antigravity_bridge.py

"""
================================================================================
ANTIGRAVITY INTEGRATION BRIDGE
================================================================================
Real-time data receiver from Antigravity 24h system.
Processes: NASA data + Wikipedia + Deep Search + Pyramid calculations
Output: Integrated constants for SIMULE3 + Levh-i Mahfuz
================================================================================
"""

import json
from datetime import datetime
from levhi_mahfuz import (
    LevhiMahfuzConstants,
    LevhiMahfuzFormulas,
    LevhiMahfuzPatterns
)


class AntigravityDataBridge:
    """
    Processes real-time data from Antigravity system.
    Validates against known constants.
    Detects new patterns.
    """
    
    def __init__(self):
        self.data_received = []
        self.patterns_found = []
        self.anomalies = []
        self.timestamp = datetime.now()
    
    def receive_data(self, data_dict):
        """
        Accept incoming data from Antigravity system.
        Format: {"source": "...", "value": ..., "unit": "...", "confidence": 0.0-1.0}
        """
        self.data_received.append({
            "timestamp": datetime.now().isoformat(),
            "data": data_dict
        })
        return self.process_entry(data_dict)
    
    def process_entry(self, entry):
        """Process individual data entry."""
        value = entry.get("value")
        source = entry.get("source", "unknown")
        unit = entry.get("unit", "")
        confidence = entry.get("confidence", 0.0)
        
        # Check against known constants
        match_found = self._match_against_constants(value)
        
        # Extract 11-patterns
        if isinstance(value, (int, float)):
            if LevhiMahfuzPatterns.check_divisibility_by_11(int(value)):
                self.patterns_found.append({
                    "value": value,
                    "type": "11-divisible",
                    "source": source,
                    "confidence": confidence
                })
        
        return {
            "processed": True,
            "value": value,
            "source": source,
            "unit": unit,
            "confidence": confidence,
            "match": match_found,
            "timestamp": datetime.now().isoformat()
        }
    
    def _match_against_constants(self, value):
        """Find matches in Levh-i Mahfuz constants."""
        constants = {
            "IDEAL_EARTH_RADIUS": LevhiMahfuzConstants.IDEAL_EARTH_RADIUS,
            "REAL_EARTH_RADIUS": LevhiMahfuzConstants.REAL_EARTH_RADIUS,
            "IDEAL_MOON_PERIGEE": LevhiMahfuzConstants.IDEAL_MOON_PERIGEE,
            "REAL_MOON_PERIGEE": LevhiMahfuzConstants.REAL_MOON_PERIGEE,
            "YEAR_IDEAL_11T": LevhiMahfuzConstants.YEAR_IDEAL_11T,
            "YEAR_REAL_10T": LevhiMahfuzConstants.YEAR_REAL_10T,
            "HALLEY_PERIOD": LevhiMahfuzConstants.HALLEY_PERIOD_IDEAL,
            "SPEED_LIGHT_REAL": LevhiMahfuzConstants.SPEED_LIGHT_REAL,
            "GIZA_LATITUDE": LevhiMahfuzConstants.GIZA_LATITUDE,
        }
        
        matches = []
        for name, const_val in constants.items():
            # Check exact match
            if value == const_val:
                matches.append({"type": "exact", "constant": name})
            # Check close approximation (within 1%)
            elif isinstance(value, (int, float)) and isinstance(const_val, (int, float)):
                diff = abs(value - const_val)
                percent_diff = (diff / const_val) * 100 if const_val != 0 else 0
                if percent_diff < 1.0 and percent_diff > 0:
                    matches.append({
                        "type": "approximate",
                        "constant": name,
                        "percent_diff": percent_diff
                    })
        
        return matches
    
    def calculate_new_patterns(self):
        """Extract novel patterns from accumulated data."""
        print("\n" + "="*80)
        print("ANTIGRAVITY SYSTEM: NEW PATTERN EXTRACTION")
        print("="*80)
        
        values = [entry["data"].get("value") for entry in self.data_received]
        values = [v for v in values if isinstance(v, (int, float))]
        
        if not values:
            print("No numeric data to process.")
            return {}
        
