e-chem 1.0.5

echemkit is a lightweight Python library designed for seamless electrochemical data analysis.

EC-python Lib

echemkit is a lightweight Python library designed for seamless electrochemical data analysis directly within Jupyter notebooks. It provides intuitive tools for loading, processing, and visualising data from standard potentiostat systems such as BioLogic, PalmSens, and Squidstat. The package supports cyclic voltammetry (CV), chrono-potentiometry (CP), electrochemically active surface area (ECSA) analysis, and Tafel slope evaluation through a consistent, object-oriented API. Researchers can quickly import raw measurement files, extract key parameters, apply smoothing or compensation routines, and generate publication-ready plots—all in an interactive environment. With clear syntax and modular design, echemkit enables reproducible, open electrochemical analysis suitable for education, prototyping, and advanced research workflows.

Features

• Load and analyse cyclic voltammetry, chrono-potentiometry, PEIS, ECSA, and Tafel data.
• Work directly with common potentiostat exports from BioLogic, PalmSens, and Squidstat.
• Process electrochemical data in Python and Jupyter notebooks with a consistent API.
• Generate publication-ready plots using NumPy, pandas, SciPy, and matplotlib.
• Run PEIS equivalent-circuit fitting and DRT workflows, including vendored headless pyDRTtools components.

Quick Setup

Install the latest published package from PyPI:

pip install e-chem

For local development, clone the repository and install it in editable mode:

git clone https://gitlab.tuwien.ac.at/iap/aip/anp/electrochemistry/basic-ec/e-chem.git
cd e-chem
pip install -e .

Basic import pattern:

from e_chem import cv, peis

Project Links

• PyPI package: e-chem
• Project homepage / research data record: 10.48436/4exfe-yx784
• Source repository: TU Wien GitLab

Citation

If you use echemkit for scientific research or publication, please cite:

DOI: 10.48436/4exfe-yx784

License And Attribution

This project is distributed under the MIT License.

Parts of the PEIS/DRT implementation include code acquired and adapted from pyDRTtools. Those components are vendored into this repository to support headless, notebook-friendly DRT analysis without requiring the original GUI stack.