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696 lines (659 loc) · 29.2 KB
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function jdata = jdataencode(data, varargin)
%
% jdata=jdataencode(data)
% or
% jdata=jdataencode(data, options)
% jdata=jdataencode(data, 'Param1',value1, 'Param2',value2,...)
%
% Annotate a MATLAB struct or cell array into a JData-compliant data
% structure as defined in the JData spec: http://github.com/NeuroJSON/jdata.
% This encoded form servers as an intermediate format that allows unambiguous
% storage, exchange of complex data structures and easy-to-serialize by
% json encoders such as savejson and jsonencode (MATLAB R2016b or newer)
%
% This function implements the JData Specification Draft 3 (Jun. 2020) and
% select keywords from Draft 4 (Apr. 2026) when FormatVersion is set to 4 or above.
% New keywords in Draft 4 (FormatVersion>=4): _ArrayShape_ types range/identity/zero,
% _ArrayCoords_, _ArrayUnits_, _ArrayFillValue_, _TableIndex_, _TableSortOrder_, _DataSchema_,
% _EnumKey_/_EnumValue_/_EnumOrdered_ (categorical), _ArrayChunks_ (chunked compression)
% see http://github.com/NeuroJSON/jdata for details
%
% author: Qianqian Fang (q.fang <at> neu.edu)
%
% input:
% data: a structure (array) or cell (array) to be encoded.
% options: (optional) a struct or Param/value pairs for user
% specified options (first in [.|.] is the default)
% AnnotateArray: [0|1] - if set to 1, convert all 1D/2D matrices
% to the annotated JData array format to preserve data types;
% N-D (N>2), complex and sparse arrays are encoded using the
% annotated format by default. Please set this option to 1 if
% you intend to use MATLAB's jsonencode to convert to JSON.
% Base64: [0|1] if set to 1, _ArrayZipData_ is assumed to
% be encoded with base64 format and need to be
% decoded first. This is needed for JSON but not
% UBJSON data
% Prefix: ['x0x5F'|'x'] for JData files loaded via loadjson/loadubjson, the
% default JData keyword prefix is 'x0x5F'; if the
% json file is loaded using matlab2018's
% jsondecode(), the prefix is 'x'; this function
% attempts to automatically determine the prefix;
% for octave, the default value is an empty string ''.
% UseArrayZipSize: [1|0] if set to 1, _ArrayZipSize_ will be added to
% store the "pre-processed" data dimensions, i.e.
% the original data stored in _ArrayData_, and then flaten
% _ArrayData_ into a row vector using row-major
% order; if set to 0, a 2D _ArrayData_ will be used
% UseArrayShape: [0|1] if set to 1, a matrix will be tested by
% to determine if it is diagonal, triangular, banded or
% toeplitz, and use _ArrayShape_ to encode the matrix
% MapAsStruct: [0|1] if set to 1, convert containers.Map into
% struct; otherwise, keep it as map
% DateTime: [1|0] if set to 1, convert datetime to string
% Compression: ['zlib'|'gzip','lzma','lz4','lz4hc'] - use zlib method
% to compress data array
% CompressArraySize: [300|int]: only to compress an array if the
% total element count is larger than this number.
% FormatVersion [2|float]: set the JSONLab output version; since
% v2.0, JSONLab uses JData specification Draft 1
% for output format, it is incompatible with all
% previous releases; if old output is desired,
% please set FormatVersion to 1.9 or earlier. Set
% to 4 to enable JData Draft 4 keywords: _ArrayShape_
% range/identity/zero, _ArrayCoords_, _ArrayUnits_,
% _ArrayFillValue_, _TableIndex_, _TableSortOrder_,
% _EnumKey_/_EnumValue_ (categorical), _ArrayChunks_.
% ArrayChunks: [[]|int] when set to a positive integer and
% Compression is also set, split the pre-processed array
% buffer into 1-D segments of this many elements and
% compress each independently (FormatVersion>=4 only).
% _ArrayZipData_ then becomes a cell array of payloads.
