Release Notes
We are excited to introduce WaLSAtools, an evolving open-source library for wave analysis that provides a solid foundation for comprehensive time-series exploration. This initial release equips users with essential tools for analysing a wide range of wave phenomena in time-series data, including:
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Core Analysis Modules:
- Fast Fourier Transform (FFT)
- Lomb-Scargle Approach
- Wavelet Transform
- Empirical Mode Decomposition (EMD)
- Hilbert-Huang Transform (HHT)
- Welch's Method
- k-ω Analysis
- Proper Orthogonal Decomposition (POD)
- Cross-Correlation Analysis
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Interactive Interface: User-friendly interface for easy access to analysis tools and parameters.
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Worked Examples: Reproducible examples demonstrating the application of WaLSAtools to synthetic datasets, as featured in the associated Nature Reviews Methods Primers article.
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Documentation: Comprehensive documentation covering installation, usage, and analysis methods (https://WaLSA.tools)
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Multi-Language Support: Available in Python and IDL, with plans to expand to other languages. Python serves as the primary development language, while IDL support is partially implemented in this release, with ongoing development to achieve full feature parity.
Known Issues
Feature Parity Between Languages: While we aim for full consistency between the Python and IDL versions, some functions have not yet been fully translated into IDL. Efforts are ongoing to bridge these gaps in future updates.
Future Developments
We are committed to continuously enhancing WaLSAtools. Upcoming plans include:
Expanded Functionality: New analysis methods, improved algorithms, and an enriched interactive experience.
Broader Language Support: Further development in IDL, with potential expansion to MATLAB and other programming languages.
Contributions and feedback are welcome to ensure WaLSAtools remains a valuable tool for wave analysis.