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# JData for Python - a lightweight and portable data annotation method
- Copyright: (C) Qianqian Fang (2019-2020) <q.fang at neu.edu>
- License: Apache License, Version 2.0
- Version: 0.3.6
- URL: https://github.com/fangq/pyjdata
[](https://travis-ci.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 install
* Github: download from https://github.com/fangq/pyjdata
* PIP: run `pip install jdata` see https://pypi.org/project/jdata/
This package can also be installed on Ubuntu (Debian package is currently under
review) via
```
sudo add-apt-repository ppa:fangq/ppa
sudo apt-get update
sudo apt-get install python-jdata python3-jdata
```
Dependencies:
* **bjdata**: PIP: run `pip install bjdata` see https://pypi.org/project/bjdata/
* **numpy**: PIP: run `pip install numpy` or `sudo apt-get install python-numpy`
* **backports.lzma**: PIP: run `pip install backports.lzma` (needed for Python 2.7)
Replacing `pip` by `pip3` if you are using Python 3.x. If either `pip` or `pip3`
does not exist on your system, please run
```
sudo apt-get install python-pip python3-pip
```
One can also install this module from the source code. To do this, you first
check out a copy of the latest code from Github by
```
git clone https://github.com/fangq/pyjdata.git
cd pyjdata
```
then install the module to your local user folder by
```
python setup.py install --user
```
or, if you prefer, install to the system folder for all users by
```
sudo python setup.py install
```
Please replace `python` by `python3` if you want to install it for Python 3.x instead of 2.x.
## 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
```
To see additional data type support, please run the built-in test using below command
```
python -m unittest discover -v test
```
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