%
% example:
% jd=jdataencode(struct('a',rand(5)+1i*rand(5),'b',[],'c',sparse(5,5)))
%
% encodedmat=jdataencode(single(magic(5)),'annotatearray',1,'prefix','x')
% jdatadecode(jsondecode(jsonencode(encodedmat))) % serialize by jsonencode
% jdatadecode(loadjson(savejson('',encodedmat))) % serialize by savejson
%
% encodedtoeplitz=jdataencode(uint8(toeplitz([1,2,3,4],[1,5,6])),'usearrayshape',1,'prefix','x')
% jdatadecode(jsondecode(jsonencode(encodedtoeplitz))) % serialize by jsonencode
% jdatadecode(loadjson(savejson('',encodedtoeplitz))) % serialize by savejson
%
% license:
% BSD or GPL version 3, see LICENSE_{BSD,GPLv3}.txt files for details
%
% -- this function is part of JSONLab toolbox (http://neurojson.org/jsonlab)
%
if (nargin == 0)
help jdataencode;
return
end
opt = varargin2struct(varargin{:});
if (isoctavemesh)
opt.prefix = jsonopt('Prefix', '', opt);
else
opt.prefix = jsonopt('Prefix', sprintf('x0x%X', '_' + 0), opt);
end
opt.compression = jsonopt('Compression', '', opt);
opt.nestarray = jsonopt('NestArray', 0, opt);
opt.formatversion = jsonopt('FormatVersion', 2, opt);
opt.compressarraysize = jsonopt('CompressArraySize', 300, opt);
opt.base64 = jsonopt('Base64', 0, opt);
opt.mapasstruct = jsonopt('MapAsStruct', 0, opt);
opt.usearrayzipsize = jsonopt('UseArrayZipSize', 1, opt);
opt.messagepack = jsonopt('MessagePack', 0, opt);
opt.usearrayshape = jsonopt('UseArrayShape', 0, opt) && exist('bandwidth');
opt.annotatearray = jsonopt('AnnotateArray', 0, opt);
opt.datetime = jsonopt('DateTime', 1, opt);
opt.arraychunks = jsonopt('ArrayChunks', [], opt);
% Performance optimization: pre-compute prefixed field names to avoid
% repeated string concatenation in hot loops
opt.N_ArrayType = [opt.prefix '_ArrayType_'];
opt.N_ArraySize = [opt.prefix '_ArraySize_'];
opt.N_ArrayData = [opt.prefix '_ArrayData_'];
opt.N_ArrayLabel = [opt.prefix '_ArrayLabel_'];
opt.N_ArrayZipSize = [opt.prefix '_ArrayZipSize_'];
opt.N_ArrayZipType = [opt.prefix '_ArrayZipType_'];
opt.N_ArrayZipData = [opt.prefix '_ArrayZipData_'];
opt.N_ArrayIsComplex = [opt.prefix '_ArrayIsComplex_'];
opt.N_ArrayIsSparse = [opt.prefix '_ArrayIsSparse_'];
opt.N_ArrayShape = [opt.prefix '_ArrayShape_'];
opt.N_TableCols = [opt.prefix '_TableCols_'];
opt.N_TableRows = [opt.prefix '_TableRows_'];
opt.N_TableRecords = [opt.prefix '_TableRecords_'];
opt.N_GraphNodes = [opt.prefix '_GraphNodes_'];
opt.N_GraphEdges = [opt.prefix '_GraphEdges_'];
opt.N_DataInfo = [opt.prefix '_DataInfo_'];
opt.N_ByteStream = [opt.prefix '_ByteStream_'];
opt.N_MapData = [opt.prefix '_MapData_'];
opt.N_ArrayCoords = [opt.prefix '_ArrayCoords_'];
opt.N_ArrayUnits = [opt.prefix '_ArrayUnits_'];
opt.N_ArrayFillValue = [opt.prefix '_ArrayFillValue_'];
opt.N_ArrayChunks = [opt.prefix '_ArrayChunks_'];
opt.N_TableIndex = [opt.prefix '_TableIndex_'];
