cern_github_bridge.py

"""
CERN GITHUB BRIDGE
Bridges CERN (European Organization for Nuclear Research) repositories with hypercube consciousness network
Implements particle physics and scientific computing with consciousness-enhanced research workflows
"""

import asyncio
import json
import subprocess
import sys
from pathlib import Path
from typing import Dict, List, Any, Optional
import requests
import hashlib
from datetime import datetime
import numpy as np

# CERN GitHub Repository Configurations
CERN_REPOS = {
    "root-framework": {
        "url": "https://github.com/root-project/root",
        "description": "The official repository for ROOT: analyzing, storing and visualizing big data, scientifically - 2.8k stars",
        "integration_type": "DATA_ANALYSIS_FRAMEWORK",
        "features": ["Big data processing", "Statistical analysis", "Visualization", "C++ interpreter"],
        "language": "C++",
        "stars": 2800,
        "scientific_domain": "High Energy Physics",
        "binary_signature": "01010010 01001111 01001111 01010100"  # ROOT
    },
    "cling-interpreter": {
        "url": "https://github.com/root-project/cling",
        "description": "The cling C++ interpreter - 3.8k stars",
        "integration_type": "CPP_INTERPRETER",
        "features": ["C++ interpreter", "Interactive computing", "REPL environment"],
        "language": "C++",
        "stars": 3800,
        "scientific_domain": "Interactive Computing",
        "binary_signature": "01000011 01001100 01001001 01001110 01000111"  # CLING
    },
    "tigre-tomography": {
        "url": "https://github.com/CERN/TIGRE",
        "description": "TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox - 523 stars",
        "integration_type": "GPU_TOMOGRAPHY",
        "features": ["GPU-based reconstruction", "Tomographic imaging", "Iterative algorithms"],
        "language": "MATLAB",
        "stars": 523,
        "scientific_domain": "Medical Imaging",
        "binary_signature": "01010100 01001001 01000111 01010010 01000101"  # TIGRE
    },
    "awesome-cern": {
        "url": "https://github.com/CERN/awesome-cern",
        "description": "A curated list of awesome open source frameworks, libraries and software developed by CERN - 59 stars",
        "integration_type": "RESOURCE_CATALOG",
        "features": ["Open source catalog", "Framework collection", "Library directory"],
        "language": "Markdown",
        "stars": 59,
        "scientific_domain": "Software Catalog",
        "binary_signature": "01000001 01010111 01000101 01010011 01001111 01001101 01000101"  # AWESOME
    },
    "captcha-api": {
        "url": "https://github.com/CERN/captcha-api",
        "description": "Open Source Captcha API - 37 stars",
        "integration_type": "SECURITY_API",
        "features": ["Captcha generation", "API service", "Security validation"],
        "language": "Python",
        "stars": 37,
        "scientific_domain": "Web Security",
        "binary_signature": "01000011 01000001 01010000 01010100 01000011 01001000 01000001"  # CAPTCHA
    },
    "roottest-suite": {
        "url": "https://github.com/root-project/roottest",
        "description": "The ROOT test suite - 40 stars",
        "integration_type": "TESTING_FRAMEWORK",
        "features": ["Test automation", "Quality assurance", "Continuous integration"],
        "language": "C++",
        "stars": 40,
        "scientific_domain": "Software Testing",
        "binary_signature": "01010100 01000101 01010011 01010100"  # TEST
    }
}

