Pkynetics

A Python library for thermal analysis kinetic methods, providing tools for data preprocessing, kinetic analysis, and result visualization.

Features

Data Import

• Support for thermal analysis instruments:
• Flexible custom importer for non-standard formats
• Automatic manufacturer detection
• Comprehensive data validation

Analysis Methods

• Model-fitting methods:
  • Johnson-Mehl-Avrami-Kolmogorov (JMAK)
  • Kissinger
  • Coats-Redfern
  • Freeman-Carroll
  • Horowitz-Metzger
• Model-Free methods:
  • Friedman method
  • Kissinger-Akahira-Sunose (KAS)
  • Ozawa-Flynn-Wall (OFW)
• Dilatometry analysis
• Data preprocessing capabilities
• Error handling and validation

Visualization

• Comprehensive plotting functions for:
  • Kinetic analysis results
  • Dilatometry data
  • Transformation analysis
  • Custom plot styling options
• Interactive visualization capabilities

Installation

Pkynetics requires Python 3.9 or later. Install using pip:

pip install pkynetics

For development installation:

git clone https://github.com/PPeitsch/pkynetics.git
cd pkynetics
pip install -e .[dev]

For detailed installation instructions and requirements, see our Installation Guide.

Documentation

Complete documentation is available at pkynetics.readthedocs.io, including:

• Detailed API reference
• Usage examples
• Method descriptions
• Best practices

Contributing

We welcome contributions! Please read our:

• Contributing Guidelines
• Code of Conduct

Security

For vulnerability reports, please review our Security Policy.

Change Log

See CHANGELOG.md for a list of changes and version updates.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citing Pkynetics

If you use Pkynetics in your research, please cite it as:

@software{pkynetics2024,
  author = {Pablo Peitsch},
  title = {Pkynetics: A Python Library for Thermal Analysis Kinetic Methods},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/PPeitsch/pkynetics}
}