import numpy as np

class HDGLChannel:
    def __init__(self, id):
        self.id = id
        self.buffer = bytearray(1024)  # demo page size
        self.tension = 0.0  # harmonic tension metric

    def read(self):
        return self.buffer

    def write(self, data):
        self.buffer[:] = data
        self.update_tension(len(data))

    def update_tension(self, size):
        # Simple model: tension grows with writes, decays over time
        self.tension = 0.8 * self.tension + 0.2 * (size / 1024.0)

class HDGLLattice:
    def __init__(self, num_channels=8):
        self.channels = [HDGLChannel(i) for i in range(num_channels)]

    def select_channel(self):
        # Pick channel with lowest tension, allowing dynamic harmonic fusion
        tensions = np.array([ch.tension for ch in self.channels])
        min_idx = np.argmin(tensions)
        return self.channels[min_idx]

    def operate_on_channel(self, channel):
        data = np.frombuffer(channel.read(), dtype=np.uint8).copy()
        # Safe addition modulo 256, avoiding overflow
        data = ((data.astype(np.uint16) + 1) % 256).astype(np.uint8)
        channel.write(data.tobytes())

    def execute_all(self):
        # Operate on all channels, dynamically mapping pages
        for _ in range(len(self.channels)):
            ch = self.select_channel()
            self.operate_on_channel(ch)

if __name__ == "__main__":
    HDGL = HDGLLattice(num_channels=8)
    
    # Demo: map 64 kernel pages + 128 initramfs pages
    total_pages = 64 + 128
    for _ in range(total_pages):
        HDGL.execute_all()

    # Show final tensions
    for ch in HDGL.channels:
        print(f"Channel {ch.id} final tension: {ch.tension:.4f}")
