Metadata-Version: 2.1
Name: DeerLab
Version: 0.13.0
Summary: Comprehensive package for data analysis of dipolar EPR spectroscopy
Home-page: https://github.com/JeschkeLab/DeerLab
Author: Luis Fábregas Ibáñez , Stefan Stoll and other contributors
License: LICENSE.txt
Project-URL: Documentation, https://jeschkelab.github.io/DeerLab/
Project-URL: Source, https://github.com/JeschkeLab/DeerLab
Description: # DeerLab
        
        [![https://jeschkelab.github.io/DeerLab/](https://img.shields.io/pypi/v/deerlab)](https://pypi.org/project/DeerLab/)
        [![Website](https://img.shields.io/website?down_message=offline&label=Documentation&up_message=online&url=https%3A%2F%2Fjeschkelab.github.io%2FDeerLab%2Findex.html)](https://jeschkelab.github.io/DeerLab/)
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/deerlab)](https://www.python.org/downloads/)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/deerlab?color=brightgreen)
        
        ### About
        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](https://jeschkelab.github.io/DeerLab/index.html).
        
        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](https://github.com/JeschkeLab/DeerLab-Matlab).
        
        ### Requirements
        
        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.
         
        ### Setup
        
        A pre-built distribution can be installed using `pip`.
        
        First, ensure that `pip` is up-to-date. From a terminal (preferably with admin privileges) use the following command:
        
            python -m pip install --upgrade pip
        
        Next, install DeerLab with
        
            python -m pip install deerlab
        
        More details on the installation of DeerLab can be found [here](https://jeschkelab.github.io/DeerLab/installation.html).
        
        ### Citation
        
        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** <br>
         Luis FÃ¡bregas IbÃ¡Ã±ez, Gunnar Jeschke, Stefan Stoll <br>
         Magn. Reson., 1, 209â€“224, 2020 <br>
         <a href="https://doi.org/10.5194/mr-1-209-2020"> doi.org/10.5194/mr-1-209-2020</a>
        
        
        ### License
        
        DeerLab is licensed under the [MIT License](LICENSE).
        
        Copyright Â© 2019-2021: Luis FÃ¡bregas IbÃ¡Ã±ez, Stefan Stoll, Gunnar Jeschke, and [other contributors](https://github.com/JeschkeLab/DeerLab/contributors).
        
Keywords: data,analysis,EPR,spectroscopy,DEER,PELDOR
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.6
Description-Content-Type: text/markdown
