Potentiodynamic Polarization Handler

A Python toolkit for interactive Butler-Volmer curve fitting on potentiodynamic scan data.

DOI: 10.5281/zenodo.1342163 License: GNU General Public License 3.0

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Features

• Fit Butler-Volmer (BV) and BV + Film growth/dissolution (BVFeq) equations
• Linear Polarization Resistance (LPR) cross-check
• Post-hoc IR compensation
• Two interfaces: Jupyter Notebook (ipywidgets) and Streamlit web app
• Output: Ecorr, Icorr, icorr density, Ba, Bc, B, Rp, R², Chi²

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Files

File	Description
Tafel_LPR_fit_notebook_IRcomp_IFilm.py	Core module — Info, Tafit classes and equation functions
dta_parser.py	Gamry Instruments .DTA file parser
polarization_fitter.py	Streamlit web app
test_driver.ipynb	Tutorial notebook (interactive, ipywidgets)
tests/test_tafel_lpr.py	Formal unit test suite (65 tests)
test_file_Icorr10uA.DTA	Sample Gamry DTA file
validate_one_step_scan.xlsx	Sample one-step scan (absolute current)
validate_two_step_scan.xlsx	Sample two-step scan (absolute current)

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Input Formats

Format	Description
.DTA	Gamry Instruments potentiodynamic scan export. Signed current parsed automatically. Sample area read from file metadata.
.xlsx / .csv — signed	Two columns: I (signed A, cathodic < 0) and E (V). One header row.
.xlsx — absolute, one-step	Two columns: |I| and E. Cathodic side auto-detected from current minimum.
.xlsx — absolute, two-step	Four columns: Ic, Ec, Ia, Ea. OCP drift correction applied automatically.

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Installation

Python ≥ 3.8 (Anaconda recommended).

pip install numpy pandas matplotlib scipy scikit-learn ipywidgets streamlit openpyxl

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Streamlit App

Hosted app: https://potentiodynamic-polarization-fitter.streamlit.app

streamlit run polarization_fitter.py

Workflow:

1. Select input format in the sidebar
2. Upload a file or click Load sample data to use the bundled example
3. Set sample area (cm²) — auto-filled from DTA metadata when available
4. Adjust data range, Tafel init points, and IR compensation with sliders
5. Optionally enable BV + Film model to fit BVFeq alongside BVeq
6. Results table shows Ecorr, Icorr, icorr density, Ba, Bc, B, Rp, R², and LPR cross-check values

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Jupyter Notebook

Open test_driver.ipynb for a step-by-step tutorial covering:

• One-step and two-step scan loading
• Interactive BV fitting with BV_LPR_interact()
• BV + Film fitting with BVF_LPR_interact()
• Validation against synthetic data with known parameters
• Multi-scan comparison with plot_compare()

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Python API

import Tafel_LPR_fit_notebook_IRcomp_IFilm as tf

# Load a Gamry DTA file
info = tf.Info("test_file_Icorr10uA.DTA", area=2.929)

# Load an Excel file (signed current)
info = tf.Info("data.xlsx", pd_dfIE=df, use_pd_df=True, area=1.0)

# Fit
tafit = tf.Tafit(info)
tafit.BV_LPR_interact(anodic_range=0.15, cathodic_range=0.15)

# Access results
print(tafit.result)   # Ecorr, Icorr, Ba, Bc, B, Rp, Icorr_LPR

DTA parser (standalone)

from dta_parser import parse_dta

df, meta = parse_dta("experiment.DTA")
# df: columns I (A, signed) and E (V)
# meta: dict with AREA, EOC, SCANRATE, DATE, ...

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Running Tests

python -m pytest tests/ -v

65 tests covering BVeq, Feq, BVFeq, Info (DataFrame / Excel / DTA loading), and Tafit fitting accuracy against synthetic ground-truth data.

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Citation

Li, Gang, Evitts, Richard, Boulfiza, Moh, & Li, Alice D.S. (2018, August 11). A customized Python module for interactive curve fitting on potentiodynamic scan data (Version v1.0.1). Zenodo. http://doi.org/10.5281/zenodo.1343975