- Copyright (C) 2011-2020 Qianqian Fang <q.fang at neu.edu>
- License: BSD or GNU General Public License version 3 (GPL v3), see License*.txt
- Version: 2.0 (Magnus Prime)
- JData Specification Version: Draft 3 (http://github.com/fangq/jdata)
- URL: http://openjdata.org/jsonlab
JSONLab v2.0 - code named "Magnus Prime" - is a stable release of JSONLab and a new milestone towards a stable, complete reference implementation of the JData Specification (http://openjdata.org) for portable scientific data storage.
There have been many major updates added to this release since the previous
release v1.9.8 in Oct. 2019. A list of the major changes are summarized below
(with key features marked by *), including the support to _ArrayShape_ to
efficiently encode special matrices and the addition of jsave/jload to save
and restore variables in MATLAB/Octave like the save/load commands (experimental):
- 2020-06-13 [81feef3] skip no-op markers, update documentation
- 2020-06-13 [4904155] jload load data to struct, test if loadbj input is buffer, update error msg
- 2020-06-12 [c334799] change default workspace to caller for jload and jsave
- 2020-06-10 [c883546] fix keeptype single integer bug
- 2020-06-09*[ ] created
jdataandbjdatapython modules to share data with MATLAB - 2020-06-08*[cbde607] add savebj and loadbj to dedicate to loading and saving bjdata
- 2020-06-08*[e2451e1] add unit testing script, fix issues found in the testing unit
- 2020-06-06 [a44015f] accelerate fast_match_bracket, drop unicode2native for speed
- 2020-06-06 [eefccf3] call jsonencode/decode in jsave/jload, parse embedded jdata struct
- 2020-06-05 [9434103] support Toeplitz matrices, use case-insensitive comparison
- 2020-06-04 [3119ce4] jdatadecode now handles _ArrayOrder_
- 2020-06-04 [89b844c] remove forced root name, update internal test results
- 2020-06-02*[15ca7ae] add keeptype option to jsave and saveubjson
- 2020-06-02 [7f2cbc4] make jsave and jload work on octave
- 2020-06-01*[8829d6b] apply data compression to strings, new datatype char
- 2020-06-01 [270cbf6] fix loadmsgpack ND array issue
- 2020-06-01*[919f502] add jsave and jload for portable data sharing,update doc
- 2020-05-31 [df3a4fa] debug arrayshape related changes and test all demo scripts
- 2020-05-31*[fc0b285] adding support to _ArrayShape_ to record special matrices
- 2020-05-15*[d88d454] jsonlab is compatible with matlab R2008
- 2020-05-13 [86efe89] flag to prevent embedding ND array size specifier
- 2020-05-07 [a189a50] use more robust integer type testing
- 2020-05-06*[82f5249] saveubjson now implments BJData spec Draft1,https://github.com/fangq/bjdata
- 2020-05-03 [34bca22] add prj file to compile a matlab package, close #60
- 2020-05-03 [82dfdcc] handle empty array in loadmsgpack, fix #63, patch by stfnp
- 2020-03-08 [7499bd8] Merge pull request #61 from j2L4e/patch-1
- 2020-02-09*[6984111] add UseMap option to avoid key name conversion
- 2019-11-16 [e46221a] if _ArraySize_ has a single length, treat as a row vector
- 2019-11-01 [f2bfb65] fix a uint8 upper bound bug
- 2019-10-24 [cc4491d] avoid escaping base64 str, avoid double processing preencoded arrayzipdata
- 2019-10-24 [4dc76ef] make example script compatible with matlab R2010
- 2019-10-24 [ad8be26] disable underscore escaping in octave,update all tests and outputs
- 2019-10-24 [d4275c6] reduce jsonopt calls to speed up encoding and decoding
- 2019-10-23 [82c9e91] fix invalid jdatadecode example
- 2019-10-23 [398539d] reoptimize for speed
- 2019-10-22*[650b5ec] enable jdataencode in savejson and saveubjson
Please note that JSONLab v2.0 is now compliant with JData Spec Draft 3; in comparison v1.9.8 is compatible with Draft 2; v1.9 and previous releases are compatible with Draft 1. JSONLab v2.0 can read all data files generated by v1.9.8, but v1.9.8 can not read the new UBJSON markers introduced in v2.0.
The newly introduced jsave/jload functions are in the experimental stage.
They generate .jamm files which are renamed binary-JData/UBJSON files;
they can be 50% smaller than .mat files if using jsave(...,'compression','lzma')
and can be readily opened among a long list of programming environments
such as Python, JavaScript and Go.
