digamma-ep 1.3.1
Symbolic audit framework with CZVS, curvature zones, and compliance logic
License: MIT
Requires Python: >=3.11
Keywords: symbolic audit, CZVS, compliance, curvature, semantic drift, badge logic

#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

```bash
# 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