copying attributes and without expanding soft links, external links, or references.

Note

When wrapping an h5py.Dataset object using H5DataIO, then all settings except link_data will be ignored as the h5py.Dataset will either be linked to or copied as on write.

Disclaimer

External links included in the tutorial are being provided as a convenience and for informational purposes only; they do not constitute an endorsement or an approval by the authors of any of the products, services or opinions of the corporation or organization or individual. The authors bear no responsibility for the accuracy, legality or content of the external site or for that of subsequent links. Contact the external site for answers to questions regarding its content.

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