Upload wealthfortress2_0.py

wealthfortress2_0.py

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+# -*- coding: utf-8 -*-
+"""WealthFortress2.0
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1GH8ouvd4W8xGw_tGZ7VgSMxz0wVsfN7Z
+"""
+
+pip install torch cryptography numpy
+
+import torch
+import numpy as np
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+
+# Step 1: Generate the dense wave (sinusoidal waveform modulated by message data)
+def generate_dense_wave(message: str, frequency: float, sample_rate: int, duration: float):
+    t = torch.linspace(0, duration, int(sample_rate * duration))
+    # Convert message to numerical values (simple encoding)
+    message_bytes = [ord(c) for c in message]
+    message_tensor = torch.tensor(message_bytes, dtype=torch.float32)
+    # Create a carrier wave (sine wave)
+    carrier_wave = torch.sin(2 * np.pi * frequency * t)
+    # Modulate the carrier wave with the message tensor
+    modulated_wave = carrier_wave * torch.sin(2 * np.pi * message_tensor.mean() * t)
+    return modulated_wave
+
+# Step 2: Encrypt the message
+def encrypt_message(message: str, key: bytes):
+    backend = default_backend()
+    iv = b'\x00' * 16  # Initialization vector (in a real system, use a secure IV)
+    cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)
+    encryptor = cipher.encryptor()
+
+    # Pad the message to be AES block-size compliant
+    padder = padding.PKCS7(algorithms.AES.block_size).padder()
+    padded_data = padder.update(message.encode()) + padder.finalize()
+
+    encrypted_message = encryptor.update(padded_data) + encryptor.finalize()
+    return encrypted_message
+
+# Step 3: Modulate encrypted message into the waveform
+def modulate_wave_with_encryption(wave: torch.Tensor, encrypted_message: bytes):
+    # Convert encrypted message to tensor
+    encrypted_tensor = torch.tensor([byte for byte in encrypted_message], dtype=torch.float32)
+    # Normalize encrypted tensor and modulate it with the wave
+    modulated_wave = wave * encrypted_tensor.mean()
+    return modulated_wave
+
+# Step 4: Demodulate and decrypt
+def decrypt_message(encrypted_message: bytes, key: bytes):
+    backend = default_backend()
+    iv = b'\x00' * 16  # Same IV as in encryption
+    cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)
+    decryptor = cipher.decryptor()
+
+    decrypted_padded = decryptor.update(encrypted_message) + decryptor.finalize()
+
+    # Unpad the message
+    unpadder = padding.PKCS7(algorithms.AES.block_size).unpadder()
+    decrypted_message = unpadder.update(decrypted_padded) + unpadder.finalize()
+
+    return decrypted_message.decode()
+
+# Step 5: Transform into wealth data (dummy transformation for demo)
+def transform_to_wealth_data(decrypted_message: str):
+    # In a real-world application, this would involve parsing wealth-specific fields
+    wealth_data = {
+        "original_message": decrypted_message,
+        "net_worth": len(decrypted_message) * 1000,  # Dummy wealth computation
+        "assets": len(decrypted_message) * 500,
+    }
+    return wealth_data
+
+# Example usage
+if __name__ == "__main__":
+    # Initial settings
+    message = "Transfer 1000 units"
+    key = b'\x01' * 32  # AES-256 key
+    frequency = 5.0  # Frequency in Hz
+    sample_rate = 100  # Samples per second
+    duration = 1.0  # Wave duration in seconds
+
+    # Step 1: Create dense wave
+    wave = generate_dense_wave(message, frequency, sample_rate, duration)
+
+    # Step 2: Encrypt the message
+    encrypted_message = encrypt_message(message, key)
+
+    # Step 3: Modulate the wave with encrypted message
+    modulated_wave = modulate_wave_with_encryption(wave, encrypted_message)
+
+    # Step 4: Decrypt the message (for demonstration)
+    decrypted_message = decrypt_message(encrypted_message, key)
+
+    # Step 5: Transform to wealth data
+    wealth_data = transform_to_wealth_data(decrypted_message)
+
+    print("Wealth Data:", wealth_data)
+
+pip install matplotlib
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+
+# Step 1: Generate the dense wave (sinusoidal waveform modulated by message data)
+def generate_dense_wave(message: str, frequency: float, sample_rate: int, duration: float):
+    t = torch.linspace(0, duration, int(sample_rate * duration))
+    # Convert message to numerical values (simple encoding)
+    message_bytes = [ord(c) for c in message]
+    message_tensor = torch.tensor(message_bytes, dtype=torch.float32)