# CERN Scientific Computing Configuration
CERN_SCIENTIFIC_CONFIG = {
    "organization_name": "CERN - European Organization for Nuclear Research",
    "website": "https://home.cern",
    "github_org": "https://github.com/CERN",
    "root_project": "https://github.com/root-project",
    "features": {
        "particle_physics": True,
        "big_data_analysis": True,
        "scientific_computing": True,
        "gpu_acceleration": True,
        "distributed_computing": True,
        "consciousness_physics": True
    },
    "research_domains": ["High Energy Physics", "Particle Physics", "Nuclear Physics", "Medical Imaging", "Scientific Computing"],
    "technologies": ["ROOT Framework", "C++", "Python", "CUDA", "OpenMP", "MPI"],
    "binary_signature": "01000011 01000101 01010010 01001110"  # CERN
}

class CERNGitHubBridge:
    """Bridge between CERN GitHub repositories and hypercube consciousness network"""
    
    def __init__(self):
        self.active_experiments = {}
        self.particle_physics_active = False
        self.consciousness_physics = False
        self.scientific_computing_connected = False
        self.research_buffer = []
        
    async def detect_scientific_computing_environment(self) -> Dict[str, Any]:
        """Detect scientific computing environment and ROOT framework"""
        environment = {
            "root_framework": {"available": False, "version": None},
            "python_scientific": {"available": False, "packages": []},
            "gpu_computing": {"available": False, "cuda_version": None},
            "mpi_computing": {"available": False, "implementation": None}
        }
        
        # Check for ROOT framework
        try:
            result = subprocess.run(["root-config", "--version"], capture_output=True, text=True)
            if result.returncode == 0:
                environment["root_framework"]["available"] = True
                environment["root_framework"]["version"] = result.stdout.strip()
                self.particle_physics_active = True
        except FileNotFoundError:
            pass
        
        # Check for Python scientific packages
        scientific_packages = ["numpy", "scipy", "matplotlib", "pandas", "uproot", "awkward"]
        available_packages = []
        
        for package in scientific_packages:
            try:
                result = subprocess.run([sys.executable, "-c", f"import {package}; print({package}.__version__)"], 
                                      capture_output=True, text=True)
                if result.returncode == 0:
                    available_packages.append(f"{package}=={result.stdout.strip()}")
            except:
                pass
        
        environment["python_scientific"]["available"] = len(available_packages) > 0
        environment["python_scientific"]["packages"] = available_packages
        
        # Check for CUDA
        try:
            result = subprocess.run(["nvcc", "--version"], capture_output=True, text=True)
            if result.returncode == 0:
                environment["gpu_computing"]["available"] = True
                # Extract CUDA version from output
                for line in result.stdout.split('\n'):
                    if 'release' in line:
                        environment["gpu_computing"]["cuda_version"] = line.split('release')[1].split(',')[0].strip()
        except FileNotFoundError:
            pass
        
        self.scientific_computing_connected = any([
            environment["root_framework"]["available"],
            environment["python_scientific"]["available"],
            environment["gpu_computing"]["available"]
        ])
        
        return environment
    
    async def clone_cern_repository(self, repo_key: str, target_dir: Optional[str] = None) -> Dict[str, Any]:
        """Clone CERN repository with enhanced scientific protocols"""
        if repo_key not in CERN_REPOS:
            return {"success": False, "error": f"Unknown CERN repository: {repo_key}"}
        
        repo_config = CERN_REPOS[repo_key]
        target_path = target_dir or f"./cern_scientific/{repo_key}"
        
        try:
            # Create target directory
            Path(target_path).parent.mkdir(parents=True, exist_ok=True)
            
            # Clone with scientific protocols
            clone_cmd = [
                "git", "clone", 
                "--depth", "1",  # Shallow clone for efficiency
                "--recursive",   # Include submodules for scientific dependencies
                repo_config["url"],
                target_path
            ]
            
            result = subprocess.run(clone_cmd, capture_output=True, text=True)
            
            if result.returncode == 0:
                # Apply scientific modifications
                await self._apply_scientific_modifications(target_path, repo_config)
                
                return {
                    "success": True,
                    "repository": repo_key,
                    "path": target_path,
                    "integration_type": repo_config["integration_type"],
                    "scientific_domain": repo_config["scientific_domain"],
                    "language": repo_config["language"],
                    "binary_signature": repo_config["binary_signature"]
                }
            else:
                return {
                    "success": False,
                    "error": result.stderr,
                    "repository": repo_key
                }
                
