DeerLab is a Python package for the analysis of dipolar EPR (electron paramagnetic resonance) spectroscopy data. Dipolar EPR spectroscopy techniques include DEER (double electron-electron resonance), RIDME (relaxation-induced dipolar modulation enhancement), and others. The documentation can be found here.
DeerLab consists of a collection of functions for modelling, data processing, and least-squares fitting. They can be combined in scripts to build custom data analysis workflows. DeerLab supports both classes of distance distribution models: non-parametric (Tikhonov regularization and related) and parametric (multi-Gaussians etc). It also provides a selection of background and experiment models.
The early versions of DeerLab (up to version 0.9.2) are written in MATLAB. The old MATLAB codebase is archived and can be found here.
DeerLab is available for Windows, Mac and Linux systems and requires Python 3.6, 3.7, 3.8, or 3.9.
All additional dependencies are automatically downloaded and installed during the setup.
A pre-built distribution can be installed from the PyPI repository using pip or from the Anaconda repository using conda.
From a terminal (preferably with admin privileges) use the following command to install from PyPI:
python -m pip install deerlab
or the following command to install from Anaconda:
conda install deerlab -c JeschkeLab
More details on the installation and updating of DeerLab can be found here.
When you use DeerLab in your work, please cite the following publication:
DeerLab: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data
Luis Fábregas Ibáñez, Gunnar Jeschke, Stefan Stoll
Magn. Reson., 1, 209–224, 2020
doi.org/10.5194/mr-1-209-2020
Here is the citation in bibtex format:
@article{FabregasIbanez2020_DeerLab,
title = {{DeerLab}: a comprehensive software package for analyzing dipolar electron paramagnetic resonance spectroscopy data},
author = {Fábregas Ibáñez, Luis and Jeschke, Gunnar and Stoll, Stefan},
journal = {Magnetic Resonance},
year = {2020},
volume = {1},
number = {2},
pages = {209--224},
doi = {10.5194/mr-1-209-2020}
}DeerLab is licensed under the MIT License.
Copyright © 2019-2021: Luis Fábregas Ibáñez, Stefan Stoll, Gunnar Jeschke, and other contributors.