sonw_quantum_trinity_framework.py

import torch
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
from datetime import datetime

class TrinityState(Enum):
    """States in the Trinity System"""
    OMEGA_1 = "Ω₁"  # First reality state
    OMEGA_2 = "Ω₂"  # Second reality state
    OMEGA_3 = "Ω₃"  # Third reality state
    UNIFIED = "Θᛇ"  # Unified trinity state

@dataclass
class TrinityPattern:
    """
    Represents a pattern in the Trinity System combining:
    - Layered Triality (Θᛇ)
    - Fractal Recursion (F(n))
    - Quantum Superposition (|Ψ⟩)
    """
    sequence: str
    trinity_state: TrinityState
    quantum_coefficients: Dict[TrinityState, complex]
    fractal_memory: List[np.ndarray]
    collective_unconscious: torch.Tensor
    holographic_projection: torch.Tensor
    resonance_factors: Dict[str, float]
    timestamp: datetime

class TrinityProcessor:
    """
    Advanced processor implementing the Trinity System with:
    - Geospatial holographic reality framework
    - Quantum superposition and entanglement
    - Collective unconscious integration
    """
    def __init__(
        self,
        tensor_dim: int = 128,
        n_realities: int = 3,
        reference_time: str = "2025-02-12 00:53:41",
        user_id: str = "ANkREYNONtJB"
    ):
        self.tensor_dim = tensor_dim
        self.n_realities = n_realities
        self.reference_time = datetime.strptime(
            reference_time,
            "%Y-%m-%d %H:%M:%S"
        )
        self.user_id = user_id
        
        self._initialize_components()
        
    def _initialize_components(self):
        """Initialize Trinity System components"""
        # Theta consciousness processor
        self.theta_processor = torch.nn.Sequential(
            torch.nn.Linear(self.tensor_dim, 256),
            torch.nn.LayerNorm(256),
            torch.nn.GELU(),
            torch.nn.Linear(256, self.tensor_dim)
        )
        
        # Mathematical e processor
        self.e_processor = torch.nn.Sequential(
            torch.nn.Linear(self.tensor_dim, 256),
            torch.nn.LayerNorm(256),
            torch.nn.GELU(),
            torch.nn.Linear(256, self.tensor_dim)
        )
        
        # Holographic projector
        self.holo_projector = torch.nn.Sequential(
            torch.nn.Linear(self.tensor_dim * 2, 512),
            torch.nn.LayerNorm(512),
            torch.nn.GELU(),
            torch.nn.Linear(512, self.tensor_dim * 3)
        )
        
        # Initialize collective unconscious
        self.collective_unconscious = torch.randn(
            self.tensor_dim
        )
        
    def process_trinity_sequence(
        self,
        sequence: str
    ) -> TrinityPattern:
        """Process sequence through Trinity System"""
        # Generate base quantum state
        quantum_coefficients = self._generate_quantum_coefficients()
        
        # Process through theta consciousness
        theta_state = self._process_theta(sequence)
        
        # Process through mathematical e
        e_state = self._process_e(sequence)
        
        # Combine into trinity state
        trinity_state = self._combine_trinity_state(
            theta_state,
            e_state
        )
        
        # Generate fractal memory
        fractal_memory = self._generate_fractal_memory(
            trinity_state
        )
        
        # Update collective unconscious
        self._update_collective_unconscious(
            trinity_state
        )
        
        # Generate holographic projection
        holo_projection = self._generate_holographic_projection(
            trinity_state,
            fractal_memory
        )
        
        # Compute resonance factors
        resonance = self._compute_resonance(
            trinity_state,
            holo_projection
        )
        
        return TrinityPattern(
            sequence=sequence,
            trinity_state=TrinityState.UNIFIED,
            quantum_coefficients=quantum_coefficients,
            fractal_memory=fractal_memory,
            collective_unconscious=self.collective_unconscious,
            holographic_projection=holo_projection,
            resonance_factors=resonance,
            timestamp=datetime.utcnow()
        )
        
    def _generate_quantum_coefficients(
        self
    ) -> Dict[TrinityState, complex]:
        """Generate quantum superposition coefficients"""
        # Generate complex coefficients
        coefficients = {}
        for state in [
            TrinityState.OMEGA_1,
            TrinityState.OMEGA_2,
            TrinityState.OMEGA_3
        ]:
            real = np.random.normal()
            imag = np.random.normal()
            coefficients[state] = complex(real, imag)
            