+    # Create a carrier wave (sine wave)
+    carrier_wave = torch.sin(2 * np.pi * frequency * t)
+    # Modulate the carrier wave with the message tensor
+    modulated_wave = carrier_wave * torch.sin(2 * np.pi * message_tensor.mean() * t)
+    return t, carrier_wave, modulated_wave
+
+# Step 2: Encrypt the message
+def encrypt_message(message: str, key: bytes):
+    backend = default_backend()
+    iv = b'\x00' * 16  # Initialization vector (in a real system, use a secure IV)
+    cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)
+    encryptor = cipher.encryptor()
+
+    # Pad the message to be AES block-size compliant
+    padder = padding.PKCS7(algorithms.AES.block_size).padder()
+    padded_data = padder.update(message.encode()) + padder.finalize()
+
+    encrypted_message = encryptor.update(padded_data) + encryptor.finalize()
+    return encrypted_message
+
+# Step 3: Modulate encrypted message into the waveform
+def modulate_wave_with_encryption(wave: torch.Tensor, encrypted_message: bytes):
+    # Convert encrypted message to tensor
+    encrypted_tensor = torch.tensor([byte for byte in encrypted_message], dtype=torch.float32)
+    # Normalize encrypted tensor and modulate it with the wave
+    modulated_wave = wave * encrypted_tensor.mean()
+    return modulated_wave
+
+# Step 4: Visualization using Matplotlib
+def visualize_modulation(t, carrier_wave, modulated_wave):
+    plt.figure(figsize=(12, 6))
+
+    # Plot the original carrier wave
+    plt.subplot(2, 1, 1)
+    plt.plot(t.numpy(), carrier_wave.numpy(), label="Carrier Wave", color="blue")
+    plt.title("Carrier Wave")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot the modulated wave
+    plt.subplot(2, 1, 2)
+    plt.plot(t.numpy(), modulated_wave.numpy(), label="Modulated Wave", color="orange")
+    plt.title("Modulated Wave (Encrypted Message)")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Show plots
+    plt.tight_layout()
+    plt.show()
+
+# Example usage
+if __name__ == "__main__":
+    # Initial settings
+    message = "Transfer 1000 units"
+    key = b'\x01' * 32  # AES-256 key
+    frequency = 5.0  # Frequency in Hz
+    sample_rate = 100  # Samples per second
+    duration = 1.0  # Wave duration in seconds
+
+    # Step 1: Create dense wave
+    t, carrier_wave, modulated_wave = generate_dense_wave(message, frequency, sample_rate, duration)
+
+    # Step 2: Encrypt the message
+    encrypted_message = encrypt_message(message, key)
+
+    # Step 3: Modulate the wave with encrypted message
+    modulated_wave_with_encryption = modulate_wave_with_encryption(modulated_wave, encrypted_message)
+
+    # Step 4: Visualize the modulation
+    visualize_modulation(t, carrier_wave, modulated_wave_with_encryption)
+
+import torch
+import numpy as np
+import time
+import base64
+import matplotlib.pyplot as plt
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+
+# Step 1: Generate the dense wave (sinusoidal waveform modulated by message data)
+def generate_dense_wave(message: str, frequency: float, sample_rate: int, duration: float):
+    t = torch.linspace(0, duration, int(sample_rate * duration))
+    # Convert message to numerical values (simple encoding)
+    message_bytes = [ord(c) for c in message]
+    message_tensor = torch.tensor(message_bytes, dtype=torch.float32)
+    # Create a carrier wave (sine wave)
+    carrier_wave = torch.sin(2 * np.pi * frequency * t)
+    # Modulate the carrier wave with the message tensor
+    modulated_wave = carrier_wave * torch.sin(2 * np.pi * message_tensor.mean() * t)
+    return t, carrier_wave, modulated_wave
+
+# Step 2: Encrypt the message (VPN layer encryption)
+def encrypt_message(message: str, key: bytes):
+    backend = default_backend()
+    iv = b'\x00' * 16  # Initialization vector (in a real system, use a secure IV)
+    cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)
+    encryptor = cipher.encryptor()
+
+    # Pad the message to be AES block-size compliant
+    padder = padding.PKCS7(algorithms.AES.block_size).padder()
+    padded_data = padder.update(message.encode()) + padder.finalize()
+
+    encrypted_message = encryptor.update(padded_data) + encryptor.finalize()
+    return encrypted_message
+
+# Step 3: Simulate VPN layer transmission with encryption
+def vpn_layer_transmission(data: bytes):
+    # Simulate the "VPN" by encrypting the message
+    print("[VPN] Transmitting data securely...")