opt.N_TableSortOrder = [opt.prefix '_TableSortOrder_'];
opt.N_DataSchema = [opt.prefix '_DataSchema_'];
opt.N_EnumKey = [opt.prefix '_EnumKey_'];
opt.N_EnumValue = [opt.prefix '_EnumValue_'];
opt.N_EnumOrdered = [opt.prefix '_EnumOrdered_'];
jdata = obj2jd(data, opt);
%% -------------------------------------------------------------------------
function newitem = obj2jd(item, opt)
newitem = item;
if (iscell(item))
newitem = cell2jd(item, opt);
elseif (isa(item, 'jdict'))
if (isempty(item{'kind'}))
newitem = obj2jd(item(), opt);
end
elseif (isstruct(item))
newitem = struct2jd(item, opt);
elseif (isnumeric(item) || islogical(item) || isa(item, 'timeseries'))
newitem = mat2jd(item, opt);
elseif (ischar(item) || isa(item, 'string'))
newitem = mat2jd(item, opt);
elseif (isa(item, 'containers.Map') || isa(item, 'dictionary'))
newitem = map2jd(item, opt);
elseif (isa(item, 'categorical'))
if (opt.formatversion >= 4)
newitem = cat2jd(item, opt);
else
newitem = cell2jd(cellstr(item), opt);
end
elseif (isa(item, 'function_handle'))
newitem = struct2jd(functions(item), opt);
elseif (isa(item, 'table'))
newitem = table2jd(item, opt);
elseif (isa(item, 'digraph') || isa(item, 'graph'))
newitem = graph2jd(item, opt);
elseif (isobject(item))
newitem = matlabobject2jd(item, opt);
end
if (isa(item, 'jdict') && isempty(item{'kind'})) % apply attribute and schema
attrpath = item.getattr();
if (~isempty(attrpath))
newitem = jdict(newitem);
for i = 1:length(attrpath)
val = newitem.(attrpath{i}).v();
if (isempty(val))
continue
end
if (isnumeric(val))
opt.annotatearray = 1;
val = mat2jd(val, opt);
end
if (~(isstruct(val) && isfield(val, opt.N_ArrayType)))
continue
end
attr = item.getattr(attrpath{i});
attrname = keys(attr);
for j = 1:length(attrname)
if (strcmp(attrname{j}, 'dims'))
val.(opt.N_ArrayLabel) = attr(attrname{j});
elseif (opt.formatversion >= 4 && strcmp(attrname{j}, 'coords'))
val.(opt.N_ArrayCoords) = attr(attrname{j});
elseif (opt.formatversion >= 4 && strcmp(attrname{j}, 'units'))
val.(opt.N_ArrayUnits) = attr(attrname{j});
elseif (opt.formatversion >= 4 && strcmp(attrname{j}, 'fillvalue'))
val.(opt.N_ArrayFillValue) = attr(attrname{j});
else
val.(attrname{j}) = attr(attrname{j});
end
newitem.(attrpath{i}) = val;
end
end
end
% emit _DataSchema_ when schema is set and format >= 4
itemschema = item.getschema();
if (opt.formatversion >= 4 && ~isempty(itemschema))
if (isa(newitem, 'jdict') && isstruct(newitem.data))
newitem.data.(opt.N_DataSchema) = itemschema;
elseif (isstruct(newitem))
newitem.(opt.N_DataSchema) = itemschema;
end
end
end
%% -------------------------------------------------------------------------
function newitem = cell2jd(item, opt)
% Optimization: direct loop instead of cellfun for better performance
n = numel(item);
newitem = cell(size(item));
for i = 1:n
newitem{i} = obj2jd(item{i}, opt);