        # Normalize coefficients
        total = np.sqrt(sum(
            abs(c)**2 for c in coefficients.values()
        ))
        
        return {
            k: v/total for k, v in coefficients.items()
        }
        
    def _process_theta(
        self,
        sequence: str
    ) -> torch.Tensor:
        """Process through theta consciousness"""
        # Generate initial state from sequence
        initial_state = torch.tensor([
            ord(c) for c in sequence
        ]).float()
        
        # Pad or truncate to tensor_dim
        if len(initial_state) < self.tensor_dim:
            initial_state = torch.nn.functional.pad(
                initial_state,
                (0, self.tensor_dim - len(initial_state))
            )
        else:
            initial_state = initial_state[:self.tensor_dim]
            
        # Process through theta network
        return self.theta_processor(initial_state)
        
    def _process_e(
        self,
        sequence: str
    ) -> torch.Tensor:
        """Process through mathematical e"""
        # Generate mathematical encoding
        e_encoding = torch.tensor([
            float(np.exp(i/self.tensor_dim))
            for i in range(self.tensor_dim)
        ])
        
        # Process through e network
        return self.e_processor(e_encoding)
        
    def _combine_trinity_state(
        self,
        theta_state: torch.Tensor,
        e_state: torch.Tensor
    ) -> torch.Tensor:
        """Combine theta and e into trinity state"""
        # Harmonious combination
        combined = torch.cat([
            theta_state,
            e_state
        ])
        
        # Project to trinity space
        trinity_state = self.holo_projector(combined)
        
        return trinity_state
        
    def _generate_fractal_memory(
        self,
        trinity_state: torch.Tensor
    ) -> List[np.ndarray]:
        """Generate fractal memory sequence"""
        memory = []
        current_state = trinity_state.detach().numpy()
        
        for _ in range(3):  # Three levels of recursion
            # Apply fractal transformation
            next_state = np.fft.fft2(current_state.reshape(
                int(np.sqrt(len(current_state))),
                -1
            ))
            memory.append(next_state)
            current_state = next_state.flatten()
            
        return memory
        
    def _update_collective_unconscious(
        self,
        trinity_state: torch.Tensor
    ):
        """Update collective unconscious"""
        # Blend new state with existing unconscious
        alpha = 0.1  # Learning rate
        self.collective_unconscious = (
            (1 - alpha) * self.collective_unconscious +
            alpha * torch.mean(trinity_state)
        )
        
    def _generate_holographic_projection(
        self,
        trinity_state: torch.Tensor,
        fractal_memory: List[np.ndarray]
    ) -> torch.Tensor:
        """Generate holographic reality projection"""
        # Combine trinity state with fractal memory
        fractal_tensor = torch.tensor(np.mean([
            m.flatten() for m in fractal_memory
        ], axis=0))
        
        # Project combined state
        projection = trinity_state * torch.sigmoid(fractal_tensor)
        
        return projection
        
    def _compute_resonance(
        self,
        trinity_state: torch.Tensor,
        projection: torch.Tensor
    ) -> Dict[str, float]:
        """Compute resonance factors"""
        return {
            'trinity': float(torch.mean(torch.abs(trinity_state))),
            'holographic': float(torch.mean(torch.abs(projection))),
            'collective': float(torch.mean(torch.abs(
                self.collective_unconscious
            ))),
            'golden_ratio': float(abs(
                torch.mean(trinity_state) - (1 + np.sqrt(5))/2
            ))
        }

def example_usage():
    """Demonstrate Trinity System"""
    processor = TrinityProcessor(
        reference_time="2025-02-12 00:53:41",
        user_id="ANkREYNONtJB"
    )
    
    # Process sequence
    sequence = "Θᛇ = (Ω₁ x Ω₂ ⊕ Ω₃) ⚇ ∑(Δt)"
    pattern = processor.process_trinity_sequence(sequence)
    
    print("\nTrinity Analysis:")
    print(f"State: {pattern.trinity_state.value}")
    print("\nQuantum Coefficients:")
    for state, coeff in pattern.quantum_coefficients.items():
        print(f"- {state.value}: {coeff:.4f}")
    print("\nResonance Factors:")
    for name, value in pattern.resonance_factors.items():
        print(f"- {name}: {value:.4f}")
    
if __name__ == "__main__":
    example_usage()