pyGSK: Generalized Spectral Kurtosis Toolkit¶

Overview¶

pyGSK (Generalized Spectral Kurtosis Toolkit) is a modular, open-source Python package for computing and visualizing the Generalized Spectral Kurtosis (SK) estimator.
It provides both programmatic and command-line interfaces for reproducible, open-science workflows.

Developed as part of the GEO OSE Track 1: SUNCAST — Software Unified Collaboration for Advancing Solar Tomography project, pyGSK serves as both a functional toolkit and a pedagogical example for sustainable, community-driven software development.

Key Features¶

⚙️ Computation of SK statistics for arbitrary integration parameters (M, N, d)
🧮 Threshold estimation from specified probability-of-false-alarm (PFA) levels
📊 Visualization tools for SK distributions, thresholds, and validation tests
💻 Command-line interface with subcommands:
sk-test — compute SK thresholds and optionally plot results
threshold-sweep — scan thresholds over PFA ranges
renorm-sk-test — perform renormalized SK analysis
🧠 Educational design: written for clarity, reproducibility, and reuse in future SUNCAST modules
📘 Examples and Notebooks: reproducible demonstrations under examples/, showcasing SK computation, validation, and simulation workflows

Quick Start¶

Install from PyPI:

pip install pygsk

Compute and print SK thresholds:

pygsk sk-test --M 128 --N 64 --pfa 1e-3

Or from Python:

from pygsk.thresholds import compute_sk_thresholds
lower, upper = compute_sk_thresholds(128, 64, 1.0, 1e-3)
print(lower, upper)

Documentation Contents¶

File	Description
install.md	Installation instructions and dependencies
usage.md	API and CLI examples for computing and plotting SK
cli_guide.md	Command-line usage and options
theory.md	Mathematical formulation and references
examples.md	Full example suite (scripts + notebooks)
dev_guide.md	Internal structure and contribution guide
dev_workflow.md	Development and release workflow

Citation¶

Nita, G. M. (2025). pyGSK: Generalized Spectral Kurtosis Toolkit. Zenodo.
https://doi.org/10.5281/zenodo.17336193

Theoretical background:

Nita, G. M., & Gary, D. E. (2010). The Generalized Spectral Kurtosis Estimator.
MNRAS Letters, 406(1), L60–L64.
https://doi.org/10.1111/j.1745-3933.2010.00882.x