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HDF5 for Python (h5py) is a Python interface to the HDF5 library. Built
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on a near-complete Python wrapping of the HDF5 C API, it exposes a simple,
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NumPy-like interface for interacting with HDF5 files, datasets, attributes
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This module demonstrates the use of HDF5 groups from h5py. HDF5 groups
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are analagous to directories in a filesystem; they even use the UNIX-style
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/path/to/resource syntax. In h5py, groups act like dictionaries. They
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also provide the necessary methods to create subgroups, datasets, and
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HDF5 for Python is available at h5py.alfven.org.
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f = h5py.File('myfile.hdf5','w')
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# The file object is also the "root group" ("/") in HDF5. It's currently
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print "Number of items in the root group: %d" % len(f)
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g1 = f.create_group('Group1')
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g2 = f.create_group('Another Group')
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g3 = f.create_group('Yet another group')
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# All groups, including the root group, support a basic dictionary-style
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print "There are now %d items in the root group" % len(f)
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print "They are: %s" % ", ".join(f) # iterating yields member names
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# Groups can contain subgroups themselves
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sub1 = g1.create_group("Subgroup1")
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# Prints "/Group1/Subgroup1"
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print "Full name of subgroup is %s" % sub1.name
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# You can retrieve them using __getitem__ syntax
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sub2 = g1['Subgroup1']
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# You can attach attributes to groups, just like datasets, containing just
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# about anything NumPy can handle.
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g1.attrs['purpose'] = "A demonstration group"
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g1.attrs['Life, universe, everything'] = 42
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g1.attrs['A numpy array'] = np.ones((3,), dtype='>i2')
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# Create datasets using group methods. (See other examples for a more in-
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# depth introduction to datasets).
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data = np.arange(100*100).reshape((100,100))
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dset = sub1.create_dataset("My dataset", data=data)
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print "The new dataset has full name %s, shape %s and type %s" % \
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(dset.name, dset.shape, dset.dtype)
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# Closing the file closes all open objects
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examples/groups.py
