Data processing and analysis¶
One of the biggest difficulties of EPR spectroscopy is data analysis. This is mostly due to the intrinsic complexity of the method and the need to fit spectral simulations to the experimental data in order to extract crucial parameters. But even for data processing prior to analysis there seems to exist no routine protocols. The result is a plethora of different ways to handle the data and usually a complete lack of details in publications. This puts reproducibility and reliability of published results severely into question. The answer: A modular, robust framework for advanced data processing and analysis that meets the high standards of full reproducibility while being both, easily extendable as well as usable even for non-experts.
Tools developed by the author¶
Due to the more than fifteen years of experience of the author with software for data analysis and a number of projects originally implemented in MATLAB®, there is a list of Python packages available, including a rather general framework for reproducible data processing and analysis (ASpecD), packages built on top of that for both, cw-EPR and TREPR spectroscopy, and developments towards packages for simulating and fitting EPR spectra.
ASpecD¶
A Python framework for reproducible data processing and analysis focussing on spectroscopy. Each processing and analysis step gets automatically logged with all parameters to ensure reproducibility. Recipe-driven data analysis provides an easy access even without programming skills. The framework is starting point for a list of other packages, including trepr and cwepr (see below).
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Python |
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development, beta |
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PyPI |
cwepr¶
Python package for analysing cwEPR data (ASpecD based)
Each processing and analysis step gets automatically logged with all parameters to ensure reproducibility. Recipe-driven data analysis provides an easy access even without programming skills.
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Python |
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development, beta |
Documentation |
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Code |
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PyPI |
trepr¶
Python package for analysing trEPR data (ASpecD based)
Each processing and analysis step gets automatically logged with all parameters to ensure reproducibility. Recipe-driven data analysis provides an easy access even without programming skills.
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Python |
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development, beta |
Documentation |
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Code |
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PyPI |
SpinPy¶
Python package for simulating EPR spectra
Focuses on simulating solid state spectra with particular emphasis on spin-polarised species, partial orientation, and multiple concurrent spectral species.
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Python |
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development, beta |
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Code |
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PyPI |
FitPy¶
Python package for fitting simulations to spectroscopic data
Focuses on providing advanced methods to fit models to data, such as stochastic algorithms to sample starting parameters and global analysis both for multiple datasets as well as multiple spectral components within one dataset.
Language |
Python |
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development, beta |
Documentation |
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Code |
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PyPI |