Pkynetics

Pkynetics is a Python library for thermal analysis kinetic methods. It provides tools for data preprocessing, kinetic analysis, and result visualization.

Features

Currently implemented:

• Data import from various thermal analysis instruments (TA Instruments, Mettler Toledo, Netzsch, Setaram)
• Model-fitting methods:
  • Avrami method
  • Kissinger method
  • Coats-Redfern method
  • Freeman-Carroll method
  • Horowitz-Metzger method
• Basic result visualization

Planned for future releases:

• Model-free (isoconversional) methods
• Additional kinetic analysis techniques
• Machine learning approaches
• Enhanced visualization and statistical analysis
• Performance optimizations

Requirements

Pkynetics requires Python 3.8 or later. It has been tested with Python 3.8, 3.9, 3.10, and 3.11.

Installation

Install Pkynetics using pip:

pip install pkynetics

Usage

For usage instructions and examples, refer to the documentation in the docs/ directory and the examples in the examples/ directory.

Documentation

The full documentation is included in the docs/ directory. To build it locally:

1. Navigate to the docs/ directory
2. Run make html
3. Open docs/_build/html/index.html in your web browser

For online access to the documentation, visit the Pkynetics documentation site.

Examples

Jupyter notebook examples demonstrating various use cases are available in the examples/ directory.

Contributing

We welcome contributions to Pkynetics. If you're interested in contributing, please open an issue or submit a pull request on our GitHub repository.

License

Pkynetics is released under the MIT License. See the LICENSE file for details.