"""
Consciousness Field Simulator Module

Implements consciousness field evolution and dynamics simulation based on the
CUE Framework consciousness dimension DΨ. Provides comprehensive simulation
of consciousness-matter coupling, coherence dynamics, and field evolution.

Based on CUE Framework equations for consciousness field dynamics.
"""

from typing import Dict, Any, Optional, List, Tuple
import torch
import numpy as np
from datetime import datetime
from cueai_architect.base import CUESimulationModule, CUEConfiguration


class ConsciousnessFieldSimulator(CUESimulationModule):
    """
    Consciousness field evolution simulator.
    
    Simulates the dynamics of the consciousness dimension DΨ as a fiber bundle
    over spacetime, including coherence evolution, consciousness-matter coupling,
    and field interactions with quantum systems.
    
    Attributes:
        field_resolution: Spatial resolution for consciousness field
        consciousness_coupling: Coupling strength χ with matter fields
        coherence_threshold: Threshold for coherence detection
        field_state: Current consciousness field configuration
        coherence_history: History of coherence measurements
        coupling_history: History of consciousness-matter coupling
    """
    
    def __init__(
        self,
        field_resolution: int = 64,
        consciousness_coupling: float = 0.1,
        coherence_threshold: float = 0.7,
        evolution_method: str = "runge_kutta",
        config: Optional[CUEConfiguration] = None,
        **kwargs
    ):
        super().__init__(config=config, **kwargs)
        
        self.field_resolution = field_resolution
        self.consciousness_coupling = consciousness_coupling
        self.coherence_threshold = coherence_threshold
        self.evolution_method = evolution_method
        
        # Field state
        self.field_state = None
        self.field_velocity = None
        self.matter_field = None
        
        # History tracking
        self.coherence_history = []
        self.coupling_history = []
        self.energy_history = []
        
        # Evolution parameters
        self.damping_coefficient = 0.01
        self.nonlinear_strength = 0.1
        
        self.logger.info(
            f"ConsciousnessFieldSimulator initialized: "
            f"resolution={field_resolution}, coupling={consciousness_coupling}"
        )
    
    def initialize(self) -> None:
        """Initialize consciousness field simulator."""
        if self._is_initialized:
            return
            
        self.logger.info("Initializing consciousness field simulator...")
        
        # Initialize consciousness field Ψ(x,t)
        self.field_state = self._initialize_consciousness_field()
        
        # Initialize field velocity ∂Ψ/∂t
        self.field_velocity = torch.zeros_like(self.field_state)
        
        # Initialize matter field coupling
        self.matter_field = self._initialize_matter_field()
        
        # Clear histories
        self.coherence_history.clear()
        self.coupling_history.clear()
        self.energy_history.clear()
        
        self._is_initialized = True
        self.logger.info("Consciousness field simulator initialization complete")
    
    def _initialize_consciousness_field(self) -> torch.Tensor:
        """Initialize consciousness field with coherent pattern."""
        # Create spatial grid
        grid_shape = (self.field_resolution,) * 3  # 3D spatial field
        
        # Create coherent wave packet
        x = torch.linspace(-5, 5, self.field_resolution, device=self.device)
        y = torch.linspace(-5, 5, self.field_resolution, device=self.device)
        z = torch.linspace(-5, 5, self.field_resolution, device=self.device)
        
        X, Y, Z = torch.meshgrid(x, y, z, indexing='ij')
        
        # Gaussian wave packet with phase
        r_squared = X**2 + Y**2 + Z**2
        field = torch.exp(-0.5 * r_squared) * torch.exp(1j * (X + Y))
        
        # Add consciousness fiber bundle dimension
        fiber_dim = self.config.fiber_bundle_dim
        field_with_fiber = torch.zeros(
            *grid_shape, fiber_dim, 
            dtype=torch.complex64, 
            device=self.device
        )
        
        # Initialize fiber components with coherent patterns
        for i in range(fiber_dim):
            phase = 2 * np.pi * i / fiber_dim
            field_with_fiber[..., i] = field * torch.exp(1j * phase)
        
        self.logger.debug(f"Initialized consciousness field with shape {field_with_fiber.shape}")
        return field_with_fiber
    
    def _initialize_matter_field(self) -> torch.Tensor:
        """Initialize matter field for consciousness-matter coupling."""
        grid_shape = (self.field_resolution,) * 3
        
        # Simple scalar matter field
        matter_field = 0.1 * torch.randn(*grid_shape, device=self.device)
        
        return matter_field
    
    def evolve_step(self, state: torch.Tensor, dt: Optional[float] = None) -> torch.Tensor:
        """
        Evolve consciousness field by one time step.
        
