eisyfit 0.1.9

eisyFIT

eisyFIT (formerly EIS Fitting) is a desktop application for exploring, cleaning, and fitting electrochemical impedance spectroscopy (EIS) data exported by BioLogic as .mpt (PEIS) files. It combines an interactive Nyquist plot with circuit parameter editing, cycle-aware analysis, DRT-assisted outlier detection, and batch fitting, so spectra can be reviewed and fitted without leaving the GUI.

The application:

• Supports multiple files and multiple measurement cycles
• Displays spectra beside an editable equivalent-circuit parameter table
• Lets you include or exclude individual points while preserving cycle state
• Runs fitting and outlier detection in the background so the GUI stays responsive
• Uses bayes-drt2 ridge analysis to identify outliers and initialize circuit fits
• Compares measured and fitted points at identical frequencies with residual connectors
• Provides a sortable spectra explorer for cycle and acquisition metadata
• Provides DRT and EEC fit-parameter explorers with an interactive curve fit tool
• Exports fitted parameters and spectrum metadata to CSV and a ready-to-run Python script

What's new in 0.1.9

• Interactive curve fit tool in the DRT and EEC fit-parameter explorers: type any equation with free parameters (for example a*x + b or a*exp(-b*x) + c), pick points in the plot, and the parameters are solved and the curve overlaid
• Removed the non-functional reference line tool from the parameter explorers
• Retrained the AEM-WE ML model on additional manually fitted PEIS samples
• Renamed the repository to eisyFIT; installation from PyPI and GitHub is unchanged

Installation

Requirements

• Windows, macOS, or Linux with a desktop environment
• Python 3.14 or newer
• uv, the environment and dependency manager used by this project

Install from PyPI

To install the released application into an isolated environment and launch it:

uvx eisyfit

To add it to an existing uv project:

uv add eisyfit
eisyfit

The PyPI release depends on public distributions of the project’s DRT backends. Until the maintained forks of bayes-drt2, hybrid-drt, and the EIS-specific wepy package are published under installable PyPI names, use the GitHub installation below.

The application requires Python 3.14 or newer and a desktop environment with Tk support. On some Linux distributions, Tk must be installed separately (for example, the python3-tk system package).

Run from GitHub

In PowerShell, Terminal, or a shell:

git clone https://github.com/jaroslav-herman/eisyFIT.git
cd eisyFIT
uv sync

uv sync creates the project environment and installs the versions recorded in uv.lock, including the required EIS and DRT libraries. Re-run it after pulling changes to update the environment.

Start the application

From the repository directory, run:

uv run python main.py

To open a BioLogic file immediately and select a cycle, pass its path:

uv run python main.py path/to/spectrum.mpt --cycle 1

On Windows, run_gui.cmd can also be double-clicked. It checks that uv is available and starts the application from the repository directory.

If uv is not available, install it using the official instructions linked above, then open a new terminal and repeat uv sync.

References and scientific works

eisyFIT builds on published methods from the electrochemical impedance community. If you use eisyFIT in published work, please cite the relevant papers behind the methods you use:

