Package for handling cw-EPR data.
The cwEPR package provides tools for handling experimental data obtained using continuous-wave EPR (cwEPR) spectroscopy and is derived from the ASpecD framework. Due to inheriting from the ASpecD superclasses, all data generated with the cwepr package are completely reproducible and have a complete history.
What is even better: Actual data processing and analysis no longer requires programming skills, but is as simple as writing a text file summarising all the steps you want to have been performed on your dataset(s) in an organised way. Curious? Have a look at the following example:
format: type: ASpecD recipe version: '0.2' settings: default_package: cwepr datasets: - /path/to/first/dataset - /path/to/second/dataset tasks: - kind: processing type: FrequencyCorrection properties: parameters: frequency: 9.8 - kind: processing type: BaselineCorrection properties: parameters: order: 0 - kind: singleplot type: SinglePlotter1D properties: filename: - first-dataset.pdf - second-dataset.pdf
For more general information on the cwepr package and for how to use it, see its documentation.
A list of features, not all implemented yet but aimed at for the first public release (cwEPR 0.1):
Fully reproducible processing of cw-EPR data
Import of EPR data from diverse sources (Bruker ESP, EMX, Elexsys; Magnettech)
Generic plotting capabilities, easily extendable
Report generation using pre-defined templates
Recipe-driven data analysis, allowing tasks to be performed fully unattended in the background
And to make it even more convenient for users and future-proof:
Open source project written in Python (>= 3.7)
Extensive user and API documentation
The cwepr package addresses scientists working with cwEPR data (both, measured and calculated) on a daily base and concerned with reproducibility. Due to being based on the ASpecD framework, the cwepr package ensures reproducibility and—as much as possible—replicability of data processing, starting from recording data and ending with their final (graphical) representation, e.g., in a peer-reviewed publication. This is achieved by automatically creating a gap-less record of each operation performed on your data. If you do care about reproducibility and are looking for a system that helps you to achieve this goal, the cwepr package may well be interesting for you.
Install the package by running:
pip install cwepr
This program is free software: you can redistribute it and/or modify it under the terms of the BSD License.
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