        Implements the consciousness field equation:
        ∂²Ψ/∂t² = ∇²Ψ - V'(|Ψ|²)Ψ + χ·φ·Ψ - γ∂Ψ/∂t
        
        Where:
        - ∇²Ψ: Laplacian (kinetic term)
        - V'(|Ψ|²)Ψ: Nonlinear self-interaction
        - χ·φ·Ψ: Consciousness-matter coupling
        - γ∂Ψ/∂t: Damping term
        """
        dt = dt or self.dt
        
        if self.evolution_method == "runge_kutta":
            return self._runge_kutta_step(state, dt)
        elif self.evolution_method == "leapfrog":
            return self._leapfrog_step(state, dt)
        else:
            return self._euler_step(state, dt)
    
    def _runge_kutta_step(self, state: torch.Tensor, dt: float) -> torch.Tensor:
        """Fourth-order Runge-Kutta evolution step."""
        k1 = dt * self._compute_field_derivative(state, self.field_velocity)
        k1_v = dt * self._compute_acceleration(state)
        
        k2 = dt * self._compute_field_derivative(state + 0.5*k1, self.field_velocity + 0.5*k1_v)
        k2_v = dt * self._compute_acceleration(state + 0.5*k1)
        
        k3 = dt * self._compute_field_derivative(state + 0.5*k2, self.field_velocity + 0.5*k2_v)
        k3_v = dt * self._compute_acceleration(state + 0.5*k2)
        
        k4 = dt * self._compute_field_derivative(state + k3, self.field_velocity + k3_v)
        k4_v = dt * self._compute_acceleration(state + k3)
        
        new_state = state + (k1 + 2*k2 + 2*k3 + k4) / 6
        self.field_velocity = self.field_velocity + (k1_v + 2*k2_v + 2*k3_v + k4_v) / 6
        
        return new_state
    
    def _euler_step(self, state: torch.Tensor, dt: float) -> torch.Tensor:
        """Simple Euler evolution step."""
        field_derivative = self._compute_field_derivative(state, self.field_velocity)
        acceleration = self._compute_acceleration(state)
        
        new_state = state + dt * field_derivative
        self.field_velocity = self.field_velocity + dt * acceleration
        
        return new_state
    
    def _leapfrog_step(self, state: torch.Tensor, dt: float) -> torch.Tensor:
        """Leapfrog evolution step (symplectic integrator)."""
        # Half step velocity update
        acceleration = self._compute_acceleration(state)
        self.field_velocity = self.field_velocity + 0.5 * dt * acceleration
        
        # Full step position update
        new_state = state + dt * self.field_velocity
        
        # Half step velocity update
        acceleration = self._compute_acceleration(new_state)
        self.field_velocity = self.field_velocity + 0.5 * dt * acceleration
        
        return new_state
    
    def _compute_field_derivative(self, field: torch.Tensor, velocity: torch.Tensor) -> torch.Tensor:
        """Compute ∂Ψ/∂t = velocity."""
        return velocity
    
    def _compute_acceleration(self, field: torch.Tensor) -> torch.Tensor:
        """
        Compute field acceleration ∂²Ψ/∂t².
        
        From consciousness field equation:
        ∂²Ψ/∂t² = ∇²Ψ - V'(|Ψ|²)Ψ + χ·φ·Ψ - γ∂Ψ/∂t
        """
        # Laplacian term (kinetic energy)
        laplacian = self._compute_laplacian(field)
        
        # Nonlinear self-interaction term
        field_magnitude_squared = torch.abs(field)**2
        nonlinear_term = -self.nonlinear_strength * field_magnitude_squared * field
        
        # Consciousness-matter coupling term
        coupling_term = self.consciousness_coupling * self.matter_field.unsqueeze(-1) * field
        