The saveubjson/loadubjson functions added support to the Binary JData specification (BJData)
v1 Draft-1 (https://github.com/fangq/bjdata) and are now renamed as savebj/loadbj
(saveubjson/loadubjson are kept for compatibility purposes as aliases to the new
functions). The BJData spec is largely compatible with UBJSON spec Draft 12, with the
following differences (we are working with the UBJSON maintainer to merge
these two specifications):
- BJData adds 4 new numeric data types:
uint16 [u],uint32 [m],uint64 [M]andfloat16 [h]('''new in JSONLab v2.0''') - BJData supports an optimized ND array container (supported in JSONLab since 2013)
- BJData does not convert
NaN/Inf/-Inftonull(supported in JSONLab since 2013)
To avoid using the new type markers, one should attach 'UBJSON',1 in the savebj
command as
savebj('',data,'FileName','myfile.bjd','UBJSON',1);To read data files generated by JSONLab v1.9 or older versions, you need to attach
option 'FormatVersion', 1.9 in all the loadjson/savejson function calls.
To convert an older file (JSON/UBJSON) to the new format, you should run
data=loadjson('my_old_data_file.json','FormatVersion',1.9)
savejson('',data,'FileName','new_file.json')You are strongly encouraged to convert all pre-v1.9.8 generated data files using the new format.
JSONLab is a free and open-source JSON/UBJSON/MessagePack encoder and decoder written in the native MATLAB language. It can be used to convert a MATLAB data structure (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 MATLAB data structure. JSONLab supports both MATLAB and GNU Octave (a free MATLAB clone).
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, specifically 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 rules not only for light-weight data storage, but also for storage and interchange of scientific data. It has both the flexibility and generality as in other general-purpose file specifications, such as HDF5 but has significantly reduced complexity and excellent readability.
Towards this goal, we have developed the JData Specification (http://github.com/fangq/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.mand 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/fangq/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-jsonlabTo enable data compression/decompression, you need to install octave-zmat using
sudo dnf install octave-zmatThen open Octave, and type pkg load jsonlab to enable jsonlab toolbox.
JSONLab is currently available on Debian unstable. To install, you may run
sudo apt-get install octave-jsonlabOne can alternatively install matlab-jsonlab if MATLAB is available.
JSONLab is currently available on the below PPA for Ubuntu users:
https://launchpad.net/~fangq/+archive/ubuntu/ppa
To install, please run
sudo add-apt-repository ppa:fangq/ppa
sudo apt-get updateto add this PPA, and then use
sudo apt-get install octave-jsonlabto 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 jsonlabJSONLab provides a pair of functions, loadjson -- a JSON parser, and
savejson -- a MATLAB-to-JSON encoder, to read/write the text-based JSON; and
two equivallent pairs -- loadubjson/saveubjson for binary
JSON 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 data structures
to the easy-to-serialized forms according to the JData specifications.
The detailed help information can be found in the Contents.m file.
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.
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 .jamm. 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 .jamm file to share matlab variables include
- a
.jammfile can be 50% smaller than a.matfile when usingjsave(..., "compression","lzma"); the only drawback is longer saving time. - a
.jammfile 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.jammfiles are largely compatible with UBJSON's parsers available at http://ubjson.org/?page_id=48 - a
.jammfile is quasi-human-readable, one can see the internal data fields even in a command line, for example usingstrings -n 2 file.jamm | astyle, making the binary data easy to be understood, shared and reused. jsave/jloadcan 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 jamdata.jamm
jsave mydata.jamm
jsave('mydata.jamm','vars',{'var1','var2'})
jsave('mydata.jamm','compression','lzma')
jsave('mydata.json','compression','gzip')jload % load variables from jamdata.jamm to the current workspace
jload mydata.jamm % load variables from mydata.jamm
vars=jload('mydata.jamm','vars',{'var1','var2'}) % return vars.var1, vars.var2
jload('mydata.jamm','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, .jamm etc). Using binary JData
files are exptected to produce much smaller file sizes and faster parsing,
while maintainining 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:
- jdata: PyPi: https://pypi.org/project/jdata/ ; Github: https://github.com/fangq/pyjdata
- bjdata PyPi: https://pypi.org/project/bjdata/ ; Github: https://github.com/fangq/pybj
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-numpyAfter the installation is done, one can then install the jdata and bjdata modules by
pip install jdata --user
pip install bjdata --userTo 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 bjdataThe 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.jamm',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/fangq/jnifti or jsnirfy: https://github.com/fangq/jsnirfy)
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 consistant 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 jsonlabor 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
- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
- created on 2009/11/02
- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
- created on 2009/03/22
- Joel Feenstra:
- http://www.mathworks.com/matlabcentral/fileexchange/20565 created on 2008/07/03
- Author: Bastian Bechtold
- URL: https://github.com/bastibe/matlab-msgpack/blob/master/parsemsgpack.m
- License: BSD 3-clause license
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:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
- Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
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.
- Author: Kota Yamaguchi
- URL: https://www.mathworks.com/matlabcentral/fileexchange/39526-byte-encoding-utilities
- License: BSD License, see below
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:
- Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution
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.