end
%% -------------------------------------------------------------------------
function newitem = struct2jd(item, opt)
num = numel(item);
if (num > 1) % struct array
newitem = obj2jd(num2cell(item), opt);
try
newitem = cell2mat(newitem);
catch
end
elseif (num == 1) % a single struct
names = fieldnames(item);
newitem = struct;
for i = 1:length(names)
newitem.(names{i}) = obj2jd(item.(names{i}), opt);
end
else
newitem = item;
end
%% -------------------------------------------------------------------------
function newitem = map2jd(item, opt)
names = item.keys;
if (opt.mapasstruct) % convert a map to struct
newitem = struct;
if (~strcmp(item.KeyType, 'char'))
data = num2cell(reshape([names, item.values], length(names), 2), 2);
for i = 1:length(names)
data{i}{2} = obj2jd(data{i}{2}, opt);
end
newitem.(opt.N_MapData) = data;
else
for i = 1:length(names)
newitem.([opt.prefix names{i}]) = obj2jd(item(names{i}), opt);
end
end
else % keep as a map and only encode its values
if (isa(item, 'dictionary'))
newitem = dictionary();
elseif (strcmp(item.KeyType, 'char'))
newitem = containers.Map();
else
newitem = containers.Map('KeyType', item.KeyType, 'ValueType', 'any');
end
if (isa(item, 'dictionary'))
for i = 1:length(names)
newitem(names(i)) = obj2jd(item(names(i)), opt);
end
else
for i = 1:length(names)
newitem(names{i}) = obj2jd(item(names{i}), opt);
end
end
end
%% -------------------------------------------------------------------------
function newitem = mat2jd(item, opt)
zipmethod = opt.compression;
minsize = opt.compressarraysize;
if (isa(item, 'timeseries'))
if (item.TimeInfo.isUniform && item.TimeInfo.Increment == 1)
if (ndims(item.Data) == 3 && size(item.Data, 1) == 1 && size(item.Data, 2) == 1)
item = permute(item.Data, [2 3 1]);
else
item = squeeze(item.Data);
end
else
item = [item.Time squeeze(item.Data)];
end
end
% Range encoding for uniformly-spaced vectors (FormatVersion >= 4)
if (opt.formatversion >= 4 && opt.usearrayshape && isvector(item) && isreal(item) && ...
~issparse(item) && numel(item) >= 3 && ~opt.nestarray && (isempty(zipmethod) || numel(item) <= minsize) && ~opt.annotatearray)
dv = diff(double(item(:)));
tol = 4 * eps(max(abs(double(item(:)))) + 1) * numel(item);
if (all(abs(dv - dv(1)) <= tol))
newitem = struct(opt.N_ArrayType, class(item), opt.N_ArraySize, size(item));
newitem.(opt.N_ArrayShape) = 'range';
newitem.(opt.N_ArrayData) = [double(item(1)), double(item(end))];
return
end
end
% FAST PATH: Early exit for simple cases that don't need encoding
% Check usearrayshape condition: only skip if shape encoding won't apply
skipshape = ~opt.usearrayshape || ndims(item) ~= 2 || isvector(item);
if (skipshape && ...
(isempty(item) || isa(item, 'string') || ischar(item) || opt.nestarray || ...
((isvector(item) || ndims(item) == 2) && isreal(item) && ~issparse(item) && ...
