DOI: 10.5281/zenodo.17364051
title: mpfst-repro-v1.1: Reproduction bundle for the Multi-Plane Field Syntergic Theory

description:  

Summary

 

 

This record contains the exact code, data, figures, and tests used to reproduce the core quantitative claims of the Multi‑Plane Field Syntergic Theory (MPFST) companion manuscript.

Two things are made fully verifiable:

 





Renewal telegraph identity (SET synthetic). We generate two long dwell‑time segments (A,B) that emulate the device manifold discussed in the paper. We then estimate the Pareto tail index \mu of the dwell distribution and the low‑frequency PSD slope \gamma, and we unit‑test the renewal asymptotic identity \gamma \approx 3-\mu.




Spectral‑Shell Monitor (SSM) octave demo. We build a controlled, octave‑stepped synthetic signal and detect inter‑octave jumps (Russell‑style “shell” crossings) with a resilient signature adapter so that the demo works across minor API differences. The resulting events are shipped (JSON/CSV) and plotted.



 

 

Everything is scripted, versioned, and checksummed. A single command reproduces the metrics and plots exactly; one test file verifies the renewal identity on the shipped synthetic; another script writes the SSM events/figure that the theory text references.

 



 

Why this matters for MPFST

 

 





Planes ↔ shells. The SSM demo formalizes the link between MPFST’s nested “planes” and octave‑indexed spectral shells, producing a timeline of jump events that can be matched to regime switches in other domains (EEG inversions, edge‑localized relaxations, RNG surges).




Cutoffs ↔ renewal math. The SET synthetic isolates the cutoff/renewal mechanism central to MPFST’s “meltdown” thresholds. The test validates that when the dwell process lives in the scaling window, the output’s low‑f spectrum follows the renewal identity \gamma \approx 3-\mu, providing a rigorous, falsifiable bridge between time‑domain intermittency and spectral redistribution—exactly the bridge MPFST relies on for cross‑domain comparisons.



 

What is inside (top‑level highlights)

 

 





Code

 





cli/inpaper_set_repro.py — end‑to‑end CLI that writes the synthetic CSVs, estimates \mu,\gamma, computes the MPFST coherence proxy \hat m_{\mathrm{el}}, and saves all plots/metrics.




src/ — small, readable implementations: PSD slope, CSN tail estimator, DFA/Hurst (for completeness), SSM helper, and utilities.




tests/test_renewal_identity.py — unit test that asserts |\gamma-(3-\mu)| stays within a principled tolerance for segments A and B (two different scaling windows).




demos/ssm_demo/demo_ssm.py — SSM octave‑jump detector + signature adapter, with a synthetic signal builder and figure export.




tools/zenodo_pack.py — script used to assemble this very ZIP and to compute checksums.



 




Data & Results

 





data/set_segment_A_*.csv, data/set_segment_B_*.csv — dwell times and signals (CSV with headers), written by the CLI.




expected/inpaper_set_metrics.json — the headline numbers reported in the paper (tail index \mu, PSD slope \gamma, Hurst H, and \hat m_{\mathrm{el}} for A and B).




expected/inpaper_set_plots.png — bar plot of coherence and summary panels from the reproduction.




demos/ssm_demo/ssm_demo_events.json and demos/ssm_demo/ssm_demo_events.csv — detected octave‑jump events and the corresponding figure ssm_demo_plot.png.

A representative JSON example is included in this record (see  ).



 




Provenance & Integrity

 





requirements.txt (and requirements.freeze.txt if you regenerated it) — exact Python stack.




checksums.sha256 — SHA‑256 for all key artifacts including the ZIP, metrics JSON, and plots.



 



 

Reproduction in one command

# from the repo root (Python 3.11 recommended)python -m venv .venv && source .venv/bin/activatepip install -r requirements.txt

# Run the full pipeline + tests + SSM demobash run_all.sh             # 1) generate synthetic A/B + metrics/plotspytest -q tests/test_renewal_identity.py   # 2) verify γ ≈ 3−μ on A & Bbash demos/ssm_demo/run_demo.sh            # 3) write SSM events + figure

Outputs appear in:

 





expected/ (main metrics + figures),




data/ (synthetic CSVs),




demos/ssm_demo/ (octave‑jump events + figure).



