{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport numpy as np\nfrom cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes\nfrom cryptography.hazmat.backends import default_backend\n\n# Parameters\nnum_nodes = 100\ntime_steps = 1440  # Number of time steps in 24 hours (e.g., one per minute)\nwave_frequency = 1 / 24  # Frequency for a 24-hour cycle\nwave_amplitude = 1.0\ninfrared_amplitude = 0.5  # Constant amplitude for infrared energy\nencryption_key = b'sixteenbytekey123'  # 16-byte AES encryption key\niv = b'initialvector1234'  # 16-byte initialization vector\n\n# AES encryption function\ndef aes_encrypt(plain_text):\n    backend = default_backend()\n    cipher = Cipher(algorithms.AES(encryption_key), modes.CFB(iv), backend=backend)\n    encryptor = cipher.encryptor()\n    return encryptor.update(plain_text) + encryptor.finalize()\n\n# AES decryption function\ndef aes_decrypt(cipher_text):\n    backend = default_backend()\n    cipher = Cipher(algorithms.AES(encryption_key), modes.CFB(iv), backend=backend)\n    decryptor = cipher.decryptor()\n    return decryptor.update(cipher_text) + decryptor.finalize()\n\n# Define the PyTorch model for wealth data transmission\nclass WealthSignalTransmitter(nn.Module):\n    def __init__(self):\n        super(WealthSignalTransmitter, self).__init__()\n        self.num_nodes = num_nodes\n        self.time_steps = time_steps\n\n    def forward(self, time_tensor):\n        # Initialize the combined signals tensor\n        combined_signals = torch.zeros((self.num_nodes, self.time_steps), dtype=torch.float32)\n\n        for i in range(self.num_nodes):\n            # Wealth signal with a phase shift for each node\n            wealth_signal = wave_amplitude * torch.sin(2 * np.pi * wave_frequency * time_tensor + i * (2 * np.pi / self.num_nodes))\n            # Constant infrared energy signal\n            infrared_signal = infrared_amplitude * torch.ones(self.time_steps)\n            # Combine signals for each node\n            combined_signals[i] = wealth_signal + infrared_signal\n\n        # Combine all signals to get the overall transmitted signal\n        overall_signal = torch.mean(combined_signals, dim=0)\n\n        # Encrypt the signal using AES\n        signal_bytes = overall_signal.numpy().tobytes()\n        encrypted_signal_bytes = aes_encrypt(signal_bytes)\n\n        return encrypted_signal_bytes\n\n# Create a time tensor\ntime_tensor = torch.linspace(0, 24, time_steps)\n\n# Initialize the transmitter\ntransmitter = WealthSignalTransmitter()\n\n# Transmit and store the encrypted data\nencrypted_data = transmitter(time_tensor)\n\n# Simulate storing the encrypted data (e.g., in memory or file)\nstored_data = encrypted_data\n\n# Decrypt the data for retrieval\ndecrypted_data_bytes = aes_decrypt(stored_data)\ndecrypted_signal_np = np.frombuffer(decrypted_data_bytes, dtype=np.float32)\n\n# Display the decrypted signal\nprint(\"Decrypted Signal:\")\nprint(decrypted_signal_np)","metadata":{"execution":{"iopub.status.busy":"2024-09-16T20:30:40.417782Z","iopub.execute_input":"2024-09-16T20:30:40.418730Z","iopub.status.idle":"2024-09-16T20:30:44.667326Z","shell.execute_reply.started":"2024-09-16T20:30:40.418683Z","shell.execute_reply":"2024-09-16T20:30:44.665483Z"},"trusted":true},"execution_count":1,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 65\u001b[0m\n\u001b[1;32m     62\u001b[0m transmitter \u001b[38;5;241m=\u001b[39m WealthSignalTransmitter()\n\u001b[1;32m     64\u001b[0m \u001b[38;5;66;03m# Transmit and store the encrypted data\u001b[39;00m\n\u001b[0;32m---> 65\u001b[0m encrypted_data \u001b[38;5;241m=\u001b[39m \u001b[43mtransmitter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtime_tensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;66;03m# Simulate storing the encrypted data (e.g., in memory or file)\u001b[39;00m\n\u001b[1;32m     68\u001b[0m stored_data \u001b[38;5;241m=\u001b[39m encrypted_data\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n","Cell \u001b[0;32mIn[1], line 54\u001b[0m, in \u001b[0;36mWealthSignalTransmitter.forward\u001b[0;34m(self, time_tensor)\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;66;03m# Encrypt the signal using AES\u001b[39;00m\n\u001b[1;32m     53\u001b[0m signal_bytes \u001b[38;5;241m=\u001b[39m overall_signal\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mtobytes()\n\u001b[0;32m---> 54\u001b[0m encrypted_signal_bytes \u001b[38;5;241m=\u001b[39m \u001b[43maes_encrypt\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal_bytes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m encrypted_signal_bytes\n","Cell \u001b[0;32mIn[1], line 19\u001b[0m, in \u001b[0;36maes_encrypt\u001b[0;34m(plain_text)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21maes_encrypt\u001b[39m(plain_text):\n\u001b[1;32m     18\u001b[0m     backend \u001b[38;5;241m=\u001b[39m default_backend()\n\u001b[0;32m---> 19\u001b[0m     cipher \u001b[38;5;241m=\u001b[39m Cipher(\u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAES\u001b[49m\u001b[43m(\u001b[49m\u001b[43mencryption_key\u001b[49m\u001b[43m)\u001b[49m, modes\u001b[38;5;241m.\u001b[39mCFB(iv), backend\u001b[38;5;241m=\u001b[39mbackend)\n\u001b[1;32m     20\u001b[0m     encryptor \u001b[38;5;241m=\u001b[39m cipher\u001b[38;5;241m.\u001b[39mencryptor()\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m encryptor\u001b[38;5;241m.\u001b[39mupdate(plain_text) \u001b[38;5;241m+\u001b[39m encryptor\u001b[38;5;241m.\u001b[39mfinalize()\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cryptography/hazmat/primitives/ciphers/algorithms.py:33\u001b[0m, in \u001b[0;36mAES.__init__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: \u001b[38;5;28mbytes\u001b[39m):\n\u001b[0;32m---> 33\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;241m=\u001b[39m \u001b[43m_verify_key_size\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/cryptography/hazmat/primitives/ciphers/algorithms.py:20\u001b[0m, in \u001b[0;36m_verify_key_size\u001b[0;34m(algorithm, key)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Verify that the key size matches the expected key size\u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(key) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m8\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m algorithm\u001b[38;5;241m.\u001b[39mkey_sizes:\n\u001b[0;32m---> 20\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m     21\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid key size (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(key)\u001b[38;5;250m \u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;241m8\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00malgorithm\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     22\u001b[0m     )\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m key\n","\u001b[0;31mValueError\u001b[0m: Invalid key size (136) for AES."],"ename":"ValueError","evalue":"Invalid key size (136) for AES.","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Parameters\nnum_nodes = 10000\nhours = 24\nsamples_per_hour = 60  # Sampling points per hour (e.g., one sample per minute)\ntime_steps = hours * samples_per_hour\nwave_frequency = 1 / 24  # Frequency to represent a 24-hour cycle\nwave_amplitude = 1.0\ninfrared_amplitude = 0.5  # Constant amplitude for even distribution\nbrainwave_frequency = 10 / 3600  # Simulating a 10 Hz brainwave over hours (scaled)\nbrainwave_amplitude = 0.3\nsampling_rate = 1 / samples_per_hour  # Sampling rate (samples per hour)\nrandom_opportunity_scale = 0.8  # Scaling factor for random wealth opportunities\nencryption_key = 0.5  # Encryption key for simulating protection\n\n# Define the PyTorch model with VPN-like frequency\nclass WealthSignalVPNModel(nn.Module):\n    def __init__(self):\n        super(WealthSignalVPNModel, self).