Exciting News! 🎉 PySDKit's first pre-release version is now available!
Install via pip and dive into our [demo](https://github.com/wwhenxuan/PySDKit/blob/main/example/demo.ipynb) to kickstart your experience—quick and easy! 🥰
pip install pysdkit
Why Develop PySDKit?
While wavelet transforms have seen remarkable integration with deep neural networks in recent years, signal decomposition techniques - hailed as the most groundbreaking time-frequency analysis method of the 21st century since the advent of Hilbert-Huang Transform (HHT) - remain significantly underutilized in deep learning applications. This gap primarily stems from the absence of a unified Python implementation framework.
To empower seamless integration of signal decomposition algorithms with deep learning architectures, accelerate research workflows, and enhance practical usability, we present PySDKit. This comprehensive library implements mainstream decomposition methodologies including:
- EMD (Empirical Mode Decomposition)
- EWT (Empirical Wavelet Transform)
- VMD (Variational Mode Decomposition)
...and more, serving as your essential toolkit for next-generation signal analysis.
Current Situation
PySDKit is currently developed by a single contributor (myself). While automated testing has been implemented, bugs may still exist. We warmly welcome:
- Issue Reporting - Please open a GitHub ticket for any errors you encounter.
- Algorithm Suggestions - Recommend promising signal decomposition methodologies via Discussions. High-value submissions will be prioritized for Python implementation.
Your contributions help shape this toolkit into a robust resource for the signal processing community.
All questions can be contacted by email: whenxuan@ieee.org
Or ask a question on Github directly: [Issues](https://github.com/wwhenxuan/PySDKit/issues)
Future Work
Currently, two of my friends will participate in the development of the project together, and we will reproduce all the algorithms listed in the table. We will also build more complete documents to help users understand the internal details and specific usage of each signal decomposition algorithm.