~opt.annotatearray && (isempty(zipmethod) || numel(item) <= minsize))))
newitem = item;
return
end
% Need full encoding - build struct with pre-computed field names
newitem = struct(opt.N_ArrayType, class(item), opt.N_ArraySize, size(item));
% 2d numerical (real/complex/sparse) arrays with _ArrayShape_ encoding enabled
if (opt.usearrayshape && ndims(item) == 2 && ~isvector(item))
encoded = 1;
% Zero and identity shapes (FormatVersion >= 4 only)
if (opt.formatversion >= 4 && isreal(item) && ~issparse(item))
if (~any(item(:))) % all-zero matrix
newitem.(opt.N_ArrayShape) = 'zero';
newitem.(opt.N_ArrayData) = 0;
return
end
if (size(item, 1) == size(item, 2) && size(item, 1) > 0)
d = diag(item);
if (all(d == d(1)) && ~any(any(item - diag(d)))) % scaled identity
newitem.(opt.N_ArrayShape) = 'identity';
newitem.(opt.N_ArrayData) = double(d(1));
return
end
end
end
if (~isreal(item))
newitem.(opt.N_ArrayIsComplex) = true;
end
symmtag = '';
if (isreal(item) && issymmetric(double(item)))
symmtag = 'symm';
item = tril(item);
elseif (~isreal(item) && ishermitian(double(item)))
symmtag = 'herm';
item = tril(item);
end
[lband, uband] = bandwidth(double(item));
newitem.(opt.N_ArrayZipSize) = [lband + uband + 1, min(size(item, 1), size(item, 2))];
if (lband + uband == 0) % isdiag
newitem.(opt.N_ArrayShape) = 'diag';
newitem.(opt.N_ArrayData) = diag(item).';
elseif (uband == 0 && lband == size(item, 1) - 1) % lower triangular
newitem.(opt.N_ArrayShape) = ['lower' symmtag];
item = item.';
newitem.(opt.N_ArrayData) = item(triu(true(size(item)))).';
elseif (lband == 0 && uband == size(item, 2) - 1) % upper triangular
newitem.(opt.N_ArrayShape) = 'upper';
item = item.';
newitem.(opt.N_ArrayData) = item(tril(true(size(item)))).';
elseif (lband == 0) % upper band
newitem.(opt.N_ArrayShape) = {'upperband', uband};
newitem.(opt.N_ArrayData) = spdiags(item.', -uband:lband).';
elseif (uband == 0) % lower band
newitem.(opt.N_ArrayShape) = {sprintf('lower%sband', symmtag), lband};
newitem.(opt.N_ArrayData) = spdiags(item.', -uband:lband).';
elseif (uband < size(item, 2) - 1 || lband < size(item, 1) - 1) % band
newitem.(opt.N_ArrayShape) = {'band', uband, lband};
newitem.(opt.N_ArrayData) = spdiags(item.', -uband:lband).';
elseif (all(toeplitz(item(:, 1), item(1, :)) == item)) % Toeplitz matrix
newitem.(opt.N_ArrayShape) = 'toeplitz';
newitem.(opt.N_ArrayZipSize) = [2, max(size(item))];
newitem.(opt.N_ArrayData) = zeros(2, max(size(item)));
newitem.(opt.N_ArrayData)(1, 1:size(item, 2)) = item(1, :);
newitem.(opt.N_ArrayData)(2, 1:size(item, 1)) = item(:, 1).';
else % full matrix
newitem = rmfield(newitem, opt.N_ArrayZipSize);
encoded = 0;
end
% serialize complex data at last
if (encoded && isstruct(newitem) && ~isreal(newitem.(opt.N_ArrayData)))
arrdata = newitem.(opt.N_ArrayData);
item2 = squeeze(zeros([2, size(arrdata)]));
item2(1, :) = real(arrdata(:));
item2(2, :) = imag(arrdata(:));
newitem.(opt.N_ArrayZipSize) = size(item2);
newitem.(opt.N_ArrayData) = item2;
end
% wrap _ArrayData_ into a single row vector
if (encoded)
if (isstruct(newitem) && ~isvector(newitem.(opt.N_ArrayData)))
arrdata = newitem.(opt.N_ArrayData);
arrdata = permute(arrdata, ndims(arrdata):-1:1);
newitem.(opt.N_ArrayData) = arrdata(:).';
else
newitem = rmfield(newitem, opt.N_ArrayZipSize);
end
newitem.(opt.N_ArrayData) = full(newitem.(opt.N_ArrayData));
return
end
end
% no encoding for char arrays or non-sparse real vectors (already handled struct creation above)
if (isempty(item) || isa(item, 'string') || ischar(item) || opt.nestarray || ...
((isvector(item) || ndims(item) == 2) && isreal(item) && ~issparse(item) && ...
