PyProBE: Python Processing for Battery Experiments

PyProBE (Python Processing for Battery Experiments) is a Python package to process experimental data in a standardised and reproducible way.Recognising that battery experiments are often derivatives of standard procedures, PyProBE simplifies data processing to improve performance and transparency.It was written with the following objectives:1. Ease of use for those with little Python experience, with a natural language interface. Accelerate battery data exploration with quick plotting and data visualisation, usingPolars under-the-hood for rapid DataFrame manipulation and a graphical user interface (GUI).3. Encourage transparent storage of battery data, with human and computer-readable README-files and a readable API syntax.4. Host a library of post-processing methods, so techniques can be easily added and compared within a standardised code structure.

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Publication Details

Journal
The Journal of Open Source Software
Published
2025-02-04
DOI
https://doi.org/10.21105/joss.07474
Citations
2
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
8.34

Funders

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article

PyProBE: Python Processing for Battery Experiments

Monica Marinescu, Daniel Cummins, Thomas L Holland
2 citations
The Journal of Open Source Software
Scientific Computing and Data Management
8.34
article

PyProBE: Python Processing for Battery Experiments

Monica Marinescu, Daniel Cummins, Thomas L Holland
article en
2 citations

Abstract

PyProBE (Python Processing for Battery Experiments) is a Python package to process experimental data in a standardised and reproducible way.Recognising that battery experiments are often derivatives of standard procedures, PyProBE simplifies data processing to improve performance and transparency.It was written with the following objectives:1. Ease of use for those with little Python experience, with a natural language interface. Accelerate battery data exploration with quick plotting and data visualisation, usingPolars under-the-hood for rapid DataFrame manipulation and a graphical user interface (GUI).3. Encourage transparent storage of battery data, with human and computer-readable README-files and a readable API syntax.4. Host a library of post-processing methods, so techniques can be easily added and compared within a standardised code structure.

The Journal of Open Source SoftwareVol. 10(106)
The Faraday Institution (GB), Imperial College London (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 4%
Scientific Computing and Data Management
8.34
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