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.

There 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.

• ASpecD

  A 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.

• trepr

  Package 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.

• FitPy

  Framework for the advanced fitting of models to spectroscopic data focussing on reproducibility, developed by T. Biskup.

You may as well be interested in the LabInform project focussing on the necessary more global infrastructure in a laboratory/scientific workgroup interested in more reproducible research. In short, LabInform is "The Open-Source Laboratory Information System".

Finally, don't forget to check out the website on reproducible research covering in more general terms aspects of reproducible research and good scientific practice.

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.