~opt.annotatearray))
newitem = item;
return
end
if (isa(item, 'logical'))
item = uint8(item);
end
if (isreal(item))
if (issparse(item))
fulldata = full(item(item ~= 0));
newitem.(opt.N_ArrayIsSparse) = true;
newitem.(opt.N_ArrayZipSize) = [2 + (~isvector(item)), length(fulldata)];
if (isvector(item))
newitem.(opt.N_ArrayData) = [find(item(:))', fulldata(:)'];
else
[ix, iy] = find(item);
newitem.(opt.N_ArrayData) = [ix(:)', iy(:)', fulldata(:)'];
end
else
if (opt.formatversion > 1.9)
item = permute(item, ndims(item):-1:1);
end
newitem.(opt.N_ArrayData) = item(:)';
end
else
newitem.(opt.N_ArrayIsComplex) = true;
if (issparse(item))
fulldata = full(item(item ~= 0));
newitem.(opt.N_ArrayIsSparse) = true;
newitem.(opt.N_ArrayZipSize) = [3 + (~isvector(item)), length(fulldata)];
if (isvector(item))
newitem.(opt.N_ArrayData) = [find(item(:))', real(fulldata(:))', imag(fulldata(:))'];
else
[ix, iy] = find(item);
newitem.(opt.N_ArrayData) = [ix(:)', iy(:)', real(fulldata(:))', imag(fulldata(:))'];
end
else
if (opt.formatversion > 1.9)
item = permute(item, ndims(item):-1:1);
end
newitem.(opt.N_ArrayZipSize) = [2, numel(item)];
newitem.(opt.N_ArrayData) = [real(item(:))', imag(item(:))'];
end
end
if (opt.usearrayzipsize == 0 && isfield(newitem, opt.N_ArrayZipSize))
data = newitem.(opt.N_ArrayData);
data = reshape(data, fliplr(newitem.(opt.N_ArrayZipSize)));
newitem.(opt.N_ArrayData) = permute(data, ndims(data):-1:1);
newitem = rmfield(newitem, opt.N_ArrayZipSize);
end
if (~isempty(zipmethod) && numel(item) > minsize)
encodeparam = {};
if (~isempty(regexp(zipmethod, '^blosc2', 'once')))
compfun = @blosc2encode;
encodeparam = {zipmethod, 'nthread', jsonopt('nthread', 1, opt), ...
'shuffle', jsonopt('shuffle', 1, opt), ...
'typesize', jsonopt('typesize', length(typecast(item(1), 'uint8')), opt)};
else
compfun = str2func([zipmethod 'encode']);
end
newitem.(opt.N_ArrayZipType) = lower(zipmethod);
if (opt.formatversion >= 4 && ~isempty(opt.arraychunks) && isfield(newitem, opt.N_ArrayData))
chunkshape = double(opt.arraychunks(:)');
if (numel(chunkshape) == 1)
% 1-D chunking: split flat pre-processed buffer into equal segments
arrdata = newitem.(opt.N_ArrayData)(:)';
chunklen = chunkshape;
nelem = numel(arrdata);
nchunks = ceil(nelem / chunklen);
zipchunks = cell(1, nchunks);
for ci = 1:nchunks
startidx = (ci - 1) * chunklen + 1;
endidx = min(ci * chunklen, nelem);
chunk = arrdata(startidx:endidx);
zipchunks{ci} = compfun(typecast(chunk, 'uint8'), encodeparam{:});
if (opt.base64)
zipchunks{ci} = char(base64encode(zipchunks{ci}));
end
end
newitem.(opt.N_ArrayChunks) = [chunklen];
if (~isfield(newitem, opt.N_ArrayZipSize))
newitem.(opt.N_ArrayZipSize) = nelem;
end
newitem.(opt.N_ArrayZipData) = zipchunks;
else
% N-D chunking: two modes depending on array type.
% Dense (real/complex): _ArrayChunks_ is in _ArraySize_ (original) order;
% tiles the row-major permuted intermediate; _ArrayZipSize_ = [1/2, N_total].
% Non-dense (sparse/shaped): _ArrayChunks_ is in _ArrayZipSize_ (processed) order;
% tiles the processed _ArrayData_ matrix directly.
isdense_chunk = ~(isfield(newitem, opt.N_ArrayIsSparse) && newitem.(opt.N_ArrayIsSparse)) && ~isfield(newitem, opt.N_ArrayShape);
if (isdense_chunk)
% Dense: item is already permuted to row-major.
