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()