+    time.sleep(1)  # Simulating network delay
+    encoded_data = base64.b64encode(data)
+    print(f"[VPN] Encrypted and transmitted data: {encoded_data.decode('utf-8')}")
+    return encoded_data
+
+# Step 4: Simulate cloud storage transfer and deep space transmission
+def cloud_transfer(encoded_data: bytes):
+    print("[Cloud] Storing data in cloud for deep space transmission...")
+    time.sleep(2)  # Simulating storage delay
+    print(f"[Cloud] Data successfully stored: {encoded_data.decode('utf-8')}")
+
+# Step 5: Visualization using Matplotlib
+def visualize_modulation(t, carrier_wave, modulated_wave):
+    plt.figure(figsize=(12, 6))
+
+    # Plot the original carrier wave
+    plt.subplot(2, 1, 1)
+    plt.plot(t.numpy(), carrier_wave.numpy(), label="Carrier Wave", color="blue")
+    plt.title("Carrier Wave")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot the modulated wave
+    plt.subplot(2, 1, 2)
+    plt.plot(t.numpy(), modulated_wave.numpy(), label="Modulated Wave", color="orange")
+    plt.title("Modulated Wave (Encrypted Message)")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Show plots
+    plt.tight_layout()
+    plt.show()
+
+# Example usage
+if __name__ == "__main__":
+    # Initial settings
+    message = "Transfer 1000 units"
+    key = b'\x01' * 32  # AES-256 key
+    frequency = 5.0  # Frequency in Hz
+    sample_rate = 100  # Samples per second
+    duration = 1.0  # Wave duration in seconds
+
+    # Step 1: Create dense wave
+    t, carrier_wave, modulated_wave = generate_dense_wave(message, frequency, sample_rate, duration)
+
+    # Step 2: Encrypt the message
+    encrypted_message = encrypt_message(message, key)
+
+    # Step 3: VPN Layer Transmission (simulate VPN secure transmission)
+    vpn_encrypted_message = vpn_layer_transmission(encrypted_message)
+
+    # Step 4: Cloud transfer and simulated "deep space" transmission
+    cloud_transfer(vpn_encrypted_message)
+
+    # Step 5: Visualize the wave modulation
+    visualize_modulation(t, carrier_wave, modulated_wave)
+
+import torch
+import numpy as np
+import time
+import base64
+import matplotlib.pyplot as plt
+from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
+from cryptography.hazmat.backends import default_backend
+from cryptography.hazmat.primitives import padding
+
+# Step 1: Generate the dense wave (sinusoidal waveform modulated by message data)
+def generate_dense_wave(message: str, frequency: float, sample_rate: int, duration: float):
+    t = torch.linspace(0, duration, int(sample_rate * duration))
+    # Convert message to numerical values (simple encoding)
+    message_bytes = [ord(c) for c in message]
+    message_tensor = torch.tensor(message_bytes, dtype=torch.float32)
+    # Create a carrier wave (sine wave)
+    carrier_wave = torch.sin(2 * np.pi * frequency * t)
+    # Modulate the carrier wave with the message tensor
+    modulated_wave = carrier_wave * torch.sin(2 * np.pi * message_tensor.mean() * t)
+    return t, carrier_wave, modulated_wave
+
+# Step 2: Combine the waves (carrier wave + modulated wave)
+def combine_waves(carrier_wave: torch.Tensor, modulated_wave: torch.Tensor):
+    combined_wave = carrier_wave + modulated_wave  # Simple addition
+    return combined_wave
+
+# Step 3: Encrypt the message (VPN layer encryption)
+def encrypt_message(message: str, key: bytes):
+    backend = default_backend()
+    iv = b'\x00' * 16  # Initialization vector (in a real system, use a secure IV)
+    cipher = Cipher(algorithms.AES(key), modes.CBC(iv), backend=backend)
+    encryptor = cipher.encryptor()
+
+    # Pad the message to be AES block-size compliant
+    padder = padding.PKCS7(algorithms.AES.block_size).padder()
+    padded_data = padder.update(message.encode()) + padder.finalize()
+
+    encrypted_message = encryptor.update(padded_data) + encryptor.finalize()
+    return encrypted_message
+
+# Step 4: Simulate VPN layer transmission with encryption
+def vpn_layer_transmission(data: bytes):
+    # Simulate the "VPN" by encrypting the message
+    print("[VPN] Transmitting data securely...")