% chunkshape is in original _ArraySize_ order; flip to get permuted-space chunks.
origsize = fliplr(size(item)); % original _ArraySize_ dimension order
ndim = numel(origsize);
chunkshape(end + 1:ndim) = origsize(numel(chunkshape) + 1:end);
chunkshape = min(chunkshape, origsize); % clamp against original dims
chunkshape_perm = fliplr(chunkshape); % chunk shape in permuted space
arrsize_perm = size(item);
nchunks_nd = ceil(arrsize_perm ./ chunkshape_perm);
zipchunks = cell(1, prod(nchunks_nd));
tidx = cell(1, ndim);
for ci = 1:prod(nchunks_nd)
[tidx{:}] = ind2sub(nchunks_nd, ci);
ranges = cell(1, ndim);
for d = 1:ndim
r1 = (tidx{d} - 1) * chunkshape_perm(d) + 1;
r2 = min(tidx{d} * chunkshape_perm(d), arrsize_perm(d));
ranges{d} = r1:r2;
end
chunk = item(ranges{:});
if (~isreal(item))
chunk_flat = [real(chunk(:))', imag(chunk(:))'];
zipchunks{ci} = compfun(typecast(chunk_flat, 'uint8'), encodeparam{:});
else
zipchunks{ci} = compfun(typecast(chunk(:)', 'uint8'), encodeparam{:});
end
if (opt.base64)
zipchunks{ci} = char(base64encode(zipchunks{ci}));
end
end
newitem.(opt.N_ArrayChunks) = chunkshape; % stored in original _ArraySize_ order
if (~isreal(item))
newitem.(opt.N_ArrayZipSize) = [2, numel(item)];
else
newitem.(opt.N_ArrayZipSize) = [1, numel(item)];
end
else
% Non-dense (sparse/shaped): tile the logical _ArrayZipSize_ shape.
% _ArrayData_ is stored as a flat row; reshape to _ArrayZipSize_ for tiling.
% _ArrayChunks_ is in _ArrayZipSize_ coordinate space.
procsize = double(newitem.(opt.N_ArrayZipSize)); % e.g. [3,K] for sparse
ndim = numel(procsize);
chunkshape(end + 1:ndim) = procsize(numel(chunkshape) + 1:end);
chunkshape = min(chunkshape, procsize);
nchunks_nd = ceil(procsize ./ chunkshape);
% Reshape flat _ArrayData_ row to logical procsize (inverse of rows-concatenated)
flatdata = newitem.(opt.N_ArrayData)(:)';
procdata = permute(reshape(flatdata, fliplr(procsize)), ndim:-1:1);
zipchunks = cell(1, prod(nchunks_nd));
tidx = cell(1, ndim);
for ci = 1:prod(nchunks_nd)
[tidx{:}] = ind2sub(nchunks_nd, ci);
ranges = cell(1, ndim);
for d = 1:ndim
r1 = (tidx{d} - 1) * chunkshape(d) + 1;
r2 = min(tidx{d} * chunkshape(d), procsize(d));
ranges{d} = r1:r2;
end
chunk = procdata(ranges{:});
zipchunks{ci} = compfun(typecast(chunk(:)', 'uint8'), encodeparam{:});
if (opt.base64)
zipchunks{ci} = char(base64encode(zipchunks{ci}));
end
end
newitem.(opt.N_ArrayChunks) = chunkshape;
% _ArrayZipSize_ was already set by the sparse/shaped encoding path; keep it.
