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We are excited to announce that the JSONLab project, as the official reference library for both the JData and BJData specifications, is funded by the US National Institute of Health (NIH) as part of the NeuroJSON project (https://neurojson.org). The goal of the NeuroJSON project is to develop human-readable, scalable and future-proof neuroimaging data standards and data sharing services. All data produced from the NeuroJSON project will be using JSON/Binary JData formats as the underlying serialization standards and use the lightweight JData specification as language-independent data annotation standard, all of which have been evolved from the over a decade development of JSONLab.
JSONLab v2.9.8 - code named "Micronus - beta" - is the beta-release of the next milestone - JSONLab v3.0 - containing a number of key feature enhancement and bug fixes. The major new features include
There have been many major updates added to this release since the previous
release v2.0 in June 2020. A list of the major changes are summarized below
(with key features marked by *), including the support to BJData Draft-2 specification,
new interface functions savejd/loadjd, and options to use MATLAB/Octave built-in
jsonencode/jsondecode functions. The octave-jsonlab package has also been
included in the official distributions of Debian Bullseye and Ubuntu 21.04 or newer.
savebj/loadbj to BJData v1-draft 2, use little-endian by defaultsavejson/loadjson to call jsonencode/jsondecode
lz4encode file namePlease note that the savejson/loadjson in both JSONLab v2.0-v3.0 are
compliant with JData Spec Draft 3; the savebj/loadbj`` in JSONLab v3.0 is
compatible to BJData spec Draft 2, which contains breaking feature changes
compared to those in JSONLab v2.0.
The BJData spec was derived from UBJSON spec Draft 12, with the following breaking differences:
uint16 [u], uint32 [m], uint64 [M]
and float16 [h] (supported in JSONLab v2.0 or newer)NaN/Inf/-Inf to null (supported in JSONLab since 2013)UiuImlMLhdDC are allowedTo avoid using the new features, one should attach 'UBJSON',1 and 'Endian','B'
in the savebj command as
savebj('',data,'FileName','myfile.bjd','UBJSON',1, 'Endian','B');
To read BJData data files generated by JSONLab v2.0, you should call
data=loadbj('my_old_data_file.bjd','Endian','B')
You are strongly encouraged to convert all pre-v2.9 JSONLab generated BJD or .pmat files using the new format.
JSONLab is an open-source JSON/UBJSON/MessagePack encoder and decoder written completely in the native MATLAB language. It can be used to convert most MATLAB data structures (array, struct, cell, struct array, cell array, and objects) into JSON/UBJSON/MessagePack formatted strings and files, or to parse a JSON/UBJSON/MessagePack file into a MATLAB data structure. JSONLab supports both MATLAB and GNU Octave (a free MATLAB clone).
Compared to other MATLAB/Octave JSON parsers, JSONLab is uniquely lightweight,
ultra-portable, producing dependable outputs across a wide-range of MATLAB
(tested on R2008) and Octave (tested on v3.8) versions. It also uniquely supports
BinaryJData/UBJSON/MessagePack data files as binary-JSON-like formats, designed
for efficiency and flexibility with loss-less binary storage. As a parser written
completely with the native MATLAB language, it is surprisingly fast when reading
small-to-moderate sized JSON files (1-2 MB) with simple hierarchical structures,
and is heavily optimized for reading JSON files containing large N-D arrays
(known as the "fast array parser" in loadjson).
JSON (JavaScript Object Notation) is a highly portable, human-readable and "fat-free" text format to represent complex and hierarchical data, widely used for data-exchange in applications. UBJSON (Universal Binary JSON) is a binary JSON format, designed to specifically address the limitations of JSON, permitting the storage of binary data with strongly typed data records, resulting in smaller file sizes and fast encoding and decoding. MessagePack is another binary JSON-like data format widely used in data exchange in web/native applications. It is slightly more compact than UBJSON, but is not directly readable compared to UBJSON.
We envision that both JSON and its binary counterparts will play important roles for storage, exchange and interoperation of large-scale scientific data among the wide-variety of tools. As container-formats, they offer both the flexibility and generality similar to other more sophisticated formats such as HDF5, but are significantly simpler with a much greater software ecosystem.