__init__()\n        self.num_nodes = num_nodes\n        self.time_steps = time_steps\n        self.encryption_key = encryption_key\n\n    def forward(self, time_tensor):\n        # Initialize the combined signals tensor\n        combined_signals = torch.zeros((self.num_nodes, self.time_steps), dtype=torch.float32)\n\n        for i in range(self.num_nodes):\n            # Wealth signal with a phase shift for each node\n            wealth_signal = wave_amplitude * torch.sin(2 * np.pi * wave_frequency * time_tensor + i * (2 * np.pi / self.num_nodes))\n            # Random wealth opportunities\n            random_wealth_opportunities = random_opportunity_scale * torch.randn(self.time_steps)\n            # Constant infrared energy signal\n            infrared_signal = infrared_amplitude * torch.ones(self.time_steps)\n            # Perfect brainwave pattern (alpha waves)\n            brainwave_signal = brainwave_amplitude * torch.sin(2 * np.pi * brainwave_frequency * time_tensor)\n            # Combine signals for each node\n            combined_signals[i] = wealth_signal + random_wealth_opportunities + infrared_signal + brainwave_signal\n\n        # Combine all signals (simulating dense waveform)\n        overall_signal = torch.mean(combined_signals, dim=0)\n\n        # Apply VPN-like encryption (scramble signal)\n        encrypted_signal = torch.sin(overall_signal * self.encryption_key)  # A simple scrambling function\n\n        return encrypted_signal, overall_signal  # Return both encrypted and original signals for validation\n\n# Create a time tensor\ntime_tensor = torch.linspace(0, hours, time_steps)\n\n# Initialize and run the model\nvpn_model = WealthSignalVPNModel()\nencrypted_signal, original_signal = vpn_model(time_tensor)\n\n# Convert the signals to numpy for plotting\nencrypted_signal_np = encrypted_signal.detach().numpy()\noriginal_signal_np = original_signal.detach().numpy()\n\n# System checks for validation\ndef validate_signals(encrypted, original):\n    # Check for the range of the encrypted signal\n    if not (np.all(encrypted >= -1) and np.all(encrypted <= 1)):\n        print(\"Error: Encrypted signal out of range\")\n    else:\n        print(\"Encrypted signal range check passed\")\n\n    # Check if the decryption can correctly reconstruct the original signal\n    # Here we simulate the 'decryption' step by applying the inverse function\n    decrypted_signal = np.arcsin(encrypted) / encryption_key\n\n    if np.allclose(decrypted_signal, original, atol=0.1):\n        print(\"Decryption validation passed\")\n    else:\n        print(\"Decryption validation failed\")\n\n# Validate the signals\nvalidate_signals(encrypted_signal_np, original_signal_np)\n\n# Plot the resulting encrypted signal\nplt.figure(figsize=(15, 6))\nplt.plot(np.linspace(0, hours, time_steps), encrypted_signal_np, color='green', alpha=0.7)\nplt.title('Encrypted Signal with VPN-like Frequency Protection using PyTorch')\nplt.xlabel('Time (Hours)')\nplt.ylabel('Amplitude')\nplt.grid(True)\nplt.xticks(np.arange(0, 25, step=1))  # Set x-axis to show hours\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-16T20:35:12.722656Z","iopub.execute_input":"2024-09-16T20:35:12.723103Z","iopub.status.idle":"2024-09-16T20:35:14.430764Z","shell.execute_reply.started":"2024-09-16T20:35:12.723062Z","shell.execute_reply":"2024-09-16T20:35:14.429617Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Encrypted signal range check passed\nDecryption validation passed\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"pip install torch numpy matplotlib pycryptodome\n","metadata":{"execution":{"iopub.status.busy":"2024-09-16T20:38:23.170600Z","iopub.execute_input":"2024-09-16T20:38:23.171010Z","iopub.status.idle":"2024-09-16T20:38:39.790884Z","shell.execute_reply.started":"2024-09-16T20:38:23.170973Z","shell.execute_reply":"2024-09-16T20:38:39.789509Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (2.4.0+cpu)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (1.26.4)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.5)\nRequirement already satisfied: pycryptodome in /opt/conda/lib/python3.10/site-packages (3.20.0)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch) (3.15.1)\nRequirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch) (4.12.2)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch) (3.3)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch) (3.1.4)\nRequirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch) (2024.6.