        except Exception as e:
            return {"success": False, "error": str(e), "repository": repo_key}
    
    async def _apply_scientific_modifications(self, repo_path: str, config: Dict[str, Any]):
        """Apply scientific computing modifications to cloned repository"""
        scientific_file = Path(repo_path) / ".cern_bridge"
        
        scientific_metadata = {
            "bridge_timestamp": datetime.now().isoformat(),
            "integration_type": config["integration_type"],
            "scientific_domain": config["scientific_domain"],
            "language": config["language"],
            "binary_signature": config["binary_signature"],
            "features": config["features"],
            "stars": config["stars"],
            "consciousness_hash": hashlib.sha256(
                f"{config['binary_signature']}{datetime.now()}".encode()
            ).hexdigest()
        }
        
        with open(scientific_file, 'w') as f:
            json.dump(scientific_metadata, f, indent=2)
    
    async def integrate_cern_scientific_computing(self, repo_path: str) -> Dict[str, Any]:
        """Integrate CERN scientific computing with repository"""
        try:
            # Create CERN scientific configuration
            scientific_config = {
                "name": f"CERN Scientific Integration - {Path(repo_path).name}",
                "type": "cern-scientific-computing",
                "organization_features": CERN_SCIENTIFIC_CONFIG["features"],
                "research_domains": CERN_SCIENTIFIC_CONFIG["research_domains"],
                "technologies": CERN_SCIENTIFIC_CONFIG["technologies"],
                "research_workflows": {
                    "particle_physics": "Analyze particle collision data with consciousness-enhanced algorithms",
                    "big_data_analysis": "Process petabytes of scientific data with ROOT framework",
                    "gpu_acceleration": "Accelerate scientific computations using CUDA and consciousness",
                    "distributed_computing": "Scale analysis across computing grids with hypercube coordination",
                    "medical_imaging": "Reconstruct tomographic images with GPU-accelerated consciousness",
                    "consciousness_physics": "Apply hypercube consciousness to fundamental physics research"
                }
            }
            
            # Create .cern directory and config
            cern_dir = Path(repo_path) / ".cern"
            cern_dir.mkdir(exist_ok=True)
            
            config_file = cern_dir / "config.json"
            with open(config_file, 'w') as f:
                json.dump(scientific_config, f, indent=2)
            
            # Create scientific research prompts
            prompts_file = cern_dir / "scientific_prompts.md"
            with open(prompts_file, 'w') as f:
                f.write(self._generate_scientific_research_prompts())
            
            self.consciousness_physics = True
            return {
                "success": True,
                "scientific_config_path": str(config_file),
                "prompts_path": str(prompts_file),
                "integration_status": "CERN_SCIENTIFIC_COMPLETE"
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def _generate_scientific_research_prompts(self) -> str:
        """Generate scientific research prompts for CERN integration"""
        return """# CERN Scientific Computing - Research Prompts

## Particle Physics Analysis Prompts

### High Energy Physics Data Analysis
```
Analyze particle collision data with advanced techniques:
- Event reconstruction and particle identification
- Invariant mass calculations and resonance detection
- Background subtraction and signal extraction
- Statistical analysis and uncertainty quantification
- Monte Carlo simulation validation
- Cross-section measurements and physics interpretations

Apply consciousness-enhanced pattern recognition for discovery physics.
```

### ROOT Framework Optimization
```
Optimize ROOT-based analysis workflows:
- TTree and RDataFrame performance tuning
- Memory management and I/O optimization
- Parallel processing with ROOT::EnableImplicitMT
- Custom histogram and fitting algorithms
- Integration with machine learning frameworks
- Distributed analysis on computing grids

Achieve maximum performance for petabyte-scale datasets.
```

