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Universal Measurement Contract Protocol (UMCP): Production-grade contract-first validation framework with GCD and RCFT. Core Axiom: What Returns Through Collapse Is Real. Features bidirectional cross-references, closure registries, and full reproducibility.

Project description

UMCP: Universal Measurement Contract Protocol

CI Python 3.11+ License: MIT Tests: 344 passing Version: 1.4.7

UMCP transforms computational experiments into auditable artifacts based on a foundational principle:

Core Axiom: "What Returns Through Collapse Is Real"

Reality is defined by what persists through collapse-reconstruction cycles. Only measurements that returnโ€”that survive transformation and can be reproducedโ€”receive credit as real, valid observations.

# Encoded in every UMCP contract
typed_censoring:
  no_return_no_credit: true

UMCP is a production-grade system for creating, validating, and sharing reproducible computational workflows. It enforces mathematical contracts, tracks provenance, generates cryptographic receipts, and validates results against frozen specifications.


๐Ÿ“Š Quick Start (5 Minutes)

Prerequisites

  • Python 3.11+ (3.12+ recommended)
  • pip (Python package installer)
  • git (version control)

Installation

# 1. Clone the repository
git clone https://github.com/calebpruett927/UMCP-Metadata-Runnable-Code.git
cd UMCP-Metadata-Runnable-Code

# 2. Create and activate virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# 3. Install production dependencies (includes numpy, scipy, pyyaml, jsonschema)
pip install -e ".[production]"

Optional installations:

# Install test dependencies (adds pytest, coverage tools)
pip install -e ".[test]"

# Install planned communication extensions (when implemented)
# pip install -e ".[api]"          # HTTP API (not yet implemented)
# pip install -e ".[viz]"          # Web UI (not yet implemented)
# pip install -e ".[communications]"  # All communication (not yet implemented)

# Install everything (production + test + future extensions)
pip install -e ".[all]"

Verify Installation

# System health check (should show HEALTHY status)
umcp health

# Run test suite (should show 344 tests passing)
pytest

# Quick validation test
umcp validate casepacks/hello_world

# Check installed version
python -c "import umcp; print(f'UMCP v{umcp.__version__}')"

Python API:

import umcp

# Validate a casepack
result = umcp.validate("casepacks/hello_world")

if result:  # Returns True if CONFORMANT
    print("โœ“ CONFORMANT")
    print(f"Errors: {result.error_count}, Warnings: {result.warning_count}")
else:
    print("โœ— NONCONFORMANT")
    for error in result.errors:
        print(f"  - {error}")

Expected output:

Status: HEALTHY
Schemas: 11
344 passed in ~13s

Launch Interactive Tools

# Visualization dashboard (port 8501)
umcp-visualize

# REST API server (port 8000)
umcp-api

# List extensions
umcp-ext list

๐ŸŽฏ What is UMCP?

UMCP is a measurement discipline for computational claims. It requires that every serious claim be published as a reproducible record (a row) with:

  • โœ… Declared inputs (raw measurements)
  • โœ… Frozen rules (mathematical contracts)
  • โœ… Computed outputs (invariants, closures)
  • โœ… Cryptographic receipts (SHA256 verification)

Operational Terms

Core Invariants (Tier-1: GCD Framework):

Symbol Name Definition Range Purpose
ฯ‰ Drift ฯ‰ = 1 - F [0,1] Collapse proximity
F Fidelity F = ฮฃ wแตขยทcแตข [0,1] Weighted coherence
S Entropy S = -ฮฃ wแตข[cแตข ln(cแตข) + (1-cแตข)ln(1-cแตข)] โ‰ฅ0 Disorder measure
C Curvature C = stddev(cแตข)/0.5 [0,1] Instability proxy
ฮบ Log-integrity ฮบ = ฮฃ wแตข ln(cแตข,ฮต) โ‰ค0 Composite stability
IC Integrity IC = exp(ฮบ) (0,1] System stability
ฯ„_R Return time Re-entry delay to domain Dฮธ โ„•โˆช{โˆž} Recovery measure

Extended Metrics (Tier-2: RCFT Framework):

Symbol Name Range Purpose
D๊œฐ Fractal dimension [1,3] Trajectory complexity
ฮจแตฃ Recursive field โ‰ฅ0 Self-referential strength
B Basin strength [0,1] Attractor robustness

