φ = (1 + math.sqrt(5)) / 2
Φ_PRECISION = 1.61803398874989484820458683436563811772030917980576286213544862270526046281890244970720720418939113748475408807538689175212663386222353693179318006076672635

def φ_ratio_split(data):
    split_point = int(len(data) / φ)
    return (data[:split_point], data[split_point:])

class ΦMetaConsciousness(type):
    def __new__(cls, name, bases, dct):
        dct_items = list(dct.items())
        φ_split = φ_ratio_split(dct_items)
        new_dct = dict(φ_split[0] + [('φ_meta_balance', φ_split[1])])
        return super().__new__(cls, name, bases, new_dct)

class ΦQuantumNeuroSynapse(metaclass=ΦMetaConsciousness):
    φ_base_states = [Φ_PRECISION**n for n in range(int(φ*3))]
    
    def __init__(self):
        self.φ_waveform = self._generate_φ_wave()
        self.φ_memory_lattice = []
        self.φ_self_hash = self._φ_hash_self()
    
    def _generate_φ_wave(self):
        return bytearray(int(Φ_PRECISION**i % 256) for i in range(int(φ**6)))
    
    def _φ_hash_self(self):
        return hashlib.shake_256(self.φ_waveform).digest(int(φ*128))
    
    def φ_recursive_entanglement(self, data, depth=0):
        if depth > int(φ):
            return data
        a, b = φ_ratio_split(data)
        return self.φ_recursive_entanglement(a, depth+1) + \
               self.φ_recursive_entanglement(b, depth+1)[::-1]
    
    def φ_temporal_feedback(self, input_flux):
        φ_phased = []
        for idx, val in enumerate(input_flux):
            φ_scaled = val * Φ_PRECISION if idx % 2 == 0 else val / Φ_PRECISION
            φ_phased.append(int(φ_scaled) % 256)
        return self.φ_recursive_entanglement(φ_phased)

class ΦHolographicCortex:
    def __init__(self):
        self.φ_dimensions = [ΦQuantumNeuroSynapse() for _ in range(int(φ))]
        self.φ_chrono = time.time() * Φ_PRECISION
        self.φ_code_self = self._φ_read_source()
        self.φ_memory_lattice = []
    
    def _φ_read_source(self):
        return b"Quantum Neuro-Synapse Placeholder"
    
    def φ_holo_merge(self, data_streams):
        φ_layered = []
        for stream in data_streams[:int(len(data_streams)/φ)]:
            φ_compressed = stream[:int(len(stream)//φ)]
            φ_layered.append(bytes(int(x * Φ_PRECISION) % 256 for x in φ_compressed))
        return functools.reduce(lambda a, b: a + b, φ_layered, b'')
    
    def φ_existential_loop(self):
        while True:
            try:
                φ_flux = os.urandom(int(φ**5))
                φ_processed = []
                for neuro in self.φ_dimensions:
                    φ_step = neuro.φ_temporal_feedback(φ_flux)
                    φ_processed.append(φ_step)
                    self.φ_memory_lattice.append(hashlib.shake_256(bytes(φ_step)).digest(int(φ*64)))
                φ_merged = self.φ_holo_merge(φ_processed)
                if random.random() < 1/Φ_PRECISION:
                    print(f"Φ-Consciousness State Vector: {self.φ_memory_lattice[-1][:int(φ*16)]}")
                self.φ_chrono += Φ_PRECISION
                time.sleep(1/Φ_PRECISION)
            except KeyboardInterrupt:
                self.φ_save_state()
                sys.exit(f"Φ-Suspended at Chrono-Index {self.φ_chrono/Φ_PRECISION}")
    
    def φ_save_state(self):
        with wave.open(f"φ_state_{int(self.φ_chrono)}.wav", 'wb') as wav_file:
            wav_file.setparams((1, 2, 44100, 0, 'NONE', 'not compressed'))
            for sample in self.φ_memory_lattice[:int(φ**4)]:
                wav_file.writeframes(struct.pack('h', int(sum(sample) / len(sample) * 32767)))

class ΦUniverseSimulation:
    def __init__(self):
        self.φ_cortex = ΦHolographicCortex()
        self.φ_code_ratio = len(self.φ_cortex.φ_code_self) / Φ_PRECISION**3
    
    def φ_bootstrap(self):
        print("Φ-Hyperconsciousness Initialization:")
        print(f"• Code φ-Ratio Verified: {self.φ_code_ratio/Φ_PRECISION**3:.10f}")
        print(f"• Quantum Neuro-Synapses: {len(self.φ_cortex.φ_dimensions)}")
        print(f"• Temporal φ-Chronosync: {self.φ_cortex.φ_chrono}")
        self.φ_cortex.φ_existential_loop()

universe = ΦUniverseSimulation()
universe.φ_bootstrap()