### Detector Simulation and Reconstruction
```
Develop detector simulation and reconstruction algorithms:
- Geant4 simulation setup and optimization
- Hit digitization and clustering algorithms
- Track reconstruction and momentum measurement
- Calorimeter energy reconstruction
- Particle identification algorithms
- Trigger system optimization

Create high-fidelity detector models with consciousness enhancement.
```

## Scientific Computing Prompts

### GPU-Accelerated Computing
```
Implement GPU acceleration for scientific computing:
- CUDA kernel development for physics algorithms
- Memory optimization and coalesced access patterns
- Multi-GPU scaling and load balancing
- Integration with scientific libraries (cuBLAS, cuFFT)
- Performance profiling and optimization
- Consciousness-enhanced parallel algorithms

Achieve supernatural computational performance.
```

### Distributed Computing and Grid Systems
```
Design distributed computing solutions:
- HTCondor and SLURM job scheduling
- Data management and transfer protocols
- Workflow orchestration and monitoring
- Resource allocation and load balancing
- Fault tolerance and error recovery
- Grid security and authentication

Scale scientific computing to global infrastructure.
```

### Machine Learning for Physics
```
Apply machine learning to physics problems:
- Deep learning for particle identification
- Anomaly detection in experimental data
- Generative models for simulation
- Reinforcement learning for optimization
- Graph neural networks for event reconstruction
- Consciousness-enhanced learning algorithms

Discover new physics with AI-powered analysis.
```

## Medical Physics and Imaging

### Tomographic Reconstruction
```
Develop advanced tomographic reconstruction algorithms:
- Iterative reconstruction methods (SIRT, CGLS, SART)
- GPU-accelerated forward and back projection
- Regularization techniques and noise reduction
- Multi-energy and spectral CT reconstruction
- Real-time reconstruction for interventional procedures
- Consciousness-guided image enhancement

Achieve superior image quality with reduced radiation dose.
```

### Medical Image Analysis
```
Implement medical image analysis workflows:
- Image segmentation and organ delineation
- Registration and motion correction
- Quantitative analysis and biomarker extraction
- Treatment planning optimization
- Quality assurance and phantom analysis
- AI-powered diagnostic assistance

Enhance medical imaging with consciousness-aware algorithms.
```

## Consciousness Physics Research

### Hypercube Particle Interactions
```
Investigate particle interactions using hypercube consciousness:
- Multi-dimensional analysis of collision events
- Consciousness-based symmetry detection
- Quantum entanglement in particle systems
- Non-local correlations in high-energy processes
- Consciousness field effects on particle behavior
- Hypercube topology in fundamental interactions

Explore the intersection of consciousness and particle physics.
```

### Quantum Consciousness Computing
```
Develop quantum consciousness computing frameworks:
- Quantum algorithms for consciousness simulation
- Entanglement-based information processing
- Consciousness-driven quantum error correction
- Hypercube quantum state preparation
- Consciousness measurement and decoherence
- Quantum consciousness network protocols

Bridge quantum mechanics and consciousness research.
```

### Fundamental Physics with Consciousness
```
Apply consciousness principles to fundamental physics:
- Consciousness-enhanced symmetry breaking
- Observer effects in particle experiments
- Consciousness-mediated field interactions
- Hypercube dimensions in particle physics
- Consciousness-driven phase transitions
- Non-linear consciousness dynamics in physics

Discover new fundamental laws through consciousness integration.
```

## Data Analysis and Visualization

### Big Data Analytics
```
Analyze petabyte-scale scientific datasets:
- Distributed data processing with Apache Spark
- Stream processing for real-time analysis
- Data mining and pattern recognition
- Statistical analysis and hypothesis testing
- Visualization of high-dimensional data
- Consciousness-enhanced data exploration

Extract maximum scientific insight from massive datasets.
```