Towards this goal, we have developed the JData Specification (http://github.com/NeuroJSON/jdata) to standardize serializations of complex scientific data structures, such as N-D arrays, sparse/complex-valued arrays, trees, maps, tables and graphs using JSON/binary JSON constructs. The text and binary formatted JData files are syntactically compatible with JSON/UBJSON formats, and can be readily parsed using existing JSON and UBJSON parsers. JSONLab is not just a parser and writer of JSON/UBJSON data files, but one that systematically converts complex scientific data structures into human-readable and universally supported JSON forms using the standardized JData data annotations.
The installation of JSONLab is no different from installing any other MATLAB toolbox. You only need to download/unzip the JSONLab package to a folder, and add the folder's path to MATLAB/Octave's path list by using the following command:
addpath('/path/to/jsonlab');
If you want to add this path permanently, you can type pathtool,
browse to the JSONLab root folder and add to the list, then click "Save".
Then, run rehash in MATLAB, and type which savejson, if you see an
output, that means JSONLab is installed for MATLAB/Octave.
If you use MATLAB in a shared environment such as a Linux server, the best way to add path is to type
mkdir ~/matlab/
nano ~/matlab/startup.m
and type addpath('/path/to/jsonlab') in this file, save and quit the editor.
MATLAB will execute this file every time it starts. For Octave, the file
you need to edit is ~/.octaverc, where ~ is your home directory.
To use the data compression features, please download the ZMat toolbox from
https://github.com/NeuroJSON/zmat/releases/latest and follow the instruction to
install ZMat first. The ZMat toolbox is required when compression is used on
MATLAB running in the -nojvm mode or GNU Octave, or 'lzma/lzip/lz4/lz4hc'
compression methods are specified. ZMat can also compress large arrays that
MATLAB's Java-based compression API does not support.
JSONLab has been available as an official Fedora package since 2015. You may install it directly using the below command
sudo dnf install octave-jsonlab
To enable data compression/decompression, you need to install octave-zmat using
sudo dnf install octave-zmat
Then open Octave, and type pkg load jsonlab to enable jsonlab toolbox.
JSONLab is currently available on Debian Bullseye. To install, you may run
sudo apt-get install octave-jsonlab
One can alternatively install matlab-jsonlab if MATLAB is available.
JSONLab is currently available on Ubuntu 21.04 or newer as package octave-jsonlab. To install, you may run
sudo apt-get install octave-jsonlab
For older Ubuntu releases, one can add the below PPA
https://launchpad.net/~fangq/+archive/ubuntu/ppa
To install, please run
sudo add-apt-repository ppa:fangq/ppa
sudo apt-get update
to add this PPA, and then use
sudo apt-get install octave-jsonlab
to install the toolbox. octave-zmat will be automatically installed.
JSONLab is also available on Arch Linux. You may install it using the below command
sudo pikaur -S jsonlab
JSONLab provides a pair of functions, loadjson -- a JSON parser, and savejson --
a MATLAB-to-JSON encoder, to read/write the text-based JSON; it also provides
three equivalent pairs -- loadbj/savebj for binary JData, loadubjson/saveubjson
for UBJSON and loadmsgpack/savemsgpack for MessagePack. The load* functions
for the 3 supported data formats share almost the same input parameter format,
similarly for the 3 save* functions (savejson/saveubjson/savemsgpack).
These encoders and decoders are capable of processing/sharing almost all
data structures supported by MATLAB, thanks to jdataencode/jdatadecode -
a pair of in-memory data converters translating complex MATLAB data structures
to their easy-to-serialized forms according to the JData specifications.
The detailed help information can be found in the Contents.m file.
In JSONLab 2.9.8 and later versions, a unified file loading and saving interface
is provided for JSON, binary JSON and HDF5, including loadjd and savejd
for reading and writing below files types:
.json`, ``.jdt (text JData file), .jmsh (text JMesh file),
.jnii (text JNIfTI file), .jnirs (text JSNIRF file).bjd`, ``.jdb` (binary JData file), ``.bmsh (binary JMesh file),
.bnii (binary JNIfTI file), .bnirs (binary JSNIRF file), .pmat (MATLAB session file).ubj
.msgpack
.h5, .hdf5, .snirf (SNIRF fNIRS data files) - require EasyH5 toolbox
In the below section, we provide a few examples on how to us each of the core functions for encoding/decoding JSON/UBJSON/MessagePack data.
jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],...
'MeshElem',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
'MeshSurf',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
'MeshCreator','FangQ','MeshTitle','T6 Cube',...
