This repository has been migrated to Python and can be found in a new repository. Follow along there for updates.

The MATLAB version of DeerLab (versions 0.9.0-0.9.2 and older) are deprecated and no further support will be provided for them.

This repository is marked archived, and will no longer receive any updates.

The DeerLab software package is a MATLAB toolbox for the analysis of data from DEER (double electron-electron resonance) spectroscopy and similar dipolar EPR spectroscopy techniques (RIDME, DQC, SIFTER,...). The documentation for the MATLAB version can be found here. This is the GitHub repository of the MATLAB DeerLab source code, including instructions for compiling and installing DeerLab.

DeerLab consists of a collection of functions that perform modelling, processing or fitting tasks. They can be combined in scripts to build custom data analysis workflows.

DeerLab requires the following products:

• MATLAB (R2017a or newer) (see https://www.mathworks.com/products/matlab.html)

DeerLab will use the following product if installed:

• Optimization Toolbox (see https://www.mathworks.com/products/optimization.html)

In order for MATLAB to access the DeerLab functions, the path to the DeerLab installation folder must be set in MATLAB.

Option 1: Add DeerLab path via MATLAB's IDE

1. On the Home tab, in the Environment section, click Set Path.

2. Click Add with Subfolders... and select the DeerLab\functions directory.

3. Click Save to save the current MATLAB search path and exit via Close.

Option2: Add DeerLab path at startup

1. Open (or create) the startup.m file in the default \MATLAB directory.

2. Add the following lines of code:

   addpath('mypath/DeerLab/functions')
   

3. Save startup.m and restart MATLAB.

A publication about DeerLab is available here. When you use DeerLab in your work, please cite

Fábregas Ibáñez, L., Jeschke, G., and Stoll, S.: DeerLab: A comprehensive toolbox for analyzing dipolar EPR spectroscopy data, Magn. Reson. Discuss., https://doi.org/10.5194/mr-2020-13, 2020

Please check back frequently for updated publication information.

The DeerLab toolbox is licensed under the MIT License. The complete toolbox consists of the functions (functions/), documentation source (docsrc/), tutorial scripts (tutorials/), test suite (tests/), and pipeline scripts (.github/workflows). See below for exceptions.

DeerLab includes code from the following projects, which have their own licenses:

• datahash.m (Hash-key generator by Jan Simon) [BSD]
• fresnelS.m, fresnelC.m (Efficient and accurate Fresnel integrals by John D'Errico) [BSD]
• nlsqbnd.m (Non-linear least squares solver with box constraints by Alain Barraud) [BSD]
• golden.m (Golden Section method algorithm by Katarzyna Zarnowiec) [BSD]
• jacobianest.m (Adaptive Robust Numerical Differentiation by John D'Errico) [BSD]
• kde.m (Kernel Density Estimator by Zdravko Botev) [BSD]
• LevenbergMarquardt.m, jacobiansimple.m (Levenberg-Marquardt & Jacobian toolbox by Alexander Dentler)[BSD]
• fdcoeffF.m (Fornberg's method for finite difference coefficients)
• minq.m (MINQ8 - General Definite and Bound Constrained Indefinite Quadratic Programming by Waltraud Huyer and Arnold Neumair)

Copyright (c) 2019-2020: Luis Fábregas Ibáñez, Stefan Stoll, Gunnar Jeschke, and other contributors.