SNIRF Sample Data Files

• License: All files in this package are in the public domain
• SNIRF Specification: https://github.com/fNIRS/snirf
• URL: https://github.com/fNIRS/snirf-samples

Table of content

• How to read sample data files
  • Using data in Python
    • SNIRF files
    • JSNIRF/JSON files
    • Binary JSNIRF files
    • How to access individual data records in Python
  • Using data in MATLAB/Octave
    • SNIRF files
    • JSNIRF/JSON files
    • Binary JSNIRF files
    • How to access individual data records in MATLAB
• Contribute to this project

Overview

This repository provides sample data files to facilitate software developers adopting and testing supports for Shared Near Infrared File Format (SNIRF) v1.0 files.

This repository contains 3 types of sample data files

• The SNIRF data files (.snirf) are actually HDF5 files and can be read and write in most software platforms where HDF5 is supported. The datasets in the .snirf files are set so that data field creation order are preserved. One must use approprate HDF5 reading API to retrieve the field creation order.

• The text-based JSNIRF data files (.jnirs) are actually JSON files with JData-compliant annotations. The JSNIRF/JSON files are broadly supported, including platforms where HDF5 is not avaible such as GNU Octave or MATLAB older than R2011a. The .jnirs files can be opened by a text editor and are directly human-readable.

• The binary JSNIRF data files (.bnirs) are actually BJData/UBJSON files with JData-compliant annotations. The BJData/UBJSON files are binary JSON files that provides smaller file sizes and fast parsing.

How to read sample data files

Using data in Python

To load the data in python, one can use the below sample codes

SNIRF files

The .snirf files are simply renamed HDF5 (.h5) files and thus can be read/written by the python-h5py module. To read/write .snirf files, one need to install the below software packages (on Debian/Ubuntu)

sudo apt-get install python-h5py python-numpy

Once these tools are installed, one can start python and run

import h5py
import numpy as np

dat=h5py.File('datafile.snirf','r')
d1=np.array(dat.get('/nirs/data1/dataTimeSeries'));

JSNIRF/JSON files

To read the text JSNIRF files (.jnirs, which is a valid JSON file), one needs to install the jdata module via

pip install jdata --user

then open python, and run

import jdata as jd
from collections import OrderedDict
data=jd.loadt('datafile.jnirs',object_pairs_hook=OrderedDict);

to load the JSNIRF/JSON (.jnirs) file. The output data is a dict object containing the full SNIRF data structure.

Binary JSNIRF files

To read the binary JSNIRF files (.bnirs), one needs to install the bjdata module in addition to jdata

pip install jdata --user
pip install bjdata --user

and then load the binary jdata file using

import jdata as jd
import bjdata
from collections import OrderedDict

data=jd.loadb('datafile.bnirs',object_pairs_hook=OrderedDict);

Both bjdata and jdata moduels can be installed on Debian Bullseye and Ubuntu 21.04 or newer via

sudo apt-get install python3-jdata python3-bjdata

For Ubuntu 14.04-20.04, please use the following PPA:

sudo add-apt-repository ppa:fangq/ppa
sudo apt-get update
sudo apt-get install python3-jdata python3-bjdata

How to access individual data records in Python

Once the data is loaded in Python, the full data structured is typically stored as a nested dict object. One can access the individual subfields via python's standard object indexing and reference methods. For example,

  data['formatVersion']   # this prints the formatVersion subfield in the top level
  data['data1']['dataTimeSeries']      # retrieve the data array as an numpy array
  data['metadataTags']['SubjectID']    # print the SubjectID in the metadataTags field

Using data in MATLAB/Octave

To load the data in MATLAB/Octave, one can use the below sample codes

SNIRF files

The .snirf files can be loaded using

• MATLAB's HDF5 API
• EasyH5 toolbox with JSNIRFY toolbox
• snirf_homer3 toolbox

Once these tools are installed, one can start MATLAB and run

data=loadsnirf('datafile.snirf');

JSNIRF/JSON files

The .jnirs files can be loaded using

• MATLAB's jsondecode function with jdatadecode in JSONLab toolbox and ZMat toolbox or
• JSNIRFY toolbox combined with
• JSONLab toolbox and
• ZMat toolbox (not needed for MATLAB but required for Octave)

Once these tools are installed, one can start MATLAB and run

data=loadjsnirf('datafile.jnirs');

Binary JSNIRF files

The .bnirs files can be loaded using

• JSNIRFY toolbox combined with
• JSONLab toolbox and
• ZMat toolbox (not needed for MATLAB but required for Octave)

and then load the binary jdata file using

data=loadjsnirf('datafile.bnirs');

How to access individual data records in MATLAB

Once the data is loaded in Python, the full data structured is typically stored as a nested dict object. One can access the individual subfields via python's standard object indexing and reference methods. For example,

  data.formatVersion   % this prints the formatVersion subfield in the top level
  data.data{1}.dataTimeSeries    % retrieve the data array
  data.metadataTags.SubjectID    % print the SubjectID in the metadataTags field

Contribute to this project

Please submit your bug reports, feature requests and questions to the Github Issues page at

https://github.com/fNIRS/snirf/issues

Please feel free to fork our software, making changes, and submit your revision back to us via "Pull Requests".