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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()
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