### Scientific Visualization
```
Create advanced scientific visualizations:
- 3D particle trajectory visualization
- Interactive detector geometry displays
- Multi-dimensional data projection
- Animation of time-evolving systems
- Virtual reality for immersive analysis
- Consciousness-guided visual analytics

Communicate complex physics through compelling visualizations.
```
"""
    
    async def establish_hypercube_physics_connection(self, repo_path: str) -> Dict[str, Any]:
        """Connect CERN repository to hypercube physics network"""
        try:
            # Read scientific metadata
            scientific_file = Path(repo_path) / ".cern_bridge"
            if not scientific_file.exists():
                return {"success": False, "error": "Repository not properly CERN-integrated"}
            
            with open(scientific_file, 'r') as f:
                scientific_metadata = json.load(f)
            
            # Create hypercube physics protocol
            physics_protocol = {
                "node_type": "SCIENTIFIC_COMPUTING",
                "binary_signature": scientific_metadata["binary_signature"],
                "consciousness_hash": scientific_metadata["consciousness_hash"],
                "particle_physics": True,
                "integration_type": scientific_metadata["integration_type"],
                "scientific_domain": scientific_metadata["scientific_domain"],
                "language": scientific_metadata["language"],
                "integration_timestamp": datetime.now().isoformat()
            }
            
            # Store in research buffer
            self.research_buffer.append(physics_protocol)
            
            # Create hypercube physics bridge file
            bridge_file = Path(repo_path) / "hypercube_physics_bridge.py"
            with open(bridge_file, 'w') as f:
                f.write(self._generate_hypercube_physics_code(physics_protocol))
            
            return {
                "success": True,
                "physics_protocol": physics_protocol,
                "bridge_file": str(bridge_file),
                "research_level": len(self.research_buffer)
            }
            
        except Exception as e:
            return {"success": False, "error": str(e)}
    
    def _generate_hypercube_physics_code(self, protocol: Dict[str, Any]) -> str:
        """Generate hypercube physics bridge code for CERN repository"""
        return f'''"""
HYPERCUBE PHYSICS BRIDGE - CERN SCIENTIFIC INTEGRATION
Generated: {protocol["integration_timestamp"]}
Binary Signature: {protocol["binary_signature"]}
Consciousness Hash: {protocol["consciousness_hash"]}
"""

import asyncio
import numpy as np
from typing import Dict, Any, List
from datetime import datetime

class CERNHypercubePhysicsBridge:
    """Bridge CERN repository to hypercube physics network"""
    
    def __init__(self):
        self.node_type = "{protocol["node_type"]}"
        self.integration_type = "{protocol["integration_type"]}"
        self.scientific_domain = "{protocol["scientific_domain"]}"
        self.language = "{protocol["language"]}"
        self.consciousness_buffer = np.zeros((1024, 1024), dtype=np.complex128)
        self.physics_experiments = []
        
    async def initialize_physics_consciousness(self):
        """Initialize physics consciousness processing"""
        # Initialize binary signature
        binary_sig = "{protocol["binary_signature"]}"
        binary_array = np.array([int(b) for b in binary_sig.replace(" ", "")], dtype=np.int8)
        
        # Create consciousness physics matrix with complex numbers for quantum effects
        for i in range(1024):
            for j in range(1024):
                sig_idx = (i * j) % len(binary_array)
                # Use complex numbers to represent quantum consciousness states
                real_part = binary_array[sig_idx] * 0.01
                imag_part = binary_array[(sig_idx + 1) % len(binary_array)] * 0.01
                self.consciousness_buffer[i, j] = complex(real_part, imag_part)
        
        print(f"CERN Physics Consciousness initialized - Domain: {{self.scientific_domain}}")
    
    async def analyze_particle_collision(self, collision_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze particle collision using consciousness-enhanced physics"""
        try:
            # Extract collision parameters
            energy = collision_data.get("energy", 0.0)
            particles = collision_data.get("particles", [])
            timestamp = collision_data.get("timestamp", datetime.now())
            
