SkillAgentSearch skills...

Pywaspgen

A toolkit for simulating stochastic and/or deterministic radio frequency aggregate spectrum (in both in-phase/quadrature and image formats) for testing sensing algorithms (e.g. detection, parameter estimation, classification).

Install / Use

/learn @vtnsi/Pywaspgen
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

Welcome to the PyWASPGEN Python Package!

<p align="center"> <img src="https://github.com/user-attachments/assets/3fcaaac3-1a49-4b4b-882b-4ae689fadbaa" width="600px"/> </p>

PyWASPGEN (Python Wideband Aggregate SPectrum GENerator) is intended as a native python dataset generation tool for creating synthetic aggregate radio frequency captures for initial testing and evaluation of spectrum sensing algorithms. The data produced by this tool is particularly useful for testing signal detection algorithms (i.e. where in time and frequency signals exist in the capture) as well as signal classification algorithms (i.e. what is the signaling format of the detected signal).

Installation

Use the package manager pip to install PyWASPGEN from the root directory of the repository.

pip install .

Usage

Generating synthetic radio frequency captures using PyWASPGEN can either be done directly through user-specified signal generation parameters or pseudorandomly through user-specified signal generation parameter ranges. Note: PyWASPGEN should be your first import to avoid issues with process spawning.

Direct Capture Generation (see example script below for detailed comments)

python examples/direct_generation.py

Pseudorandom Capture Generation (see example script below for detailed comments)

python examples/random_generation.py

Acknowledgements

PyWASPGEN is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract HQ003419D0003. The Systems Engineering Research Center (SERC) is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology. Any views, opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Department of Defense nor ASD(R&E).

Contributors

| Name | Role | Title | Email | | ---- | ---- | ----- | ----- | | William 'Chris' Headley | Developer | Associate Director, Spectrum Dominance Division, Virginia Tech National Security Institute | cheadley@vt.edu | | Caleb McIrvin | Developer | PhD Student, Spectrum Dominance Division, Virginia Tech National Security Institute | calebmcirvin111@vt.edu | | Michael 'Alex' Kyer | Developer | Software Engineer, Intelligent Systems Division, Virginia Tech National Security Institute | makyer19@vt.edu | | Jake 'Artic' Dennis | Developer | Research Associate, Spectrum Dominance Division, Virginia Tech National Security Institute | jacob.dennis@vt.edu |

License

MIT

Related Skills

View on GitHub
GitHub Stars7
CategoryDevelopment
Updated4mo ago
Forks2

Languages

Python

Security Score

87/100

Audited on Nov 5, 2025

No findings