SigVarGen Documentation¶
Welcome to the documentation site for SigVarGen, a Python-based framework designed for generating and augmenting synthetic signals with realistic variations, interruptions, and noise profiles.
This framework designed to create multiple variants of a base 1D signal under the same environmental conditions. It allows the simulation of both idle-state signals and signals affected by external perturbations, enabling robust testing of algorithms in dynamic environments based on multiple measurements of the same event. Framework is suitable for time-series analysis, signal processing, and synthetic data generation in various domains such as sensor data modeling, embedded systems testing, and machine learning augmentation.