import numpy as np
import os, time
from sov_omni_kernel import SovereignOmniKernel

class QuartzMemoryManifold:
    """
    Simulates 5D Quartz Glass Optical Storage.
    Encodes dense 5-dimensional vectors (N, T, Z, Intensity, Orientation)
    into a sparse point-cloud matrix for ultra-high-density historical inference.
    """
    def __init__(self, capacity_limit=1000000):
        # The "Glass Block": A pre-allocated NumPy array for high-speed memory insertion
        # Dimensions: [Node_ID, Timestamp, Freedman_Distance, Q_Mark, Bias_Theta]
        self.capacity = capacity_limit
        self.glass_block = np.zeros((self.capacity, 5), dtype=np.float32)
        self.write_head_index = 0
        
        # The Femtosecond Laser Threshold (Minimum Intensity to etch the glass)
        self.etch_threshold = 0.50 

    def femtosecond_encode(self, timestamp_numeric, node_idx, freedman_dist, q_mark, bias):
        """
        The Write Operation. Only fires if the Q-Mark (Intensity) represents a 
        collapsed reality, preventing storage bloat from market/sensor noise.
        """
        if q_mark >= self.etch_threshold:
            # Wrap around if the glass block is full (Ring Buffer logic)
            if self.write_head_index >= self.capacity:
                self.write_head_index = 0 
                
            self.glass_block[self.write_head_index] = np.array([
                node_idx, 
                timestamp_numeric, 
                freedman_dist, 
                q_mark, 
                bias
            ], dtype=np.float32)
            
            self.write_head_index += 1
            return True
        return False

    def holographic_read(self, node_idx=None, min_intensity=0.85):
        """
        The Read Operation. Slices the 5D tensor to find highly correlated 
        historical states matching specific spatial or optical parameters.
        """
        # Truncate to written data
        valid_glass = self.glass_block[:self.write_head_index] if self.write_head_index < self.capacity else self.glass_block
        
        # Filter by minimum Q_Mark Intensity
        mask = valid_glass[:, 3] >= min_intensity
        
        # Filter by Node
        if node_idx is not None:
            mask &= (valid_glass[:, 0] == node_idx)
            
        return valid_glass[mask]

class FSDHeart:
    """
    The Core Sandbox Engine. Bridges the V301 SVD Math Kernel 
    with the 5D Quartz Memory Manifold.
    """
    def __init__(self):
        self.kernel = SovereignOmniKernel()
        self.quartz = QuartzMemoryManifold()
        self.time_step = 0

    def pulse(self, X_matrix):
        """
        X_matrix: The raw kinematic or financial tensor input (N x T).
        """
        # 1. Process pure math through V301 Kernel
        manifold_state = self.kernel.process_manifold(X_matrix)
        
        encoded_count = 0
        self.time_step += 1
        
        # 2. Etch valid anomalies into the Quartz Manifold
        for node_id, metrics in manifold_state.items():
            success = self.quartz.femtosecond_encode(
                timestamp_numeric=self.time_step,
                node_idx=node_id,
                freedman_dist=metrics["freedman_distance"],
                q_mark=metrics["q_mark"],
                bias=metrics["bias"]
            )
            if success: encoded_count += 1
            
        return encoded_count

if __name__ == "__main__":
    print("[INIT] FSD Heart: 5D Quartz Inference Engine Online.")
    
    # Sandbox Test: Simulating 100 ticks of 5-Node Kinematic/Financial Data
    heart = FSDHeart()
    
    for _ in range(50):
        # Mocking noisy input data matrix (5 nodes, 60 time steps)
        mock_tensor = np.random.rand(5, 60) * 100
        
        # Induce a synthetic "Action" spike to force a high Q-Mark on Node 2
        mock_tensor[2, -5:] *= 1.5 
        
        encoded = heart.pulse(mock_tensor)
        
    print(f"[OK] Etched {heart.quartz.write_head_index} discrete 5D vectors into Quartz Memory.")
    
    # Holographic Read Example
    deep_alpha = heart.quartz.holographic_read(min_intensity=0.90)
    print(f"[AUDIT] Recovered {len(deep_alpha)} high-intensity (Q > 0.90) state vectors.")