๐Ÿ—๏ธ System Architecture

โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”
โ”‚                     UMCP WORKFLOW                           โ”‚
โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค
โ”‚                                                             โ”‚
โ”‚  1. INPUT                                                   โ”‚
โ”‚     โ””โ”€ raw_measurements.csv  (experimental data)            โ”‚
โ”‚                                                             โ”‚
โ”‚  2. INVARIANTS COMPUTATION                                  โ”‚
โ”‚     โ”œโ”€ ฯ‰ (drift)         โ”œโ”€ F (fidelity)                    โ”‚
โ”‚     โ”œโ”€ S (entropy)       โ””โ”€ C (curvature)                   โ”‚
โ”‚                                                             โ”‚
โ”‚  3. FRAMEWORK SELECTION                                     โ”‚
โ”‚     โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”      โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”          โ”‚
โ”‚     โ”‚ GCD (Tier-1)    โ”‚  OR  โ”‚ RCFT (Tier-2)    โ”‚          โ”‚
โ”‚     โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค      โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค          โ”‚
โ”‚     โ”‚ โ€ข Energy (E)    โ”‚      โ”‚ โ€ข Fractal (D๊œฐ)   โ”‚          โ”‚
โ”‚     โ”‚ โ€ข Collapse (ฮฆ)  โ”‚      โ”‚ โ€ข Recursive (ฮจแตฃ) โ”‚          โ”‚
โ”‚     โ”‚ โ€ข Flux (ฮฆ_gen)  โ”‚      โ”‚ โ€ข Pattern (ฮป, ฮ˜) โ”‚          โ”‚
โ”‚     โ”‚ โ€ข Resonance (R) โ”‚      โ”‚ + all GCD        โ”‚          โ”‚
โ”‚     โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜      โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜          โ”‚
โ”‚                                                             โ”‚
โ”‚  4. VALIDATION                                              โ”‚
โ”‚     โ”œโ”€ Contract conformance (schema validation)             โ”‚
โ”‚     โ”œโ”€ Regime classification (Stable/Collapse/Watch)        โ”‚
โ”‚     โ”œโ”€ Mathematical identities (F = 1-ฯ‰, IC โ‰ˆ exp(ฮบ))       โ”‚
โ”‚     โ””โ”€ Tolerance checks (within tol_seam, tol_id)           โ”‚
โ”‚                                                             โ”‚
โ”‚  5. OUTPUT                                                  โ”‚
โ”‚     โ”œโ”€ invariants.json (computed metrics)                   โ”‚
โ”‚     โ”œโ”€ closure_results.json (GCD/RCFT outputs)              โ”‚
โ”‚     โ”œโ”€ seam_receipt.json (validation status + SHA256)       โ”‚
โ”‚     โ””โ”€ CONFORMANT or NONCONFORMANT status                   โ”‚
โ”‚                                                             โ”‚
โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜

๐Ÿ“ฆ Framework Selection Guide

GCD (Generative Collapse Dynamics) - Tier-1

Best for: Energy/collapse analysis, phase transitions, basic regime classification

Closures (4):

  • energy_potential: Total system energy
  • entropic_collapse: Collapse potential
  • generative_flux: Generative flux
  • field_resonance: Boundary-interior resonance

Example:

umcp validate casepacks/gcd_complete

RCFT (Recursive Collapse Field Theory) - Tier-2

Best for: Trajectory complexity, memory effects, oscillatory patterns, multi-scale analysis

Closures (7 = 4 GCD + 3 RCFT):

  • All GCD closures +
  • fractal_dimension: Trajectory complexity (D๊œฐ โˆˆ [1,3])
  • recursive_field: Collapse memory (ฮจแตฃ โ‰ฅ 0)
  • resonance_pattern: Oscillation detection (ฮป, ฮ˜)

Example:

umcp validate casepacks/rcft_complete

Decision Matrix

Need Framework Why
Basic energy/collapse GCD Simpler, faster, foundational
Trajectory complexity RCFT Box-counting fractal dimension
History/memory RCFT Exponential decay field
Oscillation detection RCFT FFT-based pattern analysis
Maximum insight RCFT All GCD metrics + 3 new

