Upload fortunepulseypt.py

fortunepulseypt.py

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+# -*- coding: utf-8 -*-
+"""FortunePulseYPT
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1EJOL_aJRKx2BtYg_0EEl60U3VNHFy7aF
+"""
+
+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)