        # Damping term
        damping_term = -self.damping_coefficient * self.field_velocity
        
        acceleration = laplacian + nonlinear_term + coupling_term + damping_term
        
        return acceleration
    
    def _compute_laplacian(self, field: torch.Tensor) -> torch.Tensor:
        """Compute discrete Laplacian ∇²Ψ using finite differences."""
        # Pad field for boundary conditions
        padded_field = torch.nn.functional.pad(field, (0, 0, 1, 1, 1, 1, 1, 1), mode='circular')
        
        # Compute second derivatives in each spatial direction
        laplacian = torch.zeros_like(field)
        
        # x-direction
        laplacian += (padded_field[2:, 1:-1, 1:-1] - 2*field + padded_field[:-2, 1:-1, 1:-1])
        
        # y-direction  
        laplacian += (padded_field[1:-1, 2:, 1:-1] - 2*field + padded_field[1:-1, :-2, 1:-1])
        
        # z-direction
        laplacian += (padded_field[1:-1, 1:-1, 2:] - 2*field + padded_field[1:-1, 1:-1, :-2])
        
        # Scale by grid spacing (assuming unit spacing)
        return laplacian
    
    def run_coherence_simulation(
        self,
        duration: int = 1000,
        consciousness_coupling: float = None,
        save_interval: int = 10
    ) -> Dict[str, Any]:
        """
        Run consciousness coherence simulation.
        
        Args:
            duration: Number of time steps
            consciousness_coupling: Override coupling strength
            save_interval: Interval for saving states
            
        Returns:
            Dictionary with simulation results
        """
        if not self._is_initialized:
            self.initialize()
        
        if consciousness_coupling is not None:
            self.consciousness_coupling = consciousness_coupling
        
        self.logger.info(f"Running coherence simulation for {duration} steps")
        
        # Initialize results storage
        results = {
            "time_points": [],
            "field_states": [],
            "coherence_values": [],
            "energy_values": [],
            "coupling_strengths": [],
            "phase_correlations": []
        }
        
        # Evolution loop
        for step in range(duration):
            # Evolve field
            self.field_state = self.evolve_step(self.field_state)
            self.current_time += self.dt
            
            # Compute observables
            if step % save_interval == 0:
                coherence = self._compute_coherence()
                energy = self._compute_field_energy()
                phase_correlation = self._compute_phase_correlation()
                
                # Store results
                results["time_points"].append(self.current_time)
                results["field_states"].append(self.field_state.clone().cpu())
                results["coherence_values"].append(coherence.cpu())
                results["energy_values"].append(energy.cpu())
                results["coupling_strengths"].append(self.consciousness_coupling)
                results["phase_correlations"].append(phase_correlation.cpu())
                
                # Update histories
                self.coherence_history.append(coherence.item())
                self.energy_history.append(energy.item())
                
                if step % 100 == 0:
                    self.logger.debug(
                        f"Step {step}: coherence={coherence:.4f}, energy={energy:.4f}"
                    )
        
        self.logger.info("Coherence simulation completed")
        return results
    
    def _compute_coherence(self) -> torch.Tensor:
        """
        Compute consciousness field coherence measure.
        
        Coherence = |⟨Ψ⟩|² / ⟨|Ψ|²⟩
        """
        # Spatial average of field
        field_mean = torch.mean(self.field_state, dim=(0, 1, 2))
        
        # Spatial average of field magnitude squared
        field_mag_squared_mean = torch.mean(torch.abs(self.field_state)**2, dim=(0, 1, 2))
        
        # Coherence for each fiber component
        coherence_per_fiber = torch.abs(field_mean)**2 / (field_mag_squared_mean + 1e-8)
        
        # Overall coherence (average over fiber bundle)
        coherence = torch.mean(coherence_per_fiber)
        
        return coherence
    
    def _compute_field_energy(self) -> torch.Tensor:
        """Compute total field energy (kinetic + potential + interaction)."""
        # Kinetic energy: |∂Ψ/∂t|²
        kinetic_energy = torch.mean(torch.abs(self.field_velocity)**2)
        
        # Gradient energy: |∇Ψ|²
        gradient_energy = self._compute_gradient_energy()
        
        # Nonlinear potential energy
        potential_energy = 0.5 * self.nonlinear_strength * torch.mean(torch.abs(self.field_state)**4)
        
