If you’re storing large amounts of data that you need to quick access to, your standard text file isn’t going to cut it. The kinds of cosmological simulations that I run generate huge amounts of data, and to analyse them I need to be able access the exact data that I want quickly and painlessly.
HDF5 is one answer. It’s a powerful binary data format with no upper limit on the file size. It provides parallel IO, and carries out a bunch of low level optimisations under the hood to make queries faster and storage requirements smaller.
Here’s a quick intro to the h5py package, which provides a Python interface to the HDF5 data format. We’ll create a HDF5 file, query it, create a group and save compressed data.
You’ll need HDF5 installed, which can be a pain. Getting h5py is relatively painless in comparison, just use your favourite package manager.
Creating HDF5 files
We first load the
Now mock up some simple dummy data to save to our file.
(1000, 20) (1000, 200)
The first step to creating a HDF5 file is to initialise it. It uses a very similar syntax to initialising a typical text file in numpy. The first argument provides the filename and location, the second the mode. We’re writing the file, so we provide a w for write access.
This creates a file object,
hf, which has a bunch of associated methods. One is
create_dataset, which does what it says on the tin. Just provide a name for the dataset, and the numpy array.
<HDF5 dataset "dataset_2": shape (1000, 200), type "<f8">
All we need to do now is close the file, which will write all of our work to disk.
Reading HDF5 files
To open and read data we use the same
File method in read mode, r.
To see what data is in this file, we can call the
keys() method on the file object.
We can then grab each dataset we created above using the
get method, specifying the name.
This returns a HDF5 dataset object. To convert this to an array, just call numpy’s array method.
Groups are the basic container mechanism in a HDF5 file, allowing hierarchical organisation of the data. Groups are created similarly to datasets, and datsets are then added using the group object.
<HDF5 dataset "data2": shape (100, 33), type "<f8">
We can also create subfolders. Just specify the group name as a directory format.
<HDF5 dataset "data3": shape (100, 3333), type "<f8">
As before, to read data in irectories and subdirectories use the
get method with the full subdirectory path.
[(u'data3', <HDF5 dataset "data3": shape (100, 3333), type "<f8">)]
[(u'data1', <HDF5 dataset "data1": shape (100, 33), type "<f8">), (u'data2', <HDF5 dataset "data2": shape (100, 33), type "<f8">)]
To save on disk space, while sacrificing read speed, you can compress the data. Just add the
compression argument, which can be either
gzip is the most portable, as it’s available with every HDF5 install,
lzf is the fastest but doesn’t compress as effectively as
szip is a NASA format that is patented up; if you don’t know about it, chances are your organisation doesn’t have the patent, so avoid.
gzip you can also specify the additional
compression_opts argument, which sets the compression level. The default is 4, but it can be an integer between 0 and 9.
This post took inspiration from a DataJoy tutorial.
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