WaLSAtools is an open-source library for analysing a wide variety of wave phenomena in time series data, including images and multi-dimensional datasets. It provides tools to extract meaningful insights from complex datasets and is applicable across diverse fields, including astrophysics, engineering, physical and environmental sciences, and biomedical studies, among others. The library is continuously expanding with new features and functionalities, ensuring it remains a valuable resource for the wave analysis research. The core of WaLSAtools is built upon Python, one of the most widely-used programming languages in science and engineering. This ensures accessibility and ease of use for a broad audience. We are actively developing counterparts in other popular languages to further enhance accessibility, enabling researchers from various backgrounds to leverage the power of WaLSAtools for their wave analysis needs. Currently, WaLSAtools is partially implemented in IDL, with plans to expand its functionality and extend to other programming languages in the future. Developed by the WaLSA Team, WaLSAtools was initially inspired by the intricate wave dynamics observed in the Sun's atmosphere. However, its applications extend far beyond solar physics, offering a versatile toolkit for anyone working with oscillatory signals. WaLSAtools promotes reproducibility and transparency in wave analysis. Its robust implementations of both fundamental and advanced techniques ensure consistent and trustworthy results, empowering researchers to delve deeper into the complexities of their data. Through its interactive interface, WaLSAtools guides users through the analysis process, providing the necessary information and tools to perform various types of wave analysis with ease. This repository is associated with a primer article titled "Wave analysis tools" in Nature Reviews Methods Primers (NRMP), showcasing its capabilities through detailed analyses of synthetic datasets. The examples/Worked_examples__NRMP directories (for both Python and IDL) contain reproducible codes for generating all figures presented in the NRMP article, serving as a practical guide for applying WaLSAtools to real-world analyses.

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: 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 Interactive Interface: User-friendly interface for easy access to analysis tools and parameters. Worked Examples: Reproducible examples demonstrating the application of WaLSAtools to synthetic datasets, as featured in the associated Nature Reviews Methods Primers article. Documentation: Comprehensive documentation covering installation, usage, and analysis methods (https://WaLSA.tools) 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.

We wish to acknowledge scientific discussions with the Waves in the Lower Solar Atmosphere (WaLSA; www.WaLSA.team) team, which has been supported by the Research Council of Norway (project no. 262622), The Royal Society (award no. Hooke18b/SCTM), and the International Space Science Institute (ISSI Team 502).