Package: cwepr
The cwEPR package provides tools for handling experimental data obtained using continuous-wave EPR (cwEPR) spectroscopy and is derived from theASpecD 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 analysisno 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.pdfFor more general information on the cwepr package and for how to use it, see
itsdocumentation.FeaturesA list of features:Fully reproducible processing of cw-EPR dataImport of EPR data from diverse sources (Bruker ESP, EMX, Elexsys; Magnettech)Generic plotting capabilities, easily extendableReport generation using pre-defined templatesRecipe-driven data analysis, allowing tasks to be performed fully unattended in the backgroundAnd to make it even more convenient for users and future-proof:Open source project written in Python (>= 3.7)Extensive user and API documentationTarget audienceThe cwepr package addresses scientists working with cwEPR data (both, measured and calculated) on a daily base and concerned withreproducibility. Due to being based on theASpecD 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.How to citecwepr is free software. However, if you use cwepr for your own research, please cite both, the article describing it and the software itself:Mirjam Schröder, Till Biskup. cwepr – a Python package for analysing cw-EPR data focussing on reproducibility and simple usage.Journal of Magnetic Resonance335:107140, 2022.doi:10.1016/j.jmr.2021.107140|PDF|SIMirjam Schröder, Till Biskup. cwepr (2021).doi:10.5281/zenodo.4896687To make things easier, cwepr has aDOIprovided byZenodo, and you may click on the badge below to directly access the record associated with it. Note that this DOI refers to the package as such and always forwards to the most current version.Related projectsThere is a number of related packages users of the cwepr package may well be interested in, as they have a similar scope, focussing on spectroscopy and reproducible research.ASpecDA Python framework for the analysis of spectroscopic data focussing on reproducibility and good scientific practice. The framework the cwepr package is based on, developed by T. Biskup.treprPackage for processing and analysing time-resolved electron paramagnetic resonance (TREPR) data, originally developed by J. Popp, currently developed and maintained by M. Schröder and T. Biskup.FitPyFramework for the advanced fitting of models to spectroscopic data focussing on reproducibility, developed by T. Biskup.You may as well be interested in theLabInform projectfocussing on the necessary more global infrastructure in a laboratory/scientific workgroup interested in morereproducible research. In short, LabInform is “The Open-Source Laboratory Information System”.Finally, don’t forget to check out the website onreproducible researchcovering in more general terms aspects of reproducible research and good scientific practice.InstallationInstall the package by running:pip install cweprLicenseThis program is free software: you can redistribute it and/or modify it under the terms of theBSD License.