Python datatype support for hdf files
While there are many ways to store different data types, many of them have their drawbacks. Sometimes it can be practical to store arrays with additional (pythonic) data in a single file. While hdf attributes can support some types, many exception exists especially with pyhtonic types.
This is a small implementation of recursive dict support for python to write and read hdf files with many different pythonic data types. Almost all types implemented in default python and numpy should be supported, even in nested structures. The resulting files work in hdfview and panoply with some small drawbacks.
Data types which are unknown will be serialized if possible using yaml. Lists and tuples are unrolled so they do not have to be serialized in most cases. Lazy support can be enabled allowing to work fast with large files, only loading references of large arrays and fetching the data on demand as supported by h5py. This works for numpy arrays only but mixed results are possible, e.g. having fully loaded pythonic types and referenced numpy arrays in a single loaded file.
Since this module is most likely used for data storage, please be warned that tests are still WiP and there is a good chance that you will encounter some types that either won't be saved or possibly break your file. No warranty is given.
The original idea was taken from SiggiGue thus there are some obvious similarities. This package extends the functionality to handle most of the pythonic data types and adds functions for convenient handling of the different data structures.
Find the Full documentation here