Flammkuchen

Library for flexible HDF5 saving/loading. It was forked from the deepdish library from the University of Chicago to maintain its convenient i/o module.

Installation

pip install flammkuchen

Main feature

The primary feature of flammkuchen (ex deepdish) is its ability to save and load all kinds of data as HDF5. It can save any Python data structure, offering the same ease of use as pickling or numpy.save. However, it improves by also offering:

• Interoperability between languages (HDF5 is a popular standard)
• Easy to inspect the content from the command line (using h5ls or our specialized tool ddls)
• Highly compressed storage (thanks to a PyTables backend)
• Native support for scipy sparse matrices and pandas DataFrame and Series
• Ability to partially read files, even slices of arrays

An example:

import flammkuchen as fl

d = {
    'foo': np.ones((10, 20)),
    'sub': {
        'bar': 'a string',
        'baz': 1.23,
    },
}
fl.save('test.h5', d)

This can be reconstructed using fl.load('test.h5'), or inspected through the command line using either a standard tool:

$ h5ls test.h5
foo                      Dataset {10, 20}
sub                      Group

Or, better yet, our custom tool ddls (or python -m fl.ls):

$ ddls test.h5
/foo                       array (10, 20) [float64]
/sub                       dict
/sub/bar                   'a string' (8) [unicode]
/sub/baz                   1.23 [float64]

Further, one can use the metadata dynamically in a python script to load a subset of data with an unknown shape:

import flammkuchen as fl

foo_shape = fl.meta("test.h5", "/foo").shape
# (10, 20)

for i in range(foo_shape[0]):
    a_tiny_slice = fl.load("test.h5", "/foo", sel=fl.aslice[i, :])
    print(a_tiny_slice.shape)
    # (20, ) 

Read more at Saving and loading data.