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This repository was archived by the owner on Aug 26, 2020. It is now read-only.
The DeerLab software package is a MATLAB toolbox for the analysis of data from DEER (double electron-electron resonance) spectroscopy and similar dipolar spectroscopy techniques (DQC, RIDME, SIFTER). The main homepage can be found at www.deeranalysis.org. This is the GitHub repository of the DeerLab source code, including instructions for compiling and installing DeerLab.
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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 main homepage can be found at [jeschkelab.github.io/DeerLab](https://jeschkelab.github.io/DeerLab/). This is the GitHub repository of the DeerLab source code, including instructions for compiling and installing DeerLab.
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It consists of a collection of functions that perform single processing or fitting tasks. They can be combined in scripts to generate custom analysis workflows.
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It 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.
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To model distance distributions, DeerLab supports two types of model classes and associated workflows: parameter-free models (as used in Tikhonov regularization) as well as a series of parameterized models (mutli-Gaussians etc). It also provides a selection of background models. There are functions for generating synthetic datasets as well as for fitting and analyzing experimental data sets.
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To model distance distributions, DeerLab supports two types of model classes and associated workflows: parameter-free models (as used in Tikhonov regularization) as well as a series of parameterized models (mutli-Gaussians etc). It also provides a selection of background and experiment models. There are functions for generating synthetic datasets as well as for fitting and analyzing experimental data sets.
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### Requirements
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The application programming interface (API) of DeerLab requires the following products:
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DeerLab requires the following products:
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* MATLAB (oldest version supported R2016b) (see <https://ch.mathworks.com/products/matlab.html>)
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* MATLAB (R2016b or newer) (see <https://ch.mathworks.com/products/matlab.html>)
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Optional functionality may require the following products:
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@@ -48,20 +47,21 @@ In order for MATLAB to access the DeerLab API functions, the path to the DeerLab
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2) Add the following lines of code:
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addpath('mypath/DeerLab/functions')
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addpath('mypath/DeerLab/functions/models')
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3) Save ``startup.m`` and restart MATLAB.
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### License
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The DeerLab package is licensed under the MIT License. The "package" consists of the DOE ([functions/](https://github.com/luisfabib/deerlab/tree/master/functions)), documentation source ([docsrc/](https://github.com/luisfabib/deerlab/tree/master/docsrc)), tutorial scripts ([tutorials/](https://github.com/luisfabib/deerlab/tree/master/tutorials)), test suite ([tests/](https://github.com/luisfabib/deerlab/tree/master/tests)) and pipeline scripts ([.github/workflows](https://github.com/luisfabib/deerlab/tree/master/.github/workflows)). See below for exceptions.
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Copyright (c) 2019: Luis Fabregas, Stefan Stoll, Gunnar Jeschke, and [other contributors](https://github.com/luisfabib/deerlab/contributors).
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The DeerLab toolbox is licensed under the MIT License. The complete toolbox consists of the functions ([functions/](https://github.com/JeschkeLab/DeerLab/tree/master/functions)), documentation source ([docsrc/](https://github.com/JeschkeLab/DeerLab/tree/master/docsrc)), tutorial scripts ([tutorials/](https://github.com/JeschkeLab/DeerLab/tree/master/tutorials)), test suite ([tests/](https://github.com/JeschkeLab/DeerLab/tree/master/tests)), and pipeline scripts ([.github/workflows](https://github.com/JeschkeLab/DeerLab/tree/master/.github/workflows)). See below for exceptions.
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DeerLab includes code from the following projects, which have their own licenses:
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-[datahash.m](https://www.mathworks.com/matlabcentral/fileexchange/31272-datahash) (Hash-key generator by Jan Simon) [BSD]
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-[fresnelS.m, fresnelC.m](https://www.mathworks.com/matlabcentral/fileexchange/28765-fresnels-and-fresnelc) (Efficient and accurate Fresnel integrals by John D'Errico) [BSD]
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-[fminsearchcon.m](https://www.mathworks.com/matlabcentral/fileexchange/8277-fminsearchbnd-fminsearchcon) (Bound constrained optimization using fminsearch by John D'Errico) [BSD]
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-[nlsqbnd.m](https://www.mathworks.com/matlabcentral/fileexchange/8277-fminsearchbnd-fminsearchcon) (Non-linear least squares solver with box constraints by Alain Barraud) [BSD]
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-[nlsqbnd.m](https://ch.mathworks.com/matlabcentral/fileexchange/23621-nlsqbnd) (Non-linear least squares solver with box constraints by Alain Barraud) [BSD]
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-[golden.m](https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm) (Golden Section method algorithm by Katarzyna Zarnowiec) [BSD]
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-[jacobianest.m](https://www.mathworks.com/matlabcentral/fileexchange/13490-adaptive-robust-numerical-differentiation) (Adaptive Robust Numerical Differentiation by John D'Errico) [BSD]
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-[kde.m](https://ch.mathworks.com/matlabcentral/fileexchange/14034-kernel-density-estimator) (Kernel Density Estimator by Zdravko Botev) [BSD]
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-[LevenbergMarquardt.m, jacobiansimple.m](https://ch.mathworks.com/matlabcentral/fileexchange/53449-levenberg-marquardt-toolbox)(Levenberg-Marquardt & Jacobian toolbox by Alexander Dentler)[BSD]
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Copyright (c) 2019-2020: Luis Fabregas, Stefan Stoll, Gunnar Jeschke, and [other contributors](https://github.com/JeschkeLab/DeerLab/contributors).
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