GEMLA: Γ–EML–α Transport Architecture SDK
Project description
GEMLA SDK
GEMLA is a Γ–EML–α transport architecture research SDK for observable path measures, lifted-phase diagnostics, adversarial-control gates, and finite transport-topology evaluation.
The SDK evaluates whether a supplied trajectory produces a stable lifted transport signature that passes diagnostic gates and rejects adversarial controls.
Current Status
Research preview: v0.5.0-practical-demos
Current capabilities:
- package import and editable install
- synthetic transport generation
- lifted phase construction
- RevA v2-SF gate evaluation
- wrong-sign, phase-shuffle, and residue-scramble controls
- anchor irregularity diagnostics
- integer winding diagnostics
- Markdown report export
- benchmark suite
- latent embedding evaluation
- V-JEPA-style embedding adapter
- industrial telemetry demo
- market microstructure demo
- cyber event transport demo
- pytest validation
- GitHub Actions CI
Core Pipeline
source trajectory
→ complex / latent surrogate
→ lifted phase Θ(t)
→ RevA v2-SF gate
→ adversarial controls
→ anchor diagnostics
→ winding diagnostics
→ PASS / FAIL verdict
Install
From the repository root:
pip install -e ".[dev]"
Check installation:
gemla --help
Quickstart
Run the minimal vertical-slice pipeline:
gemla evaluate
Or run the original Python example:
python examples/quickstart/01_minimal_pipeline.py
Expected output includes:
GEMLA Transport Evaluation
--------------------------
RevA v2-SF main pass: True
Wrong-sign rejected: True
Phase-shuffle rejected: True
Residue-scramble rejected: True
Final verdict: PASS
The command writes a Markdown report to:
reports/gemla_transport_report.md
CLI Usage
Default transport evaluation
gemla evaluate
Custom output:
gemla evaluate --output reports/custom_report.md
Custom synthetic trajectory:
gemla evaluate --n 1500 --t-max 100 --noise 0.02 --seed 9
Benchmark suite
gemla benchmark
Custom benchmark output directory:
gemla benchmark --output-dir benchmarks/results
Latent embedding evaluation
Run synthetic latent evaluation:
gemla evaluate-latent
Evaluate saved latent embeddings:
gemla evaluate-latent --input path/to/latents.npy
Expected input shape:
(n_steps, latent_dim)
V-JEPA-style embedding evaluation
Run synthetic V-JEPA-style demo:
gemla evaluate-vjepa --synthetic
Evaluate external embeddings:
gemla evaluate-vjepa --input path/to/embeddings.npy
GEMLA does not download or redistribute third-party model weights. The V-JEPA-style adapter treats external embeddings as surrogate transport trajectories.
Practical deployment demos
Industrial telemetry:
gemla demo-industrial
Market microstructure:
gemla demo-market
Cyber event transport:
gemla demo-cyber
Each command generates synthetic domain-proxy data, runs the GEMLA lifted-phase gate stack, rejects adversarial controls, and writes a Markdown report.
Python Examples
Minimal transport example:
python examples/quickstart/01_minimal_pipeline.py
Industrial telemetry:
python examples/industrial_telemetry/run_industrial_telemetry_demo.py
Market microstructure:
python examples/market_microstructure/run_market_microstructure_demo.py
Cyber event transport:
python examples/cybersecurity/run_cyber_transport_demo.py
Latent embeddings:
python examples/latent_embeddings/run_latent_transport_demo.py
V-JEPA-style embeddings:
python examples/vjepa_latent_transport/run_vjepa_latent_transport_demo.py
Run Tests
pytest
Repository Structure
src/gemla/
data/ synthetic and domain-proxy data generators
lifted/ lifted phase, anchors, spectral flatness, winding
gates/ RevA v2-SF gate evaluation
controls/ adversarial control generation
pipelines/ end-to-end GEMLA pipelines
integrations/ latent and V-JEPA-style adapters
benchmarks/ benchmark runner and result writers
reports/ Markdown report exporter
cli/ command-line interface
examples/
quickstart/
industrial_telemetry/
market_microstructure/
cybersecurity/
latent_embeddings/
vjepa_latent_transport/
tests/
unit and integration tests
Documentation
Interpretation
The current version is a finite diagnostic SDK. It does not make universal claims. A PASS means the supplied trajectory satisfied the current GEMLA gate stack and rejected the included adversarial controls. A PASS does not mean the system is safe, optimal, causal in a physical sense, or ready for high-stakes deployment.
Roadmap
Planned next steps:
- documentation site
- sample output reports
- richer benchmark summaries
- configurable gate thresholds
- real-data adapter examples
- hosted demo or release bundle
- public release preparation
License
See LICENSE.
Citation
See CITATION.cff.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file gemla-1.0.0.tar.gz.
File metadata
- Download URL: gemla-1.0.0.tar.gz
- Upload date:
- Size: 21.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
fb8df8116e809c397649daedc13c849110956f65a9163c6d64ce2f0d2f72e39e
|
|
| MD5 |
0e29b60e9fd7ae6ee49d946fc4070ce4
|
|
| BLAKE2b-256 |
d3417c41b6520ac463183407896d79d7c4638f71ffc3557f517ec75dbeab6561
|
File details
Details for the file gemla-1.0.0-py3-none-any.whl.
File metadata
- Download URL: gemla-1.0.0-py3-none-any.whl
- Upload date:
- Size: 26.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.10.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8a52fe927974e560d2c0cdde199c22fe2575c09c74d4098d8f8aa018d1caed12
|
|
| MD5 |
3a16de31da3609199c32455534a61fe8
|
|
| BLAKE2b-256 |
ac54ebffc20b922499697dc84e5a3b568f08cdd293fb32823e9d5766d585afe4
|