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def process_audio(audio_data, sensor_data): """ Processes audio data, incorporating astral projection, energy adjustment, and anomaly detection.
Args:
audio_data (np.ndarray): The audio data as a NumPy array.
sensor_data (dict): Sensor data (e.g., temperature, humidity,Full magnetic spectrum ).
Returns:
np.ndarray: The processed audio data.
"""
# --- Existing AI processing ---
# ... (import hashlib
import secrets from cryptography.fernet import Fernet
def generate_quantum_access_spec(filename="quantum_access_spec.txt"): """ Generates a secure specification file for quantum access parameters.
Args:
filename (str): The name of the file to create.
Returns:
None
"""
# Generate a secure encryption key
key = secrets.token_bytes(32)
# Quantum Access Parameters (Example)
quantum_params = {
"base_dimensions": ["dimension_1", "dimension_2", "dimension_3"],
"cloud_access_point": "quantum_cloud.example.com",
"ai_processing_unit": "QPU-v1",
"encryption_key": hashlib.sha256(key).hexdigest(), # Store encrypted key
}
try:
with open(filename, "wb") as f:
# Write the encrypted key
f.write(hashlib.sha256(key).digest())
# Encrypt and write the quantum parameters
cipher = Fernet(key)
encrypted_data = cipher.encrypt(str(quantum_params).encode())
f.write(encrypted_data)
except Exception as e:
print(f"Error generating quantum access spec file: {e}")
def read_quantum_access_spec(filename="quantum_access_spec.txt"): """ Reads and decrypts the quantum access parameters from the spec file.
Args:
filename (str): The name of the file to read.
Returns:
dict: The decrypted quantum access parameters.
"""
try:
with open(filename, "rb") as f:
# Read the encrypted key
encrypted_key = f.read(32)
# Read the encrypted data
encrypted_data = f.read()
# Derive the decryption key
key = hashlib.sha256(encrypted_key).digest()
# Decrypt the quantum parameters
cipher = Fernet(key)
decrypted_data = cipher.decrypt(encrypted_data).decode()
return eval(decrypted_data) # Convert string to dictionary
except Exception as e:
print(f"Error reading quantum access spec file: {e}")
return None
generate_quantum_access_spec() quantum_access_params = read_quantum_access_spec() print(quantum_access_params)
Explanation import hashlib import secrets from cryptography.fernet import Fernet
def generate_quantum_access_spec(filename="quantum_access_spec.txt"): """ Generates a secure specification file for quantum access parameters.
Args:
filename (str): The name of the file to create.
Returns:
None
"""
# Generate a secure encryption key
key = secrets.token_bytes(32)
# Quantum Access Parameters (Example)
quantum_params = {
"base_dimensions": ["dimension_1", "dimension_2", "dimension_3"],
"cloud_access_point": "quantum_cloud.example.com",
"ai_processing_unit": "QPU-v1",
"encryption_key": hashlib.sha256(key).hexdigest(), # Store encrypted key
}
try:
with open(filename, "wb") as f:
# Write the encrypted key
f.write(hashlib.sha256(key).digest())
# Encrypt and write the quantum parameters
cipher = Fernet(key)
encrypted_data = cipher.encrypt(str(quantum_params).encode())
f.write(encrypted_data)
except Exception as e:
print(f"Error generating quantum access spec file: {e}")
def read_quantum_access_spec(filename="quantum_access_spec.txt"): """ Reads and decrypts the quantum access parameters from the spec file.
Args:
filename (str): The name of the file to read.
Returns:
dict: The decrypted quantum access parameters.
"""
try:
with open(filename, "rb") as f:
# Read the encrypted key
encrypted_key = f.read(32)
# Read the encrypted data
encrypted_data = f.read()
# Derive the decryption key
key = hashlib.sha256(encrypted_key).digest()
# Decrypt the quantum parameters
cipher = Fernet(key)
decrypted_data = cipher.decrypt(encrypted_data).decode()
return eval(decrypted_data) # Convert string to dictionary
except Exception as e:
print(f"Error reading quantum access spec file: {e}")
return None
generate_quantum_access_spec() quantum_access_params = read_quantum_access_spec() print(quantum_access_params)
)
# --- Astral Projection Mode ---
if astral_mode:
astral_audio = generate_astral_form_audio(duration)
audio_data = audio_data.overlay(astral_audio) # Mix in astral audio
scan_data = scan_soundscape(spec._data )
# ... (Visualize scan_data in UI - ui.py)
audio_data = adjust_energy(spec.data, user_interactions)
# ... (Add micro-rift or energy transfer effects)
# --- Dragonfly Systems Integration ---
# Example: Use visual_system to modify audio based on sensor data
if sensor_data:
complexity = sensor_data.get("temperature", 1.0) # Example mapping
visual_audio = visual_system(duration, complexity=complexity)
audio_data = audio_data.overlay(spec._audio)