MATLAB SDK

Get data from Marple DB in MATLAB

MATLAB allows for complex custom analysis on data that is stored in Marple DB

Installation

1. The SDK is currently shipped as a DB.m file, get the latest file in the Marple SDK Gitlab repository.

2. Place DB.m in your current directory, or in a directory that is on your MATLAB path so that it's reachable by your scripts.

3. Create a file config.json in the same directory as the DB.m script. Add the following values:

{
    // might be a different URL if you have self-hosted or VPC Marple
    "api_url": "https://db.marpledata.com/api/v1",
    "api_key": "<your-api-token-here>",
    "workspace": "<name of your workspace>"
}

Usage

% create a DB object
mdb = DB.from_config();

% list data streams
mdb.get_streams()

% list files for a data stream, also contains metadata
mdb.get_datasets('flight data')

% get time series data
mdb.get_data('2025-11-04-cessna-looping-test.tdms', 'altitude')

mdb.get_data will always return a MATLAB table with one column time and one column with the name of the signal requested. The time is always given in nanoseconds, and as a UTC timestamp if the original data was datetimes.

If you want to combine multiple tables, it might require resampling of your signals to be able to combine them into calculations or visualisations. You can use built-in MATLAB functions to do this:

data = mdb.get_data('2025-11-04-cessna-looping-test.tdms', 'altitude')

% resample signal
t1 = datetime(data.time/1e9, 'ConvertFrom','posixtime', 'TimeZone','UTC');
TT = table2timetable(table(data, data.altitude, 'VariableNames', {'time', 'altitude'}), 'RowTimes','time');
TT_altitude = retime(TT, 'regular', 'nearest', 'TimeStep', seconds(2));

% combine into one big table
TT_all_data = synchronize(TT_altitude, TT_speed, TT_battery, 'union', 'nearest');

Caching

The MATLAB SDK builds up automatic caching of data that you have used in previous runs of your script. On each run, the SDK will check if it needs the fetch the data from Marple DB or not. This speeds up your scripts a lot if you are doing similar analysis multiple times, or if you are continuously tweaking a script.

The cache files are located at cold/ in your current working directory.

Roadmap

The following features are planned for Q1 2026:

• Resampling for multiple signals at once

• Uploading data

• Easy filtering on file metadata

Future roadmap:

• Loading multiple parquet files at once (for very large signals)

• Configuring caching strategy