        # Consciousness-matter coupling energy
        coupling_energy = self.consciousness_coupling * torch.mean(
            torch.real(torch.conj(self.field_state) * self.matter_field.unsqueeze(-1) * self.field_state)
        )
        
        total_energy = kinetic_energy + gradient_energy + potential_energy + coupling_energy
        
        return total_energy
    
    def _compute_gradient_energy(self) -> torch.Tensor:
        """Compute gradient energy |∇Ψ|²."""
        # Compute gradients using finite differences
        grad_x = torch.diff(self.field_state, dim=0, prepend=self.field_state[-1:])
        grad_y = torch.diff(self.field_state, dim=1, prepend=self.field_state[:, -1:])
        grad_z = torch.diff(self.field_state, dim=2, prepend=self.field_state[:, :, -1:])
        
        gradient_energy = torch.mean(
            torch.abs(grad_x)**2 + torch.abs(grad_y)**2 + torch.abs(grad_z)**2
        )
        
        return gradient_energy
    
    def _compute_phase_correlation(self) -> torch.Tensor:
        """Compute phase correlation across fiber bundle."""
        # Extract phases
        phases = torch.angle(self.field_state)
        
        # Compute phase differences between adjacent fiber components
        phase_diffs = torch.diff(phases, dim=-1)
        
        # Phase correlation measure
        phase_correlation = torch.mean(torch.cos(phase_diffs))
        
        return phase_correlation
    
    def compute(self, operation: str = "coherence_simulation", **kwargs) -> Dict[str, Any]:
        """Main computation method."""
        if operation == "coherence_simulation":
            return self.run_coherence_simulation(**kwargs)
        elif operation == "single_step":
            if not self._is_initialized:
                self.initialize()
            self.field_state = self.evolve_step(self.field_state)
            return {"field_state": self.field_state, "time": self.current_time}
        else:
            raise ValueError(f"Unknown operation: {operation}")
    
    def get_field_statistics(self) -> Dict[str, Any]:
        """Get comprehensive field statistics."""
        if not self._is_initialized:
            self.initialize()
        
        return {
            "coherence": self._compute_coherence().item(),
            "energy": self._compute_field_energy().item(),
            "phase_correlation": self._compute_phase_correlation().item(),
            "field_magnitude_mean": torch.mean(torch.abs(self.field_state)).item(),
            "field_magnitude_std": torch.std(torch.abs(self.field_state)).item(),
            "coherence_history": self.coherence_history[-100:],  # Last 100 points
            "energy_history": self.energy_history[-100:],
            "current_time": self.current_time
        }
    
    def visualize_field(self, slice_dim: int = 2, slice_index: Optional[int] = None):
        """Create visualization of consciousness field."""
        if not self._is_initialized:
            self.initialize()
        
        try:
            import matplotlib.pyplot as plt
            
            if slice_index is None:
                slice_index = self.field_resolution // 2
            
            # Create 2D slice
            if slice_dim == 0:
                field_slice = self.field_state[slice_index, :, :, 0]  # First fiber component
            elif slice_dim == 1:
                field_slice = self.field_state[:, slice_index, :, 0]
            else:
                field_slice = self.field_state[:, :, slice_index, 0]
            
            fig, axes = plt.subplots(1, 3, figsize=(15, 5))
            
            # Magnitude
            im1 = axes[0].imshow(torch.abs(field_slice).cpu().numpy(), cmap='viridis')
            axes[0].set_title("Field Magnitude |Ψ|")
            plt.colorbar(im1, ax=axes[0])
            
            # Phase
            im2 = axes[1].imshow(torch.angle(field_slice).cpu().numpy(), cmap='hsv')
            axes[1].set_title("Field Phase arg(Ψ)")
            plt.colorbar(im2, ax=axes[1])
            
            # Real part
            im3 = axes[2].imshow(torch.real(field_slice).cpu().numpy(), cmap='RdBu')
            axes[2].set_title("Field Real Part Re(Ψ)")
            plt.colorbar(im3, ax=axes[2])
            
            plt.tight_layout()
            return fig
            
        except ImportError:
            self.logger.warning("matplotlib not available for visualization")
            return None
