DLSFH Entropy & Coherence Diagnostics for Solar Flare and CME Risk

This repository accompanies the paper:

Entropy Rings and Fragmented Suns: A New Approach to Flare and CME Risk Detection
Antonios Valamontes, Kapodistrian Academy of Science

It contains the computational materials required for transparent, auditable reproduction of the paper’s diagnostic framework: a 20-node DLSFH partition of solar magnetograms, localized Shannon entropy estimation, coherence amplitude $\psi^\star_s = \exp(-S)$, SGCV fragmentation detection, entropy-ring identification via the DLSFH adjacency graph, and the composite flare/CME risk score $(R_{\mathrm{flare}})$.

The repository supports method verification, qualitative robustness assessment, and exploratory interpretation. It is not an operational forecasting system.

---

Contents

Notebooks (source of truth)

In this repository, “source of truth” denotes the authoritative computational implementations from which the figures, diagnostics, and numerical results reported in the paper are generated.

Core reproducibility and analysis

1. DLSFH_Entropy_Diagnostic_NOAA.ipynb
   Canonical end-to-end pipeline: magnetogram partitioning, entropy extraction, coherence computation, fragmentation detection, entropy-ring identification, and risk scoring.

2. DLSFH_Entropy_AutoAnalysis_Patched.ipynb
   Automated batch-style execution of the entropy–coherence pipeline with patched robustness handling. Used for repeated frame analysis and consistency checks.

3. Complete_DLSFH_Dual_Overlay_Test.ipynb
   Validation notebook demonstrating geometric alignment between DLSFH node placement and NOAA active-region overlays.

Sensitivity, robustness, and interpretation

4. DLSFH_PhysicsEntropy_Enhanced.ipynb
   Sensitivity and robustness exploration (coherence threshold variation, entropy binning effects, node-level trend stability).

5. DLSFH_PhysicsEntropy_AdvancedFinal_FINAL.ipynb
   Exploratory and interpretive analysis motivated by the DLSFH/SGCV framework. Not required to reproduce operational diagnostics.

---

Execution order

Recommended execution order depends on intent:

Reproduce the paper’s core results

1. DLSFH_Entropy_Diagnostic_NOAA.ipynb

Validate geometry and overlays

2. Complete_DLSFH_Dual_Overlay_Test.ipynb

Automated or repeated analyses

3. DLSFH_Entropy_AutoAnalysis_Patched.ipynb

Sensitivity and robustness

4. DLSFH_PhysicsEntropy_Enhanced.ipynb

Exploratory theoretical interpretation (optional)

5. DLSFH_PhysicsEntropy_AdvancedFinal_FINAL.ipynb

See RUN_ORDER.md for concise guidance.

---

Environment requirements

• Python 3.10+
• Jupyter Notebook or JupyterLab
• Standard scientific Python stack

Environment specifications:

• requirements.txt (pip)
• environment.yml (conda, optional)

Step-by-step instructions and expected outputs are documented in REPRODUCIBILITY.md.

---

Data sources

All analyses rely on publicly available solar magnetogram data, including NOAA and NSO/GONG products. No proprietary datasets are required.

---

Citation and archiving

Formal citation metadata and archival information are provided in:

• CITATION.cff
• zenodo.json

A versioned Zenodo DOI is minted upon release to ensure long-term traceability.

---

License

See LICENSE.

---

Contact

Antonios Valamontes
Kapodistrian Academy of Science
Email: avalamontes@Kapodistrian.edu.gr