+    time.sleep(1)  # Simulating network delay
+    encoded_data = base64.b64encode(data)
+    print(f"[VPN] Encrypted and transmitted data: {encoded_data.decode('utf-8')}")
+    return encoded_data
+
+# Step 5: Simulate cloud storage transfer and deep space transmission
+def cloud_transfer(encoded_data: bytes):
+    print("[Cloud] Storing data in cloud for deep space transmission...")
+    time.sleep(2)  # Simulating storage delay
+    print(f"[Cloud] Data successfully stored: {encoded_data.decode('utf-8')}")
+
+# Step 6: Visualization using Matplotlib
+def visualize_modulation(t, carrier_wave, modulated_wave, combined_wave):
+    plt.figure(figsize=(12, 8))
+
+    # Plot the original carrier wave
+    plt.subplot(3, 1, 1)
+    plt.plot(t.numpy(), carrier_wave.numpy(), label="Carrier Wave", color="blue")
+    plt.title("Carrier Wave")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot the modulated wave
+    plt.subplot(3, 1, 2)
+    plt.plot(t.numpy(), modulated_wave.numpy(), label="Modulated Wave", color="orange")
+    plt.title("Modulated Wave (Encrypted Message)")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot the combined wave
+    plt.subplot(3, 1, 3)
+    plt.plot(t.numpy(), combined_wave.numpy(), label="Combined Wave", color="green")
+    plt.title("Combined Wave (Carrier + Modulated)")
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Show plots
+    plt.tight_layout()
+    plt.show()
+
+# Example usage
+if __name__ == "__main__":
+    # Initial settings
+    message = "Transfer 1000 units"
+    key = b'\x01' * 32  # AES-256 key
+    frequency = 5.0  # Frequency in Hz
+    sample_rate = 100  # Samples per second
+    duration = 1.0  # Wave duration in seconds
+
+    # Step 1: Create dense wave
+    t, carrier_wave, modulated_wave = generate_dense_wave(message, frequency, sample_rate, duration)
+
+    # Step 2: Combine the carrier and modulated waves
+    combined_wave = combine_waves(carrier_wave, modulated_wave)
+
+    # Step 3: Encrypt the message
+    encrypted_message = encrypt_message(message, key)
+
+    # Step 4: VPN Layer Transmission (simulate VPN secure transmission)
+    vpn_encrypted_message = vpn_layer_transmission(encrypted_message)
+
+    # Step 5: Cloud transfer and simulated "deep space" transmission
+    cloud_transfer(vpn_encrypted_message)
+
+    # Step 6: Visualize the wave modulation and combined wave
+    visualize_modulation(t, carrier_wave, modulated_wave, combined_wave)
+
+import numpy as np
+
+# Hamming(7, 4) code for simple error detection and correction
+def hamming_encode(message: str):
+    message_bits = [int(b) for b in ''.join(format(ord(c), '08b') for c in message)]
+    encoded_bits = []
+
+    # Apply Hamming(7,4) encoding
+    for i in range(0, len(message_bits), 4):
+        d = message_bits[i:i+4]
+        if len(d) < 4:  # Pad if necessary
+            d += [0] * (4 - len(d))
+
+        p1 = d[0] ^ d[1] ^ d[3]  # Parity bits
+        p2 = d[0] ^ d[2] ^ d[3]
+        p3 = d[1] ^ d[2] ^ d[3]
+
+        # Add data and parity bits
+        encoded_bits += [p1, p2, d[0], p3, d[1], d[2], d[3]]
+
+    return np.array(encoded_bits)
+
+def hamming_decode(encoded_bits):
+    decoded_message = []
+
+    # Decode Hamming(7,4)
+    for i in range(0, len(encoded_bits), 7):
+        b = encoded_bits[i:i+7]
+        if len(b) < 7:  # Skip if not enough bits
+            continue
+
+        # Calculate syndrome bits
+        p1 = b[0] ^ b[2] ^ b[4] ^ b[6]
+        p2 = b[1] ^ b[2] ^ b[5] ^ b[6]
+        p3 = b[3] ^ b[4] ^ b[5] ^ b[6]
+
+        # Error position (if any)
+        error_position = p1 + (p2 * 2) + (p3 * 4)
+
+        if error_position != 0:
+            b[error_position - 1] = 1 - b[error_position - 1]  # Correct the bit
+
+        # Extract the original data bits
+        decoded_message += [b[2], b[4], b[5], b[6]]
+
+    return ''.join([chr(int(''.join(map(str, decoded_message[i:i+8])), 2)) for i in range(0, len(decoded_message), 8)])
+
+# Example usage
