jdata 0.2.1

Encoding and decoding Python data structrues using portable JData-annotated formats

# JData for Python - a lightweight and portable data annotation method

• Copyright: (C) Qianqian Fang (2019) <q.fang at neu.edu>
• License: Apache License, Version 2.0
• Version: 0.2
• URL: https://github.com/fangq/pyjdata

The [JData Specification](https://github.com/fangq/jdata/) defines a lightweight language-independent data annotation interface targetted at storing and sharing complex data structures across different programming languages such as MATLAB, JavaScript, Python etc. Using JData formats, a complex Python data structure can be encoded as a dict object that is easily serialized as a JSON/binary JSON file and share such data between programs of different languages.

## How to use

The PyJData module is easy to use. You can use the encode()/decode() functions to encode Python data into JData annotation format, or decode JData structures into native Python data, for example

` import jdata as jd import numpy as np a={'str':'test','num':1.2,'list':[1.1,[2.1]],'nan':float('nan'),'np':np.arange(1,5,dtype=np.uint8)} jd.encode(a) jd.decode(jd.encode(a)) d1=jd.encode(a,{'compression':'zlib','base64':1}) d1 jd.decode(d1,{'base64':1}) `

One can further save the JData annotated data into JSON or binary JSON (UBJSON) files using the jdata.save function, or loading JData-formatted data to Python using jdata.load

` import jdata as jd import numpy as np a={'str':'test','num':1.2,'list':[1.1,[2.1]],'nan':float('nan'),'np':np.arange(1,5,dtype=np.uint8)} jd.save(a,'test.json') newdata=jd.load('test.json') newdata `