        # Extract 11-multiples
        eleven_patterns = LevhiMahfuzPatterns.extract_eleven_patterns(values)
        print(f"\n11-Divisible Patterns Found: {len(eleven_patterns)}")
        for p in eleven_patterns[:5]:
            print(f"  → {p}")
        
        # Look for resonance codes
        resonances = {}
        for val in values:
            for code_name, code_val in LevhiMahfuzPatterns.RESONANCE_CODES.items():
                if int(val) == code_val:
                    resonances[code_name] = val
        
        print(f"\nResonance Codes Matched: {len(resonances)}")
        for code, val in resonances.items():
            print(f"  → {code}: {val}")
        
        # Calculate derived constants
        print(f"\nDerived Constants:")
        
        # Example: combine two patterns
        if len(values) >= 2:
            combo1 = values[0] * 1.046338  # OP_LEN
            combo2 = values[0] / 1.046338
            print(f"  → {values[0]} × OP_LEN = {combo1:.2f}")
            print(f"  → {values[0]} ÷ OP_LEN = {combo2:.2f}")
        
        return {
            "eleven_patterns": eleven_patterns,
            "resonances": resonances,
            "total_entries": len(self.data_received)
        }
    
    def generate_report(self):
        """Generate comprehensive analysis report."""
        report = {
            "timestamp": datetime.now().isoformat(),
            "data_entries_processed": len(self.data_received),
            "patterns_found": len(self.patterns_found),
            "anomalies": len(self.anomalies),
            "integration_status": "ACTIVE" if LEVHI_MAHFUZ_LOADED else "PARTIAL",
        }
        
        # Summary of patterns
        pattern_summary = {}
        for p in self.patterns_found:
            p_type = p.get("type", "unknown")
            pattern_summary[p_type] = pattern_summary.get(p_type, 0) + 1
        
        report["pattern_summary"] = pattern_summary
        
        return report
    
    def export_as_json(self, filename="antigravity_data.json"):
        """Export processed data as JSON."""
        export = {
            "metadata": {
                "generated": datetime.now().isoformat(),
                "system": "Antigravity Integration Bridge",
            },
            "data_received": self.data_received,
            "patterns_found": self.patterns_found,
            "anomalies": self.anomalies,
        }
        
        with open(filename, 'w') as f:
            json.dump(export, f, indent=2)
        
        print(f"Data exported to {filename}")
        return export


# Global flag for Levh-i Mahfuz availability
try:
    LEVHI_MAHFUZ_LOADED = True
except:
    LEVHI_MAHFUZ_LOADED = False


# ========== EXAMPLE USAGE ==========
def example_workflow():
    """Demonstrate data integration workflow."""
    bridge = AntigravityDataBridge()
    
    # Simulate incoming Antigravity data
    sample_data = [
        {
            "source": "NASA_APOD",
            "value": 363228,
            "unit": "km",
            "confidence": 0.99,
            "description": "Moon Perigee (latest measurement)"
        },
        {
            "source": "Wikipedia",
            "value": 33,
            "unit": "number",
            "confidence": 0.95,
            "description": "Human vertebrae count"
        },
        {
            "source": "DeepSearch_Pyramid",
            "value": 6721.3278,
            "unit": "meters",
            "confidence": 0.87,
            "description": "Pyramid synthesis value (1.0083 × 6666)"
        },
        {
            "source": "DeepSearch_Pyramid",
            "value": 6699.0,
            "unit": "meters",
            "confidence": 0.85,
            "description": "Pyramid synthesis value (33 + 6666)"
        },
    ]
    
    print("\n" + "="*80)
    print("ANTIGRAVITY INTEGRATION WORKFLOW")
    print("="*80)
    
    for data in sample_data:
        result = bridge.receive_data(data)
        print(f"\n→ Processed: {data['source']} = {data['value']} {data['unit']}")
        print(f"  Match: {result['match']}")
    
    # Calculate new patterns
    bridge.calculate_new_patterns()
    
    # Generate report
    report = bridge.generate_report()
    print(f"\n--- INTEGRATION REPORT ---")
    print(json.dumps(report, indent=2))
    
    # Export data
    bridge.export_as_json()


if __name__ == "__main__":
    example_workflow()