jdict

Universal Dictionary Interface for MATLAB/Octave

A powerful, xarray-inspired data structure that combines the flexibility of Python dictionaries with MATLAB's numerical computing power. Navigate complex hierarchical data effortlessly.

🚀 Unified Access

Works seamlessly with structs, containers.Map, and dictionaries

🎯 JSONPath Support

Query nested data with powerful JSONPath expressions

🔬 Dimension Labels

xarray-like dimension indexing for scientific data

⚡ Attribute Storage

Attach metadata to any level of your data hierarchy

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Getting Started

Simple, Intuitive Syntax

Access nested data structures with clean, readable syntax. No more deep struct navigation!

% Create a complex nested structure
data = struct('experiments', ...
    struct('trial1', rand(100, 64), 'metadata', ...
        struct('date', '2025-01-01', 'subject', 'P001')));

jd = jdict(data);

% Clean navigation
subject = jd.('experiments').('trial1').('metadata').('subject')()

% Or use JSONPath for deep queries
subject = jd.('$.experiments.trial1.metadata.subject')()

Metadata Management

Attach Attributes Anywhere

Store metadata like dimension names, units, and descriptions alongside your data. Perfect for scientific datasets!

% Create EEG data
eeg_data = rand(1000, 64, 50);  % time x channels x trials
jd = jdict(eeg_data);

% Attach metadata using curly braces (MATLAB)
jd{'dims'} = {'time', 'channels', 'trials'};
jd{'units'} = 'microvolts';
jd{'sampling_rate'} = 1000;

% Read attributes
dims = jd{'dims'}          % → {'time', 'channels', 'trials'}
units = jd{'units'}        % → 'microvolts'

xarray-Inspired

Dimension-Based Selection

Select data using dimension names instead of numeric indices. Make your code self-documenting!

% Setup multidimensional data
jd = jdict(rand(1000, 64, 50));
jd{'dims'} = {'time', 'channels', 'trials'};

% Select by dimension name - so much clearer!
first_100 = jd.time(1:100);           % First 100 timepoints
channel_5 = jd.channels(5);          % Just channel 5
subset = jd.time(1:500).trials(1:10);  % Chain selections!

% No more confusing: data(1:100, :, :) vs data(:, 5, :) ✨

Advanced Features

Smart Struct Array Handling

Combine dimension selection with struct field access. Get data from multiple struct elements at once!

% Create 2x3 struct array
sa = repmat(struct('name', 'exp', 'data', rand(10, 5)), 2, 3);
jd = jdict(sa);
jd{'dims'} = {'experiments', 'conditions'};

% Select row 1, then get all names
names = jd.experiments(1).('name')();
% → {'exp', 'exp', 'exp'} from all 3 structs in row 1

% Access specific element's data
data_point = jd.experiments(1).('data')(2);
% → data from 2nd element in row 1

Powerful Queries

JSONPath Deep Search

Use JSONPath expressions to query complex nested structures. Find data anywhere in your hierarchy!

% Load complex REST API data
jd = jdict('https://api.example.com/data.json');

% Deep search with '..' operator
all_names = jd.('$..name')();      % Find all 'name' fields
all_ids = jd.('$..id')();          % Find all 'id' fields

% Array indexing in path
first = jd.('$.results[0].data')();

% Combine with attribute access
jd.('$.results[0]'){'processed'} = true;

Flexible Metadata

Attributes at Any Level

Attach different metadata to different parts of your data structure. Each subfield can have its own attributes!

% Complex neuroimaging dataset
jd = jdict(struct('eeg', rand(1000,64), 'fmri', rand(100,100,50)));

% Different dims for different data types
jd.('eeg'){'dims'} = {'time', 'channels'};
jd.('eeg'){'units'} = 'uV';

jd.('fmri'){'dims'} = {'x', 'y', 'z'};
jd.('fmri'){'units'} = 'BOLD';

% Use dimension names for each modality
eeg_snippet = jd.('eeg').time(1:100);
fmri_slice = jd.('fmri').z(25);

Ready to Use

Get Started with jdict

Part of the JSONLab toolbox — one of MATLAB's most popular JSON parsers.

Installation

% Download JSONLab from GitHub
git clone https://github.com/NeuroJSON/jsonlab.git

% Add to MATLAB path
addpath('/path/to/jsonlab');

Key Methods

jd.('key') — Navigate

jd{'attr'} — Get/set attributes

jd.dim(indices) — Select by dimension

jd.tojson() — Export to JSON

jd.keys() — List subfields

jd.v(i) — Array indexing

Created by Qianqian Fang • Part of the NeuroJSON Project