.. image:: ./images/jsonlab-banner.png

##############################################################################                                               
      JSONLab: An open-source MATLAB/Octave JSON encoder and decoder             
##############################################################################

* 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


#################
Table of Contents
#################
.. contents::
  :local:
  :depth: 3

============
What's New
============

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] skil 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 ``jdata`` and ``bjdata`` python 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]`` 
  and ``float16 [h]`` ('''new in JSONLab v2.0''')
- BJData supports an optimized ND array container (supported in JSONLab since 2013)
- BJData does not convert ``NaN/Inf/-Inf`` to ``null`` (supported in JSONLab since 2013)

To avoid using the new type markers, one should attach ``'UBJSON',1`` in the ``savebj``
command as

.. code-block:: matlab

   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

.. code-block:: matlab

   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.


============
Introduction
============

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 <http://www.gnu.org/software/octave>`_ (a free MATLAB clone).

JSON (`JavaScript Object Notation <http://www.json.org/>`_) is a highly portable, 
human-readable and `"fat-free" <http://en.wikipedia.org/wiki/JSON>`_ text format 
to represent complex and hierarchical data, widely used for data-exchange in applications.
UBJSON (`Universal Binary JSON <http://ubjson.org/>`_) 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 <http://www.hdfgroup.org/HDF5/whatishdf5.html>`_ 
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.


================
Installation
================

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:

.. code:: shell

    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 

.. code:: shell

   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/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.

----------
Install JSONLab on Fedora 24 or later
----------

JSONLab has been available as an official Fedora package since 2015. You may
install it directly using the below command

.. code:: shell

   sudo dnf install octave-jsonlab

To enable data compression/decompression, you need to install ``octave-zmat`` using

.. code:: shell

   sudo dnf install octave-zmat

----------
Install JSONLab on Arch Linux
----------

JSONLab is also available on Arch Linux. You may install it using the below command

.. code:: shell

   sudo pikaur -S jsonlab

================
Using 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; 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.

----------
savejson.m
----------

.. code-block:: matlab

       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.m
----------

.. code-block:: matlab

       loadjson('{}')
       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
       dat=loadjson(['examples' filesep 'example1.json'])
       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',0)

-------------
savebj.m (saveubjson.m as an alias)
-------------

.. code-block:: matlab

       a={single(rand(2)), struct('va',1,'vb','string'), 1+2i};
       savebj(a)
       savebj('rootname',a,'testdata.ubj')
       savebj('zeros',zeros(100),'Compression','gzip')

-------------
loadbj.m (loadubjson.m as an alias)
-------------

.. code-block:: matlab

       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))

----------
jdataencode.m
----------

.. code-block:: matlab

      jd=jdataencode(struct('a',rand(5)+1i*rand(5),'b',[],'c',sparse(5,5)))
      savejson('',jd)

----------
jdatadecode.m
----------

.. code-block:: matlab

      rawdata=struct('a',rand(5)+1i*rand(5),'b',[],'c',sparse(5,5));
      jd=jdataencode(rawdata)
      newjd=jdatadecode(jd)
      isequaln(newjd,rawdata)

---------
examples
---------

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.

---------
unit testing
---------

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.


================
Using ``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 ``.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 ``.jamm`` file can be 50% smaller than a ``.mat`` file when using 
  ``jsave(..., "compression","lzma")``; the only drawback is longer saving time.
* a ``.jamm`` 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 ``.jamm`` files are largely compatible with UBJSON's parsers available at 
  http://ubjson.org/?page_id=48
* a ``.jamm`` file is quasi-human-readable, one can see the internal data fields 
  even in a command line, for example using ``strings -n 2 file.jamm | 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.m
----------

.. code-block:: matlab

      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.m
----------

.. code-block:: matlab

      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')


================
Sharing JSONLab created data files in Python
================

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)

.. code-block:: shell

      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

.. code-block:: shell

      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 

.. code-block:: shell

      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

.. code-block:: python

      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.

=======================
Known Issues and TODOs
=======================

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 results
  * ``savejson`` can only export the properties from MATLAB classes, but not the methods
  * ``saveubjson`` converts a logical array into a ``uint8`` (``[U]``) array
  * a special N-D array format, as defined in the JData specification, is 
    implemented in ``saveubjson``. You may use ``saveubjson(...,'NestArray',1)``
    to create UBJSON Draft-12 compliant files 
  * ``loadubjson`` can not parse all UBJSON Specification (Draft 12) compliant 
    files, however, it can parse all UBJSON files produced by ``saveubjson``.

==========================
Contribution and feedback
==========================

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:


.. code:: shell

      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).


==========================
Acknowledgement
==========================

---------
loadjson.m
---------

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

---------
loadmsgpack.m
---------

* 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.

---------
zlibdecode.m, zlibencode.m, gzipencode.m, gzipdecode.m, base64encode.m, base64decode.m
---------

* 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.