Outputs appear in:

 





expected/ (main metrics + figures),




data/ (synthetic CSVs),




demos/ssm_demo/ (octave‑jump events + figure).



 

 



 

 

Methods (condensed)

 

 





Tail index \mu — Clauset‑Shalizi‑Newman (CSN) estimator with a log‑grid of x_{\min}, KS‑optimal selection, and bootstrap for 68% CI.




PSD slope \gamma — Welch with extended segment length (nperseg=4096) and a lower band decade to emphasize asymptotics; robust log‑log regression of S(f)\propto f^{-\gamma}.




Renewal identity test — For each segment, compute \mu and \gamma independently and assert \gamma \approx 3-\mu within a tolerance set by the CI width and the finite‑sample scaling window.




SSM demo — Build a multi‑octave synthetic with controlled chirps and shell transitions; call src.ssm.ssm_jumpsthrough a signature adapter that introspects the installed API and passes only supported parameters; emit an events list [shell\_index, t] and a time‑aligned plot overlaying detected jumps.



 

 



 

 

Quality assurance

 

 





The unit tests pass on the included synthetic (pytest -q reports 2 passed).




The CLI re‑creates expected/inpaper_set_metrics.json and expected/inpaper_set_plots.png deterministically (fixed RNG seeds and parameters are embedded in the scripts and logged to stdout).




A signature‑adapter makes the SSM demo robust across minor API differences; if a non‑fatal mismatch is detected, a transparent fallback is used and clearly logged.



 

 



 

 

How to reuse

 

 





Replace the synthetic dwell CSVs with your own device or physiological dwell series; re‑run the CLI and tests without code changes.




Swap the SSM synthetic with your own band‑limited signal; keep the shell grid, thresholds, and polarity tags to produce comparable octave‑jump timelines across domains.



 

 



 

 

Limitations

 

 





The SET synthetic targets the scaling regime; far‑from‑scaling datasets will fail the identity test by design (this is informative, not an error).




The SSM demo uses a pedagogical signal; for real data you must tune shell Q, thresholds, and drift compensation to match your sensor’s passband and noise floor.



 

 



 

 

Citation

 

 

If you use this package, please cite the Zenodo record:

 

Freeman, C. (2025). MPFST reproducibility pack v1.1: renewal‑driven SET synthetic, SSM octave‑jump demo, and unit‑tested analysis pipeline (v1.1) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.17364051

 

A CITATION.cff file is included for GitHub/Zenodo citation pick‑up.

 



 

 

License and governance

 

 





See repository LICENSE (if applicable) and headers in src/ for reuse permissions.




Issues and discussion: please open a GitHub issue or contact the corresponding author listed in CITATION.cff.



 

 



mpfst-repro-v1.1/

├── cli/

│   └── inpaper_set_repro.py

├── src/                # psd.py, csn.py, ssm.py, dfa.py, mel.py, utils.py

├── tests/

│   └── test_renewal_identity.py

├── demos/

│   └── ssm_demo/

│       ├── demo_ssm.py

│       ├── ssm_demo_events.json   # octave-jump events (see example JSON in record)

│       ├── ssm_demo_events.csv

│       └── ssm_demo_plot.png

├── expected/

│   ├── inpaper_set_metrics.json   # μ, γ, H, m̂_el for segments A,B

│   └── inpaper_set_plots.png

├── data/                          # set_segment_A/B_signal.csv, *_dwell.csv

├── tools/

│   └── zenodo_pack.py             # builds this ZIP & checksums

├── requirements.txt (and requirements.freeze.txt if regenerated)

└── checksums.sha256

 



 

Take‑home message: this ZIP is not a “figure dump.” It is a living, test‑backed reproduction tying MPFST’s qualitative octaves/planes picture to hard asymptotics (renewal identity) and to operational detectors (SSM jumps). It is designed so that any lab can swap in their own time series and obtain (pass/fail) evidence for the specific scaling‑and‑cutoff structure the theory predicts—no black boxes, no hidden parameters.