extract_data.py

import torch
import numpy as np
import pandas as pd
import os

def extract_real_12d_manifold_states(sequence_length=64):
    """
    Bridges the live feedback loop with the V7 enterprise training process.
    Prioritizes empirical telemetry pools generated by the industrial interface,
    falling back onto deterministic phase-space coordinate paths if a live stream
    is not actively writing to disk.
    """
    csv_path = "quantum_training_feedback_pool.csv"
    target_npy_path = "quantum_training_feedback_pool.npy"
    
    if os.path.exists(csv_path):
        print(f"🔄 Real-time industrial feedback pool detected! Processing: {csv_path}")
        df = pd.read_csv(csv_path)
        
        # Extract numerical data columns representing the 12D hardware trajectories
        numerical_data = df.select_dtypes(include=[np.number]).values
        
        if numerical_data.shape[1] < 12:
            print(f"⚠️ Warning: Found only {numerical_data.shape[1]} dimensions. Padding up to 12D.")
            padding = np.zeros((numerical_data.shape[0], 12 - numerical_data.shape[1]))
            numerical_data = np.hstack((numerical_data, padding))
            
        compiled_matrix = numerical_data[:, :12]
        print(f"📈 Successfully extracted {len(compiled_matrix)} real feedback states.")
        
    else:
        print("ℹ️ No live feedback CSV found yet. Generating baseline 12D geometric trajectories for initial matrix alignment...")
        # Fallback to structural trajectory coordinates matching the underlying continuous system
        num_samples = 50000
        t = np.linspace(0, 50, num_samples + sequence_length + 1)
        
        d1 = np.sin(t) / (np.sqrt(2) + np.cos(t))
        d2 = np.cos(t) / (np.sqrt(2) + np.sin(t))
        d3 = np.tanh(t * 0.1)
        d4, d5, d6 = np.gradient(d1), np.gradient(d2), np.gradient(d3)
        d7 = np.sin(t * 1.5) * np.exp(-t * 0.01)
        d8 = np.cos(t * 1.5) * np.exp(-t * 0.01)
        d9 = np.sin(t * 0.5)
        d10, d11, d12 = d4 * d7, d5 * d8, d6 * d9
        
        compiled_matrix = np.stack([d1, d2, d3, d4, d5, d6, d7, d8, d9, d10, d11, d12], axis=1)
    
    # Save the numpy matrix array down to disk so train_v5.py can ingest it instantly
    print(f"💾 Saving compiled 12D telemetry matrix to: {target_npy_path} [Shape: {compiled_matrix.shape}]")
    np.save(target_npy_path, compiled_matrix)
    return compiled_matrix

if __name__ == "__main__":
    # Initialize the arrays inside the active workspace
    extract_real_12d_manifold_states()