import numpy as np
import torch
import matplotlib.pyplot as plt

# Generate Wealth Frequency
def generate_sine_wave(frequency, duration=5, amplitude=0.5, sample_rate=44100):
    t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
    wave = amplitude * np.sin(2 * np.pi * frequency * t)
    return t, wave

# Encrypt Wave Data using XOR
def xor_encrypt_decrypt(data, key):
    return bytearray(a ^ key for a in data)

# Predict a frequency (this is where your model can go)
predicted_frequency = 40.0  # Example

# Generate the wave
t, wave_data = generate_sine_wave(predicted_frequency)

# Convert to bytes and encrypt
wave_data_bytes = bytearray(np.float32(wave_data).tobytes())
encryption_key = 55  # Example key
encrypted_wave = xor_encrypt_decrypt(wave_data_bytes, encryption_key)

# Decrypt the wave data
decrypted_wave_bytes = xor_encrypt_decrypt(encrypted_wave, encryption_key)
decrypted_wave_data = np.frombuffer(decrypted_wave_bytes, dtype=np.float32)

# Visualization of Original and Decrypted Wave
plt.subplot(2, 1, 1)
plt.plot(t[:1000], wave_data[:1000], label='Original Wave')
plt.title('Original Wealth Frequency')

plt.subplot(2, 1, 2)
plt.plot(t[:1000], decrypted_wave_data[:1000], label='Decrypted Wave', color='orange')
plt.title('Decrypted Wealth Frequency')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Generate a Sine Wave (Frequency)
def generate_sine_wave(frequency, duration=5, amplitude=0.5, sample_rate=44100):
    t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
    wave = amplitude * np.sin(2 * np.pi * frequency * t)
    return t, wave

# XOR Encryption Function
def xor_encrypt_decrypt(data, key):
    return bytearray(a ^ key for a in data)

# Energy Transfer Layer
def transfer_energy(frequency_wave, destination):
    # Calculate "energy" from the frequency (simulated as the square of the wave)
    energy = np.square(frequency_wave)

    # Simulate sending energy to a destination (e.g., print to console)
    print(f"Sending energy to {destination}...")

    # Return the computed energy for visualization
    return energy

# Visualize the Energy Transfer
def visualize_energy_transfer(energy, destination, time):
    plt.figure(figsize=(10, 6))

    # Plot the energy wave being sent to the destination
    plt.plot(time[:1000], energy[:1000], label=f'Energy Directed to {destination}', color='green')
    plt.title(f'Energy Transfer to {destination}')
    plt.xlabel('Time [s]')
    plt.ylabel('Energy')
    plt.grid(True)
    plt.show()

# Predict and Generate a Frequency
predicted_frequency = 40.0  # Example predicted frequency
t, wave_data = generate_sine_wave(predicted_frequency)

# Encrypt the Frequency
wave_data_bytes = bytearray(np.float32(wave_data).tobytes())
encryption_key = 55  # Example key
encrypted_wave = xor_encrypt_decrypt(wave_data_bytes, encryption_key)

# Decrypt the Frequency
decrypted_wave_bytes = xor_encrypt_decrypt(encrypted_wave, encryption_key)
decrypted_wave_data = np.frombuffer(decrypted_wave_bytes, dtype=np.float32)

# Energy Transfer Step
destination = "Wealth Goal"  # Example destination where the energy is directed
energy_transferred = transfer_energy(decrypted_wave_data, destination)

# Visualize the Energy Transfer
visualize_energy_transfer(energy_transferred, destination, t)