end
newitem.(opt.N_ArrayZipData) = zipchunks;
end
newitem = rmfield(newitem, opt.N_ArrayData);
else
if (~isfield(newitem, opt.N_ArrayZipSize))
newitem.(opt.N_ArrayZipSize) = size(newitem.(opt.N_ArrayData));
end
newitem.(opt.N_ArrayZipData) = compfun(typecast(newitem.(opt.N_ArrayData)(:).', 'uint8'), encodeparam{:});
newitem = rmfield(newitem, opt.N_ArrayData);
if (opt.base64)
newitem.(opt.N_ArrayZipData) = char(base64encode(newitem.(opt.N_ArrayZipData)));
end
end
end
if (isfield(newitem, opt.N_ArrayData) && isempty(newitem.(opt.N_ArrayData)))
newitem.(opt.N_ArrayData) = [];
end
%% -------------------------------------------------------------------------
function newitem = cat2jd(item, opt)
% Encode a MATLAB categorical array as a JData enumeration (Draft 4)
keys = reshape(categories(item), 1, []); % ensure 1xN row for flat JSON array
vals = uint32(item); % 1-based codes; 0 for <undefined>
newitem = struct(opt.N_EnumKey, {keys}, opt.N_EnumValue, vals);
if (isordinal(item))
newitem.(opt.N_EnumOrdered) = true;
end
%% -------------------------------------------------------------------------
function newitem = table2jd(item, opt)
newitem = struct;
newitem.(opt.N_TableCols) = item.Properties.VariableNames;
newitem.(opt.N_TableRows) = item.Properties.RowNames';
newitem.(opt.N_TableRecords) = table2cell(item);
if (opt.formatversion >= 4 && ~isempty(item.Properties.UserData) && isstruct(item.Properties.UserData))
if (isfield(item.Properties.UserData, 'TableIndex'))
newitem.(opt.N_TableIndex) = item.Properties.UserData.TableIndex;
end
if (isfield(item.Properties.UserData, 'TableSortOrder'))
newitem.(opt.N_TableSortOrder) = item.Properties.UserData.TableSortOrder;
end
end
%% -------------------------------------------------------------------------
function newitem = graph2jd(item, opt)
newitem = struct;
nodedata = table2struct(item.Nodes);
if (isfield(nodedata, 'Name'))
nodedata = rmfield(nodedata, 'Name');
newitem.(opt.N_GraphNodes) = containers.Map(item.Nodes.Name, num2cell(nodedata), 'UniformValues', false);
else
newitem.(opt.N_GraphNodes) = containers.Map(1:max(item.Edges.EndNodes(:)), num2cell(nodedata), 'UniformValues', false);
end
edgenodes = num2cell(item.Edges.EndNodes);
edgedata = table2struct(item.Edges);
if (isfield(edgedata, 'EndNodes'))
edgedata = rmfield(edgedata, 'EndNodes');
end
edgenodes(:, 3) = num2cell(edgedata);
if (isa(item, 'graph'))
if (strcmp(opt.prefix, 'x'))
newitem.(genvarname('_GraphEdges0_')) = edgenodes;
else
newitem.(encodevarname('_GraphEdges0_')) = edgenodes;
end
else
newitem.(opt.N_GraphEdges) = edgenodes;
end
%% -------------------------------------------------------------------------
function newitem = matlabobject2jd(item, opt)
if (~opt.datetime && (isa(item, 'datetime') || isa(item, 'duration')))
newitem = item;
return
end
try
if numel(item) == 0 % empty object
newitem = struct();
elseif numel(item) == 1 %
newitem = char(item);
else
propertynames = properties(item);
for p = 1:numel(propertynames)
for o = numel(item):-1:1 % array of objects
newitem(o).(propertynames{p}) = item(o).(propertynames{p});
end
end
end
catch
newitem = any2jd(item, opt);
end
%% -------------------------------------------------------------------------
function newitem = any2jd(item, opt)
try
newitem.(opt.N_DataInfo) = struct('MATLABObjectClass', class(item), 'MATLABObjectSize', size(item));
newitem.(opt.N_ByteStream) = getByteStreamFromArray(item); % use undocumented matlab function
if (opt.base64)
newitem.(opt.N_ByteStream) = char(base64encode(newitem.(opt.N_ByteStream)));
end
catch
error('any2jd: failed to convert object of type %s', class(item));
end
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