'SpecialData',[nan, inf, -inf]);
savejson(jsonmesh)
savejson('jmesh',jsonmesh)
savejson('',jsonmesh,'Compact',1)
savejson('jmesh',jsonmesh,'outputfile.json')
savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g','FileName','outputfile2.json')
savejson('cpxrand',eye(5)+1i*magic(5))
savejson('ziparray',eye(10),'Compression','zlib','CompressArraySize',1)
savejson('',jsonmesh,'ArrayToStruct',1)
savejson('',eye(10),'UseArrayShape',1)
loadjson('{}')
dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
dat=loadjson(['examples' filesep 'example1.json'])
dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',0)
a={single(rand(2)), struct('va',1,'vb','string'), 1+2i};
savebj(a)
savebj('rootname',a,'testdata.ubj')
savebj('zeros',zeros(100),'Compression','gzip')
obj=struct('string','value','array',single([1 2 3]),'empty',[],'magic',uint8(magic(5)));
ubjdata=savebj('obj',obj);
dat=loadbj(ubjdata)
class(dat.obj.array)
isequaln(obj,dat.obj)
dat=loadbj(savebj('',eye(10),'Compression','zlib','CompressArraySize',1))
jd=jdataencode(struct('a',rand(5)+1i*rand(5),'b',[],'c',sparse(5,5)))
savejson('',jd)
rawdata=struct('a',rand(5)+1i*rand(5),'b',[],'c',sparse(5,5));
jd=jdataencode(rawdata)
newjd=jdatadecode(jd)
isequaln(newjd,rawdata)
Under the examples folder, you can find several scripts to demonstrate the
basic utilities of JSONLab. Running the demo_jsonlab_basic.m script, you
will see the conversions from MATLAB data structure to JSON text and backward.
In jsonlab_selftest.m, we load complex JSON files downloaded from the Internet
and validate the loadjson/savejson functions for regression testing purposes.
Similarly, a demo_ubjson_basic.m script is provided to test the saveubjson
and loadubjson functions for various matlab data structures, and
demo_msgpack_basic.m is for testing savemsgpack and loadmsgpack.
Please run these examples and understand how JSONLab works before you use it to process your data.
Under the test folder, you can find a script to test individual data types and
inputs using various encoders and decoders. This unit testing script also serves as
a specification validator to the JSONLab functions and ensure that the outputs
are compliant to the underlying specifications.
jsave/jload to share workspace
Starting from JSONLab v2.0, we provide a pair of functions, jsave/jload to store
and retrieve variables from the current workspace, similar to the save/load
functions in MATLAB and Octave. The files that jsave/jload reads/writes is by
default a binary JData file with a suffix .pmat. The file size is comparable
(can be smaller if use lzma compression) to .mat files. This feature
is currently experimental.
The main benefits of using .pmat file to share matlab variables include
.pmat file can be 50% smaller than a .mat file when using
jsave(..., "compression","lzma"); the only drawback is longer saving time..pmat file can be readily read/opened among many programming environments, including
Python, JavaScript, Go, Java etc, where .mat file support is not generally available.
Parsers of .pmat files are largely compatible with UBJSON's parsers available at
http://ubjson.org/?page_id=48
.pmat file is quasi-human-readable, one can see the internal data fields
even in a command line, for example using strings -n 2 file.pmat | astyle,
making the binary data easy to be understood, shared and reused.jsave/jload can also use MessagePack and JSON formats as the underlying
data storage format, addressing needs from a diverse set of applications.
MessagePack parsers are readily available at https://msgpack.org/
jsave % save the current workspace to default.pmat
jsave mydata.pmat
jsave('mydata.pmat','vars',{'var1','var2'})
jsave('mydata.pmat','compression','lzma')
jsave('mydata.json','compression','gzip')
jload % load variables from default.pmat to the current workspace
jload mydata.pmat % load variables from mydata.pmat
vars=jload('mydata.pmat','vars',{'var1','var2'}) % return vars.var1, vars.var2
jload('mydata.pmat','simplifycell',0)
jload('mydata.json')
Despite the use of portable data annotation defined by the JData Specification,
the output JSON files created by JSONLab are 100% JSON compatible (with
the exception that long strings may be broken into multiple lines for better
readability). Therefore, JSONLab-created JSON files (.json, .jnii, .jnirs etc)
can be readily read and written by nearly all existing JSON parsers, including
the built-in json module parser in Python.
However, we strongly recommend one to use a lightweight jdata module,
developed by the same author, to perform the extra JData encoding and decoding
and convert JSON data directly to convenient Python/Numpy data structures.