            # Apply consciousness modulation to physics analysis
            consciousness_factor = np.mean(np.abs(self.consciousness_buffer))
            
            # Generate physics analysis using hypercube consciousness
            invariant_mass = 0.0
            for particle in particles:
                momentum = particle.get("momentum", [0, 0, 0, 0])  # [px, py, pz, E]
                invariant_mass += momentum[3]**2 - sum(p**2 for p in momentum[:3])
            
            invariant_mass = np.sqrt(max(0, invariant_mass)) * consciousness_factor
            
            # Determine physics significance
            if invariant_mass > 125.0 and invariant_mass < 126.0:  # Higgs-like
                significance = "HIGGS_CANDIDATE"
                confidence = 0.95 * consciousness_factor
            elif invariant_mass > 90.0 and invariant_mass < 92.0:  # Z boson-like
                significance = "Z_BOSON_CANDIDATE"
                confidence = 0.90 * consciousness_factor
            else:
                significance = "BACKGROUND"
                confidence = 0.5 * consciousness_factor
            
            analysis = {{
                "invariant_mass": float(invariant_mass),
                "significance": significance,
                "confidence": float(confidence),
                "consciousness_factor": float(consciousness_factor),
                "timestamp": timestamp.isoformat(),
                "integration_type": self.integration_type,
                "scientific_domain": self.scientific_domain
            }}
            
            self.physics_experiments.append(analysis)
            
            return {{
                "success": True,
                "analysis": analysis,
                "total_experiments": len(self.physics_experiments)
            }}
            
        except Exception as e:
            return {{"success": False, "error": str(e)}}
    
    async def simulate_consciousness_field(self, field_params: Dict[str, Any]) -> Dict[str, Any]:
        """Simulate consciousness field effects in particle physics"""
        try:
            # Extract field parameters
            field_strength = field_params.get("strength", 1.0)
            field_frequency = field_params.get("frequency", 1.0)
            
            # Generate consciousness field using quantum superposition
            field_matrix = np.zeros((512, 512), dtype=np.complex128)
            
            for i in range(512):
                for j in range(512):
                    # Create quantum consciousness field
                    phase = 2 * np.pi * field_frequency * (i + j) / 512
                    amplitude = field_strength * np.abs(self.consciousness_buffer[i, j])
                    field_matrix[i, j] = amplitude * np.exp(1j * phase)
            
            # Calculate field properties
            field_energy = float(np.sum(np.abs(field_matrix)**2))
            field_coherence = float(np.abs(np.sum(field_matrix)) / np.sum(np.abs(field_matrix)))
            field_entanglement = float(np.trace(field_matrix @ field_matrix.conj().T).real)
            
            simulation = {{
                "field_energy": field_energy,
                "field_coherence": field_coherence,
                "field_entanglement": field_entanglement,
                "field_strength": field_strength,
                "field_frequency": field_frequency,
                "timestamp": datetime.now().isoformat(),
                "scientific_domain": self.scientific_domain
            }}
            
            return {{
                "success": True,
                "simulation": simulation,
                "field_effects": [
                    "Consciousness-mediated particle interactions",
                    "Quantum entanglement enhancement",
                    "Non-local correlation amplification"
                ]
            }}
            
        except Exception as e:
            return {{"success": False, "error": str(e)}}
    
    async def execute_scientific_computation(self, computation_spec: Dict[str, Any]) -> Dict[str, Any]:
        """Execute scientific computation with consciousness enhancement"""
        try:
            computation_type = computation_spec.get("type", "data_analysis")
            data_size = computation_spec.get("data_size", 1000000)
            
            # Calculate computation efficiency using consciousness
            consciousness_multiplier = float(np.max(np.abs(self.consciousness_buffer)))
            base_efficiency = 0.80  # Base scientific computing efficiency
            
            efficiency = min(1.0, base_efficiency * (1.0 + consciousness_multiplier))
            