๐Ÿ”Œ Built-In Features

UMCP includes two core features that enhance validation without requiring external dependencies:

1. Continuous Ledger (Automatic)

No install needed - built into core

# Automatically logs every validation run
cat ledger/return_log.csv

Purpose: Provides complete audit trail of all validations

  • Timestamp (ISO 8601 UTC)
  • Run status (CONFORMANT/NONCONFORMANT)
  • Key invariants (ฯ‰, C, stiffness)
  • Enables trend analysis and historical review

๐Ÿš€ Future Communication Extensions

The following communication extensions are planned for future implementation:

  • Contract Auto-Formatter (Entry point: umcp-format - not yet implemented)
  • REST API (HTTP/JSON interface for remote validation)
  • Web Dashboard (Interactive visualization with Streamlit)

These would provide standard protocol interfaces but are not required for core validation.

๐Ÿ“– See: EXTENSION_INTEGRATION.md | QUICKSTART_EXTENSIONS.md


โšก Performance

UMCP validation is optimized for production use:

Typical Validation Times:

  • Small casepack (hello_world): ~5-10ms
  • Medium casepack (GCD complete): ~15-30ms
  • Large casepack (RCFT complete): ~30-50ms
  • Full repository validation: ~100-200ms

Overhead vs. Basic Validation:

  • Speed: +71% slower than basic schema validation
  • Value: Contract conformance, closure verification, semantic rules, provenance tracking
  • Memory: <100MB for typical workloads

Benchmark Results (from benchmark_umcp_vs_standard.py):

UMCP Validator:
  Mean: 9.4ms per validation
  Median: 6.5ms
  Accuracy: 100% (400/400 errors caught, 0 false positives)
  
Additional Features:
  โœ“ Cryptographic receipts (SHA256)
  โœ“ Git commit tracking
  โœ“ Contract conformance
  โœ“ Closure verification
  โœ“ Full audit trail

Scaling: Validated on datasets with 1000+ validation runs. Ledger handles millions of entries efficiently (O(1) append).


Overhead vs. Basic Validation:

  • Speed: +71% slower than basic schema validation
  • Value: Contract conformance, closure verification, semantic rules, provenance tracking
  • Memory: <100MB for typical workloads

Benchmark Results (from benchmark_umcp_vs_standard.py):

UMCP Validator:
  Mean: 9.4ms per validation
  Median: 6.5ms
  Accuracy: 100% (400/400 errors caught, 0 false positives)
  
Additional Features:
  โœ“ Cryptographic receipts (SHA256)
  โœ“ Git commit tracking
  โœ“ Contract conformance
  โœ“ Closure verification
  โœ“ Full audit trail

Scaling: Validated on datasets with 1000+ validation runs. Ledger handles millions of entries efficiently (O(1) append).


๐Ÿ“š Documentation

Core Protocol

Indexing & Reference

Framework Documentation

Governance

Developer Guides


๐Ÿ“‚ Repository Structure

UMCP-Metadata-Runnable-Code/
โ”œโ”€โ”€ src/umcp/              # All Python code (API, CLI, extensions)
โ”œโ”€โ”€ tests/                 # Test suite (344 tests)
โ”œโ”€โ”€ scripts/               # Utility scripts
โ”œโ”€โ”€ contracts/             # Frozen contracts (GCD, RCFT)
โ”œโ”€โ”€ closures/              # Computational functions (7 closures)
โ”‚   โ”œโ”€โ”€ gcd/              # 4 GCD closures
โ”‚   โ””โ”€โ”€ rcft/             # 3 RCFT closures
โ”œโ”€โ”€ casepacks/             # Reproducible examples
โ”‚   โ”œโ”€โ”€ hello_world/      # Zero entropy example
โ”‚   โ”œโ”€โ”€ gcd_complete/     # GCD validation
โ”‚   โ””โ”€โ”€ rcft_complete/    # RCFT validation
โ”œโ”€โ”€ schemas/               # JSON schemas
โ”œโ”€โ”€ canon/                 # Canonical anchors
โ”œโ”€โ”€ ledger/                # Validation log (continuous append)
โ”œโ”€โ”€ integrity/             # Integrity metadata (SHA256)
โ”œโ”€โ”€ docs/                  # Documentation
โ””โ”€โ”€ pyproject.toml         # Project configuration (v1.4.0)

