Symbolic audit framework with CZVS, curvature zones, and compliance logic
Project description
#Digamma Prime (digamma-ep)
Sistema simbólico para auditoria de modelos com métricas de divergência estrutural, temporal e algébrica.
Symbolic audit framework for comparing models, tracking divergence, and teaching algebraic structure.
Installation
📦 Installation
# Install from PyPI
pip install digamma-ep
# Install from source
git clone https://github.com/Cerene-Salt/Digamma-Prime-Framework.git
cd digamma-ep
pip install -e .
---
## 🔧 GitHub Patch Instructions
### 1. Edit `README.md`
Open the file and:
- Replace all `digamma_prime` references with `digamma-ep`
- Confirm the install block matches the one above
### 2. Stage and commit
```bash
git add README.md
git commit -m "Update install instructions to use pip install digamma-ep"
git push origin main
#A Quickstart
from epe_maria.metrics import phi, delta_phi, phi_star
f = lambda x: x**2 + 2*x + 1
g = lambda x: x**2 + x + 1
print(phi(f, g)) # Structural divergence
print(delta_phi(f, g)) # Rate divergence
print(phi_star(f, g)) # Fusion metric
#Documentation
Operator fichas: docs/operators.md
Symbolic manifesto: docs/epe_maria_manifesto.md
#Examples in:
python examples/symbolic_mollification_demo.py
python examples/kernel_divergence_trace.py
#Features
Symbolic comparison of models
CLI-ready modules for drift detection
Visualizations for φ and Δφ
Curriculum-ready structure
PyPI + GitHub CI/CD integration
Vision
Digamma Prime aims to become a universal symbolic standard for model auditing, drift detection, and interpretability — rooted in the algebraic legacy of Epe Piancé Maria II.
🤝 Contributing
We welcome contributions in tutorials, metrics, automation, and visualizations. See docs/roadmap.md and docs/style_guide.md to get started.
Tests:;
pytest test_benchmark.py
pytest test_monitor.py
bout
Created by Cerene Rúbio License: MIT Namespace: epe_maria/ — honoring the symbolic grammar of Epe Piancé Maria II
Release History
v0.2.0 – Symbolic Expansion
Added operator fichas and visual demos
Published examples/ and docs/ to PyPI
Preserved epe_maria/ namespace
Synced GitHub and PyPI
v0.1.7 – Initial PyPI Release
Core symbolic audit engine
Modules: benchmark, metrics, monitor, visuals
Python 3.11+ compatible
## 🧠 Conditional Zero-Variance State (CZVS)
Digamma Prime now supports symbolic analysis of CZVS — a rare, optimal system state defined by:
Ω_CZVS = Optimization(Var(ΔS)) = -b₀²
This equation is not always true — it represents a **Conditional State Equation (CSE)** that holds only when:
- `b₀ = 0` (constant term of g(x) is grounded)
- `Var(ΔS) = 0` (perfect structural consistency under weighting W)
Use `Ω_CZVS(f, g, W)` to compute the optimized variance of divergence, and `𝓜_CZVS(f, g, W)` to measure how close the system is to CZVS feasibility.
For full logic, see [`docs/module7.md`](docs/module7.md).
git clone https://github.com/Cerene-Salt/Digamma-Prime-Framework.git
cd digamma-ep
pip install -e .
from epe_maria import phi, Ω_CZVS, 𝓜_CZVS
def f(x): return x**2
def g(x): return x**2 + 0.01
W = np.eye(21)
score = 𝓜_CZVS(f, g, W)
print("Compliance Score:", score)
Testing
cd tests
python test_czvs.py
python test_czvs_extended.py
Thresholds
Symbolic compliance zones based on 𝓜_CZVS:
Zone Threshold Interpretation
✅ Robust 𝓜_CZVS < 0.2 Fully compliant
⚠️ Drifting 0.2 ≤ 𝓜_CZVS < 0.5 Transitional zone
❌ Non-compliant 𝓜_CZVS ≥ 0.5 Symbolic instability
Modules
core: kernel logic and symbolic scaffolding
metrics: divergence and fusion operators
monitor: drift, curvature, and alert logic
benchmark: audit traces and symbolic tests
temporal: time-based variation
visuals: plotting utilities
utils: padding, projection, and helpers
---
## ✅ GitHub Patch Instructions
1. Save the above as `README.md` in your repo root
2. Stage and commit all changes:
```bash
git add epe_maria/__init__.py tests/test_czvs.py tests/test_czvs_extended.py README.md
git commit -m "v1.1.0: CZVS module, compliance metric, extended tests, PyPI-ready README"
git push origin main
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