• Equivalent-circuit fitting and Lin-KK validation — impedance.py: Murbach, M. D., Gerwe, B., Dawson-Elli, N., & Schwartz, D. T. (2020). impedance.py: A Python package for electrochemical impedance analysis. Journal of Open Source Software, 5(52), 2349. https://doi.org/10.21105/joss.02349
• Kramers–Kronig validation (Lin-KK algorithm used for data validation): Schönleber, M., Klotz, D., & Ivers-Tiffée, E. (2014). A Method for Improving the Robustness of Linear Kramers-Kronig Validity Tests. Electrochimica Acta, 131, 20–27. https://doi.org/10.1016/j.electacta.2014.01.034
• bayes-drt2 ridge analysis, outlier detection, and DRT-assisted EEC initialization — hierarchical Bayesian DRT inversion: Huang, J., Papac, M., & O’Hayre, R. (2021). Towards robust autonomous impedance spectroscopy analysis: A calibrated hierarchical Bayesian approach for electrochemical impedance spectroscopy (EIS) inversion. Electrochimica Acta, 367, 137493. https://doi.org/10.1016/j.electacta.2020.137493
• hyperparametric ridge regression (used by bayes_drt2.ridge_fit with hyperparameters): Ciucci, F., & Chen, C. (2015). Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A Bayesian and hierarchical Bayesian approach. Electrochimica Acta, 167, 439–454. https://doi.org/10.1016/j.electacta.2015.03.123; Effat, M. B., & Ciucci, F. (2017). Bayesian and Hierarchical Bayesian Based Regularization for Deconvolving the Distribution of Relaxation Times from Electrochemical Impedance Spectroscopy Data. Electrochimica Acta, 247, 1117–1129. https://doi.org/10.1016/j.electacta.2017.07.050
• hybrid-drt DRT analysis and fit-parameter explorers — probabilistic DRT with a dual regression-classification framework: Huang, J., Sullivan, N. P., Zakutayev, A., & O’Hayre, R. (2023). How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy. Electrochimica Acta, 443, 141879. https://doi.org/10.1016/j.electacta.2023.141879 (open version: https://www.osti.gov/servlets/purl/1957991)
• scikit-learn (used for the ML topology and parameter-prediction pipelines): Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.48550/arXiv.1201.0490

License

eisyFIT is licensed under the GNU General Public License v3.0 or later. Its dependencies remain under their respective licenses; see THIRD_PARTY_NOTICES.md for the dependency license summary.

Run

The command-line entry point also accepts options for the initial control channel, outlier threshold, and equivalent-circuit model:

uv run python main.py path/to/spectrum.mpt --cycle 1 --threshold 1.0 --circuit "R0-L0-p(R1,CPE1)"

GUI controls

• Click a point: toggle include/exclude
• Cycle arrows or selector: move between cycles
• Spectra explorer: select a spectrum or sort rows by source file, cycle, voltage, current, point count, or frequency limits
• Fitting model: select a preset or enter an impedance.py circuit string, then set it
• Apply to current cycle: use the frequency range only for the displayed spectrum
• Apply to all cycles: use the frequency range for every spectrum in the file
• Outliers: current: run the bayes-drt2 ridge analysis on active points only, apply its outlier mask without reactivating excluded points, and initialize the EEC fit from its ohmic resistance, inductance, and strongest DRT peaks
• Outliers: all cycles: perform the same active-point analysis and EEC initialization for every spectrum in the background
• Fit spectrum: fit included points with the parameter table values
• Batch fit from current: fit toward higher cycles, using each result to initialize the next
• Export to Python: save fitted parameters and spectrum metadata from all loaded files to CSV, create a ready-to-run pandas script, and open it in VS Code or another available editor
• Reset points: include all points and clear detected outliers

The File menu contains additive multi-file data import, project load/save, fit-parameter and Python-workspace exports, and exit commands. Import accepts several .mpt files at once and keeps previously loaded spectra.

The Fit menu fits the selected spectrum or batch-fits upward/downward through the explorer's visible order. Metadata-limited batches use the last clicked numeric explorer column and stop at its nearest available target value.

Outlier detection never runs automatically. Cycle state is retained while the GUI is open.

Keyboard shortcuts:

• Alt+A: copy fitted values from the previous cycle into the current initial values
• Alt+D: copy fitted values from the next cycle into the current initial values
• Alt+S: fit the selected spectrum
• Ctrl+O: import another .mpt data file
• Ctrl+Shift+O: load an EIS fitting project
• Ctrl+S: save the EIS fitting project

Code structure

• eis_model.py: GUI-independent project and per-cycle state
• eis_services.py: file loading, outlier detection, and fitting operations
• eis_project.py: versioned project persistence and fit CSV export
• eis_gui.py: Tk interface, plot rendering, and event coordination
• main.py: command-line entry point