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Comprehensive package for data analysis of dipolar EPR spectroscopy

Project description

DeerLab Website PyPI - Python Version PyPI - Downloads


DeerLab is a comprehensive free scientific software package for Python focused on modelling, penalized least-squares regression, and uncertainty quantification. It provides highly specialized on 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.

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, 3.9, or 3.10.

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.

Citing DeerLab

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

Here is the citation in bibtex format:

  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 © 2019-2022: Luis Fábregas Ibáñez, Stefan Stoll, Gunnar Jeschke, and other contributors.

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