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Package for handling tr-EPR data.

Project description

trepr is a package for handling data obtained using time-resolved electron paramagnetic resonance (TREPR) spectroscopy. It is based on the ASpecD framework. Due to inheriting from the ASpecD superclasses, all data generated with the trepr 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:

  type: ASpecD recipe
  version: '0.2'

  default_package: trepr

  - /path/to/first/dataset
  - /path/to/second/dataset

  - kind: processing
    type: PretriggerOffsetCompensation
  - kind: processing
    type: BackgroundCorrection
        num_profiles: [10, 10]
  - kind: singleplot
    type: SinglePlotter2D
        - first-dataset.pdf
        - second-dataset.pdf

For more general information on the trepr package and for how to use it, see its documentation.


A list of features:

  • Fully reproducible processing of tr-EPR data

  • Import and export of data from and to different formats

  • Customisable plots

  • Automatically generated reports

  • Recipe-driven data analysis, allowing tasks to be performed fully unattended in the background and without programming skills

And to make it even more convenient for users and future-proof:

  • Open source project written in Python (>= 3.5)

  • Extensive user and API documentation

Target audience

The trepr package addresses scientists working with TREPR data (both, measured and calculated) on a daily base and concerned with reproducibility. Due to being based on the ASpecD framework, the trepr 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 trepr package may well be interesting for you.


Install the package by running:

pip install trepr


This program is free software: you can redistribute it and/or modify it under the terms of the BSD License.

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