+message = "Test"
+encoded_message = hamming_encode(message)
+print(f"Encoded Message (Hamming): {encoded_message}")
+
+# Simulate transmission and potential bit-flips (errors)
+encoded_message[2] = 1 - encoded_message[2]  # Introduce an error
+
+# Decode and correct errors
+decoded_message = hamming_decode(encoded_message)
+print(f"Decoded Message: {decoded_message}")
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Step 1: Generate the dense wave (sinusoidal waveform modulated by message data)
+def generate_dense_wave(message: str, frequency: float, sample_rate: int, duration: float):
+    t = torch.linspace(0, duration, int(sample_rate * duration))
+    message_bytes = [ord(c) for c in message]
+    message_tensor = torch.tensor(message_bytes, dtype=torch.float32)
+    carrier_wave = torch.sin(2 * np.pi * frequency * t)
+    modulated_wave = carrier_wave * torch.sin(2 * np.pi * message_tensor.mean() * t)
+    return t, carrier_wave, modulated_wave
+
+# Step 2: Space-Time Coding (Alamouti Scheme with 2 antennas)
+def space_time_code(wave1: torch.Tensor, wave2: torch.Tensor):
+    # Alamouti Space-Time Block Code for 2 antennas
+    s1 = wave1
+    s2 = wave2
+    transmit_antenna_1 = torch.stack([s1, -s2.conj()])
+    transmit_antenna_2 = torch.stack([s2, s1.conj()])
+    return transmit_antenna_1, transmit_antenna_2
+
+# Step 3: Doppler Compensation
+def doppler_compensation(wave: torch.Tensor, velocity: float, frequency: float, sample_rate: int):
+    c = 3e8  # Speed of light in meters per second
+    doppler_shift = frequency * (velocity / c)  # Doppler shift formula
+    compensated_wave = wave * torch.exp(-1j * 2 * np.pi * doppler_shift * torch.arange(len(wave)) / sample_rate)
+    return compensated_wave.real  # Take real part after compensation
+
+# Step 4: Combine Waves (Carrier + Modulated)
+def combine_waves(carrier_wave: torch.Tensor, modulated_wave: torch.Tensor):
+    combined_wave = carrier_wave + modulated_wave
+    return combined_wave
+
+# Step 5: Visualization using Matplotlib
+def visualize_modulation(t, wave1, wave2, combined_wave, title1, title2, combined_title):
+    plt.figure(figsize=(12, 8))
+
+    # Plot Wave 1
+    plt.subplot(3, 1, 1)
+    plt.plot(t.numpy(), wave1.numpy(), label=title1, color="blue")
+    plt.title(title1)
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot Wave 2
+    plt.subplot(3, 1, 2)
+    plt.plot(t.numpy(), wave2.numpy(), label=title2, color="orange")
+    plt.title(title2)
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    # Plot Combined Wave
+    plt.subplot(3, 1, 3)
+    plt.plot(t.numpy(), combined_wave.numpy(), label=combined_title, color="green")
+    plt.title(combined_title)
+    plt.xlabel("Time (s)")
+    plt.ylabel("Amplitude")
+    plt.grid(True)
+
+    plt.tight_layout()
+    plt.show()
+
+# Example usage
+if __name__ == "__main__":
+    # Initial settings
+    message = "Deep Space Message"
+    frequency = 5.0  # Frequency in Hz
+    sample_rate = 100  # Samples per second
+    duration = 1.0  # Wave duration in seconds
+    velocity = 10000  # Relative velocity (m/s) for Doppler compensation
+
+    # Step 1: Generate dense wave
+    t, carrier_wave, modulated_wave = generate_dense_wave(message, frequency, sample_rate, duration)
+
+    # Step 2: Space-Time Coding (using two antennas)
+    st_wave1, st_wave2 = space_time_code(carrier_wave, modulated_wave)
+
+    # Step 3: Doppler Compensation
+    doppler_wave1 = doppler_compensation(st_wave1[0], velocity, frequency, sample_rate)
+    doppler_wave2 = doppler_compensation(st_wave2[0], velocity, frequency, sample_rate)
+
+    # Step 4: Combine the waves (carrier + modulated)
+    combined_wave = combine_waves(doppler_wave1, doppler_wave2)
+
+    # Step 5: Visualization
+    visualize_modulation(t, doppler_wave1, doppler_wave2, combined_wave,
+                         "Doppler-Compensated Wave 1", "Doppler-Compensated Wave 2",
+                         "Combined Doppler-Compensated Wave")