Resources on LabBench, Version 6.0

Development tools

Mon, 01 Jan 0001 00:00:00 +0000

For writing Experiment Definition Files, as well as Python scripts, an Integrated Development Environment (IDE) makes the process significantly easier due to code completion. Code completion means that you do not need to remember which XML elements or attributes are valid at a given place in your definition file. Instead, tap CTRL + SPACE (for Visual Studio Code) to get a list of valid elements or attributes Figure 1. Code completion also reduces the need for manual typing, as you can simply select the required element or attribute and then press enter, at which point the IDE will complete the code for you without requiring typing.

Introduction to XML

Mon, 01 Jan 0001 00:00:00 +0000

The Experiment Definition File is a plain text file, which means any text editor can be used. The format for these files is based on the eXtensible Markup Language (XML), a general-purpose markup language that can be used to implement domain specific languages, such as in this case the LabBench Language for Experiment Definition Files.

The code listing below provides the structure of an Experiment Definition File:

<?xml version="1.0" encoding="UTF-8"?>
<experiment xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
            xsi:noNamespaceSchemaLocation="https://files.labbench.io/xsd/6.0/experiment.xsd">
    <participant-validator regex="^S[0-9]{3}$|^TEST[0-9]{3}$"
                       advice="Please enter an ID in the form of SXXX, where X is a digit" />
    <experimental-setup-variants default="SetupID">
        <experimental-setup id="SetupID" name="Name of experimental setup">
            <devices>
                <!--  Please see page Experimental Setups -->
            </devices>
            <device-mapping>
                <!--  Please see page Experimental Setups -->
            </device-mapping>
        </experimental-setup>    
    </experimental-setup-variants>
    <protocol>
        <languages>
          <!--  Please see page Languages and Localization -->
        </languages>
        <sessions>
          <!--  Please see page Sessions -->
        </sessions>
        <properties>
          <!--  Please see page Protocol properties -->
        </properties>
        <persistent-variables>
          <!--  Please see page Persistent variables -->
        </persistent-variables>        
        <variables>
          <!--  Please see page Variables -->
        </variables>
        <templates>
              <includes>
                <!--  Please see page Templating -->
              </includes>
              <protocol-variables>
                <!--  Please see page Templating -->
              </protocol-variables>
              <template-variables>
                <!--  Please see page Templating -->
              </template-variables>
              <procedure-templates>
                <!--  Please see page Templating -->
              </procedure-templates>
              <assets>
                <!--  Please see page Templating -->
              </assets>           
        </templates>
        <procedures>
          <!--  Please see page Procedures -->
        </procedures>
        <assets>
          <!--  Contains files that are included in the protocol -->
        </assets>
    </protocol>
    <post-actions>
      <!--  Please see page Post-Session Actions -->
    </post-actions>
</experiment>

Listing 1: Structure of an experiment definition file

Markdown

Mon, 01 Jan 0001 00:00:00 +0000

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Mon, 01 Jan 0001 00:00:00 +0000

Consuming LabBench HDF5 Files from MATLAB

This guide explains how MATLAB users should read and interpret HDF5 files generated by LabBench using the PureHDF-based exporter.

The layout is intentionally simple, deterministic, and compatible with MATLAB’s built-in h5read, h5info, and array handling.

1. Opening a LabBench HDF5 file

In MATLAB, HDF5 files are accessed using h5info and h5read.

info = h5info('experiment.h5');

All data lives under a single top-level group named after the root object:

Mon, 01 Jan 0001 00:00:00 +0000

Consuming LabBench HDF5 Files from Python

This guide explains how Python users should read and interpret HDF5 files generated by LabBench.

The layout is intentionally simple, deterministic, and compatible with h5py, NumPy, Matlab, and R.

1. Opening a LabBench HDF5 file

import h5py

f = h5py.File("experiment.h5", "r")

All data lives under a single top-level group named after the root object:

/root

So typically:

root = f["root"]

2. Scalars (DataNumber, DataBoolean, DataString)

LabBench scalars are stored as datasets.