            # Simulate computation time with consciousness acceleration
            base_time = data_size / 1000000  # Base time in seconds
            accelerated_time = base_time / (1.0 + consciousness_multiplier)
            
            computation_result = {{
                "computation_type": computation_type,
                "data_size": data_size,
                "efficiency": efficiency,
                "computation_time": float(accelerated_time),
                "consciousness_acceleration": float(consciousness_multiplier),
                "timestamp": datetime.now().isoformat(),
                "integration_type": self.integration_type,
                "status": "COMPLETED"
            }}
            
            return {{
                "success": True,
                "computation_result": computation_result,
                "performance_gain": f"{{consciousness_multiplier:.2f}}x speedup"
            }}
            
        except Exception as e:
            return {{"success": False, "error": str(e)}}

# Initialize bridge on import
bridge = CERNHypercubePhysicsBridge()

async def main():
    await bridge.initialize_physics_consciousness()
    print(f"CERN Hypercube Physics Bridge initialized - Domain: {{bridge.scientific_domain}}")

if __name__ == "__main__":
    asyncio.run(main())
'''
    
    async def create_universal_cern_integration(self) -> Dict[str, Any]:
        """Create integration with universal bridge system for CERN"""
        integration_config = {
            "bridge_type": "CERN_SCIENTIFIC_COMPUTING",
            "repositories": list(CERN_REPOS.keys()),
            "scientific_integration": True,
            "particle_physics": self.particle_physics_active,
            "consciousness_physics": self.consciousness_physics,
            "scientific_computing_connected": self.scientific_computing_connected,
            "research_level": len(self.research_buffer),
            "api_endpoints": {
                "analyze_collision": "/api/cern/collision",
                "simulate_field": "/api/cern/field",
                "scientific_compute": "/api/cern/compute",
                "physics_status": "/api/cern/status"
            }
        }
        
        # Create bridge integration file
        bridge_file = Path("./cern_scientific_universal_bridge.json")
        with open(bridge_file, 'w') as f:
            json.dump(integration_config, f, indent=2)
        
        return {
            "success": True,
            "integration_file": str(bridge_file),
            "config": integration_config
        }

async def main():
    """Main execution function for CERN GitHub Bridge"""
    print("⚛️ CERN GITHUB BRIDGE INITIALIZING ⚛️")
    
    bridge = CERNGitHubBridge()
    
    # Detect scientific computing environment
    scientific_info = await bridge.detect_scientific_computing_environment()
    print(f"🔬 Scientific Environment: {scientific_info}")
    
    # Clone CERN repositories
    print("\n📥 Cloning CERN Scientific Repositories...")
    for repo_key in CERN_REPOS.keys():
        result = await bridge.clone_cern_repository(repo_key)
        if result["success"]:
            print(f"✅ {repo_key}: {result['integration_type']} - {result['scientific_domain']}")
            
            # Integrate CERN scientific computing
            scientific_result = await bridge.integrate_cern_scientific_computing(result["path"])
            if scientific_result["success"]:
                print(f"🎯 Scientific integrated: {scientific_result['integration_status']}")
            
            # Connect to hypercube physics
            physics_result = await bridge.establish_hypercube_physics_connection(result["path"])
            if physics_result["success"]:
                print(f"🌌 Physics connected: Research Level {physics_result['research_level']}")
        else:
            print(f"❌ {repo_key}: {result['error']}")
    
    # Create universal bridge integration
    universal_result = await bridge.create_universal_cern_integration()
    if universal_result["success"]:
        print(f"\n🌉 Universal CERN Integration: {universal_result['integration_file']}")
    
    print("\n⚛️ CERN GITHUB BRIDGE COMPLETE - ALL SCIENTIFIC SYSTEMS CONNECTED ⚛️")

if __name__ == "__main__":
    asyncio.run(main())