Combining hdf5 files

This is actually one of the use-cases of HDF5. If you just want to be able to access all the datasets from a single file, and don't care how they're actually stored on disk, you can use external links. From the HDF5 website:

External links allow a group to include objects in another HDF5 file and enable the library to access those objects as if they are in the current file. In this manner, a group may appear to directly contain datasets, named datatypes, and even groups that are actually in a different file. This feature is implemented via a suite of functions that create and manage the links, define and retrieve paths to external objects, and interpret link names:

Here's how to do it in h5py:

myfile = h5py.File('foo.hdf5','a')
myfile['ext link'] = h5py.ExternalLink("otherfile.hdf5", "/path/to/resource")

Be careful: when opening myfile, you should open it with 'a' if it is an existing file. If you open it with 'w', it will erase its contents.

This would be very much faster than copying all the datasets into a new file. I don't know how fast access to otherfile.hdf5 would be, but operating on all the datasets would be transparent - that is, h5py would see all the datasets as residing in foo.hdf5.

I found a non-python solution by using h5copy from the official hdf5 tools. h5copy can copy individual specified datasets from an hdf5 file into another existing hdf5 file.

If someone finds a python/h5py-based solution I would be glad to hear about it.

One solution is to use the h5py interface to the low-level H5Ocopy function of the HDF5 API, in particular the h5py.h5o.copy function:

In [1]: import h5py as h5

In [2]: hf1 = h5.File("f1.h5")

In [3]: hf2 = h5.File("f2.h5")

In [4]: hf1.create_dataset("val", data=35)
Out[4]: <HDF5 dataset "val": shape (), type "<i8">

In [5]: hf1.create_group("g1")
Out[5]: <HDF5 group "/g1" (0 members)>

In [6]: hf1.get("g1").create_dataset("val2", data="Thing")
Out[6]: <HDF5 dataset "val2": shape (), type "|O8">

In [7]: hf1.flush()

In [8]: h5.h5o.copy(hf1.id, "g1", hf2.id, "newg1")

In [9]: h5.h5o.copy(hf1.id, "val", hf2.id, "newval")

In [10]: hf2.values()
Out[10]: [<HDF5 group "/newg1" (1 members)>, <HDF5 dataset "newval": shape (), type "<i8">]

In [11]: hf2.get("newval").value
Out[11]: 35

In [12]: hf2.get("newg1").values()
Out[12]: [<HDF5 dataset "val2": shape (), type "|O8">]

In [13]: hf2.get("newg1").get("val2").value
Out[13]: 'Thing'

The above was generated with h5py version 2.0.1-2+b1 and iPython version 0.13.1-2+deb7u1 atop Python version 2.7.3-4+deb7u1 from a more-or-less vanilla install of Debian Wheezy. The files f1.h5 and f2.h5 did not exist prior to executing the above. Note that, per salotz, for Python 3 the dataset/group names need to be bytes (e.g., b"val"), not str.

The hf1.flush() in command [7] is crucial, as the low-level interface apparently will always draw from the version of the .h5 file stored on disk, not that cached in memory. Copying datasets to/from groups not at the root of a File can be achieved by supplying the ID of that group using, e.g., hf1.get("g1").id.

Note that h5py.h5o.copy will fail with an exception (no clobber) if an object of the indicated name already exists in the destination location.