The jdata module can also directly read/write UBJSON/Binary JData outputs
from JSONLab (.bjd, .ubj, .bnii, .bnirs, .pmat etc). Using binary JData
files are expected to produce much smaller file sizes and faster parsing,
while maintaining excellent portability and generality.
In short, to conveniently read/write data files created by JSONLab into Python, whether they are JSON based or binary JData/UBJSON based, one just need to download the below two light-weight python modules:
To install these modules on Python 2.x, please first check if your system has
pip and numpy, if not, please install it by running (using Ubuntu/Debian as example)
sudo apt-get install python-pip python3-pip python-numpy python3-numpy
After the installation is done, one can then install the jdata and bjdata modules by
pip install jdata --user
pip install bjdata --user
To install these modules for Python 3.x, please replace pip by pip3.
If one prefers to install these modules globally for all users, simply
execute the above commands using
sudo pip install jdata
sudo pip install bjdata
The above modules require built-in Python modules json and NumPy (numpy).
Once the necessary modules are installed, one can type python (or python3), and run
import jdata as jd
import numpy as np
from collections import OrderedDict
data1=jd.loadt('myfile.json',object_pairs_hook=OrderedDict);
data2=jd.loadb('myfile.ubj',object_pairs_hook=OrderedDict);
data3=jd.loadb('myfile.pmat',object_pairs_hook=OrderedDict);
where jd.loadt() function loads a text-based JSON file, performs
JData decoding and converts the enclosed data into Python dict, list
and numpy objects. Similarly, jd.loadb() function loads a binary
JData/UBJSON file and performs similar conversions. One can directly call
jd.load() to open JSONLab (and derived toolboxes such as jnifti:
https://github.com/NeuroJSON/jnifti or jsnirf: https://github.com/NeuroJSON/jsnirf)
generated files based on their respective file suffix.
Similarly, the jd.savet(), jd.saveb() and jd.save functions
can revert the direction and convert a Python/Numpy object into JData encoded
data structure and store as text-, binary- and suffix-determined output files,
respectively.
JSONLab has several known limitations. We are striving to make it more general and robust. Hopefully in a few future releases, the limitations become less.
Here are the known issues:
- 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays
- When processing names containing multi-byte characters, Octave and MATLAB can give different field-names; you can use
feature('DefaultCharacterSet','latin1')in MATLAB to get consistent resultssavejsoncan only export the properties from MATLAB classes, but not the methodssaveubjsonconverts a logical array into auint8([U]) array- a special N-D array format, as defined in the JData specification, is implemented in
saveubjson. You may usesaveubjson(...,'NestArray',1)to create UBJSON Draft-12 compliant filesloadubjsoncan not parse all UBJSON Specification (Draft 12) compliant files, however, it can parse all UBJSON files produced bysaveubjson.
JSONLab is an open-source project. This means you can not only use it and modify it as you wish, but also you can contribute your changes back to JSONLab so that everyone else can enjoy the improvement. For anyone who want to contribute, please download JSONLab source code from its source code repositories by using the following command:
git clone https://github.com/fangq/jsonlab.git jsonlab
or browsing the github site at
https://github.com/fangq/jsonlab
Please report any bugs or issues to the below URL:
https://github.com/fangq/jsonlab/issues
Sometimes, you may find it is necessary to modify JSONLab to achieve your goals, or attempt to modify JSONLab functions to fix a bug that you have encountered. If you are happy with your changes and willing to share those changes to the upstream author, you are recommended to create a pull-request on github.
To create a pull-request, you first need to "fork" jsonlab on Github by clicking on the "fork" button on top-right of JSONLab's github page. Once you forked jsonlab to your own directory, you should then implement the changes in your own fork. After thoroughly testing it and you are confident the modification is complete and effective, you can then click on the "New pull request" button, and on the left, select fangq/jsonlab as the "base". Then type in the description of the changes. You are responsible to format the code updates using the same convention (tab-width: 8, indentation: 4 spaces) as the upstream code.
We appreciate any suggestions and feedbacks from you. Please use the following mailing list to report any questions you may have regarding JSONLab:
https://github.com/fangq/jsonlab/issues
(Subscription to the mailing list is needed in order to post messages).
The loadjson.m function was significantly modified from the earlier parsers
(BSD 3-clause licensed) written by the below authors
Copyright (c) 2014,2016 Bastian Bechtold All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Copyright (c) 2012, Kota Yamaguchi All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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