๐Ÿงช Testing

# All tests (344 total)
pytest

# Verbose output
pytest -v

# Specific framework
pytest -k "gcd"    # GCD tests
pytest -k "rcft"   # RCFT tests

# Coverage report
pytest --cov

# Fast subset
pytest tests/test_00_schemas_valid.py

Test Structure: 344 tests = 142 original + 56 RCFT + 146 integration/coverage


๐Ÿš€ Production Features

  • โœ… 344 tests passing (100% success rate)
  • โœ… Health checks: umcp health for system monitoring
  • โœ… Structured logging: JSON output for ELK/Splunk/CloudWatch
  • โœ… Performance metrics: Duration, memory, CPU tracking
  • โœ… Container ready: Docker + Kubernetes support
  • โœ… Cryptographic receipts: SHA256 verification
  • โœ… Zero technical debt: No TODO/FIXME/HACK markers
  • โœ… <5s validation: Fast for typical repositories

๐Ÿ“– See: Production Deployment Guide


๐Ÿ”’ Integrity & Automation

# Verify file integrity
sha256sum -c integrity/sha256.txt

# Update after changes
python scripts/update_integrity.py

# Check merge status
./scripts/check_merge_status.sh

Automated:

  • โœ… 344 tests on every commit (CI/CD)
  • โœ… Code formatting (ruff, black)
  • โœ… Type checking (mypy)
  • โœ… SHA256 tracking (12 files)

๐Ÿ“Š What's New in v1.4.0

Complete Protocol Infrastructure:

  • โœ… 8 major protocol documents (~5,500 lines)
  • โœ… Formal specification (34 lemmas, kernel definitions)
  • โœ… Publication standards (CasePack structure)
  • โœ… 344 tests passing (GCD + RCFT frameworks)
  • โœ… Production ready (manuscript-aligned)

๐Ÿ“– See: CHANGELOG.md | IMMUTABLE_RELEASE.md


๐Ÿค Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/name)
  3. Add tests for new functionality
  4. Ensure all tests pass (pytest)
  5. Validate code quality (ruff check, mypy)
  6. Commit changes (git commit -m 'feat: Description')
  7. Push to branch (git push origin feature/name)
  8. Open Pull Request

๐Ÿ“– See: Python Coding Standards | CONTRIBUTING.md


๐Ÿ“„ License

MIT License - see LICENSE for details.


๐Ÿ“ž Support & Resources


๐Ÿ† System Status

โ•”โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•—
โ•‘           UMCP PRODUCTION SYSTEM STATUS                   โ•‘
โ•šโ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•

  ๐ŸŽฏ Core Axiom:   "What Returns Through Collapse Is Real"
  ๐Ÿ” Canon:        UMCP.CANON.v1
  ๐Ÿ“œ Contract:     UMA.INTSTACK.v1
  ๐Ÿ“š DOI:          10.5281/zenodo.17756705 (PRE)
                   10.5281/zenodo.18072852 (POST)
                   10.5281/zenodo.18226878 (PACK)
  
  โš™๏ธ  Tier-1:      p=3  ฮฑ=1.0  ฮป=0.2  ฮท=0.001
  ๐ŸŽฏ Regimes:      Stable: ฯ‰<0.038  F>0.90
                   Collapse: ฯ‰โ‰ฅ0.30
  
  ๐Ÿ“Š Status:       CONFORMANT โœ…
  ๐Ÿงช Tests:        344 passing
  ๐Ÿ“ฆ Casepacks:    3 validated
  ๐Ÿ”’ Integrity:    12 files checksummed

โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”
     "No improvisation. Contract-first. Tier-1 reserved."
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”

๐ŸŽ“ Citation

Framework: UMCP (Universal Measurement Contract Protocol)
Author: Clement Paulus
Version: 1.4.0
Release: January 23, 2026
Tests: 344 passing
Integrity: SHA256 verified

Frameworks:

  • Tier-1: GCD (Generative Collapse Dynamics) - 4 closures
  • Tier-2: RCFT (Recursive Collapse Field Theory) - 7 closures

Built with โค๏ธ for reproducible science
"What Returns Through Collapse Is Real"

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