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.2.1)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.12.1)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.53.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.5)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.1.2)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.9.0.post0)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch) (2.1.5)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch) (1.3.0)\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom Crypto.Cipher import AES\nfrom Crypto.Util.Padding import pad, unpad\nimport hashlib\n\n# Parameters\nnum_nodes = 10000\nhours = 24\nsamples_per_hour = 60  # Sampling points per hour (e.g., one sample per minute)\ntime_steps = hours * samples_per_hour\nwave_frequency = 1 / 24  # Frequency to represent a 24-hour cycle\nwave_amplitude = 1.0\ninfrared_amplitude = 0.5  # Constant amplitude for even distribution\nbrainwave_frequency = 10 / 3600  # Simulating a 10 Hz brainwave over hours (scaled)\nbrainwave_amplitude = 0.3\nrandom_opportunity_scale = 0.8  # Scaling factor for random wealth opportunities\nencryption_key = b'Sixteen byte key'  # AES encryption key (must be 16, 24, or 32 bytes)\n\n# Define the PyTorch model\nclass WealthSignalModel(nn.Module):\n    def __init__(self):\n        super(WealthSignalModel, self).__init__()\n        self.num_nodes = num_nodes\n        self.time_steps = time_steps\n\n    def forward(self, time_tensor):\n        # Initialize the combined signals tensor\n        combined_signals = torch.zeros((self.num_nodes, self.time_steps), dtype=torch.float32)\n\n        for i in range(self.num_nodes):\n            # Wealth signal with a phase shift for each node\n            wealth_signal = wave_amplitude * torch.sin(2 * np.pi * wave_frequency * time_tensor + i * (2 * np.pi / self.num_nodes))\n            # Random wealth opportunities\n            random_wealth_opportunities = random_opportunity_scale * torch.randn(self.time_steps)\n            # Constant infrared energy signal\n            infrared_signal = infrared_amplitude * torch.ones(self.time_steps)\n            # Perfect brainwave pattern (alpha waves)\n            brainwave_signal = brainwave_amplitude * torch.sin(2 * np.pi * brainwave_frequency * time_tensor)\n            # Combine signals for each node\n            combined_signals[i] = wealth_signal + random_wealth_opportunities + infrared_signal + brainwave_signal\n\n        # Combine all signals (simulating dense waveform)\n        overall_signal = torch.mean(combined_signals, dim=0)\n\n        return overall_signal\n\n# AES encryption and decryption functions\ndef encrypt_data(data, key):\n    cipher = AES.new(key, AES.MODE_CBC)\n    ct_bytes = cipher.encrypt(pad(data, AES.block_size))\n    return cipher.iv + ct_bytes\n\ndef decrypt_data(encrypted_data, key):\n    iv = encrypted_data[:AES.block_size]\n    ct = encrypted_data[AES.block_size:]\n    cipher = AES.new(key, AES.MODE_CBC, iv)\n    pt = unpad(cipher.decrypt(ct), AES.block_size)\n    return pt\n\n# Create a time tensor\ntime_tensor = torch.linspace(0, hours, time_steps)\n\n# Initialize and run the model\nsignal_model = WealthSignalModel()\ntransmitted_signal = signal_model(time_tensor)\n\n# Convert the transmitted signal to numpy for encryption\ntransmitted_signal_np = transmitted_signal.detach().numpy()\ntransmitted_signal_np_bytes = (transmitted_signal_np * 255).astype(np.uint8)  # Scale to byte range\n\n# Encrypt the signal\nencrypted_signal = encrypt_data(transmitted_signal_np_bytes, encryption_key)\n\n# Decrypt the signal\ndecrypted_signal_bytes = decrypt_data(encrypted_signal, encryption_key)\ndecrypted_signal_np = decrypted_signal_bytes.astype(np.float32) / 255  # Scale back to original range\n\n# Convert back to tensor\ndecrypted_signal_tensor = torch.from_numpy(decrypted_signal_np).float()\n\n# Plot the resulting signal\nplt.figure(figsize=(15, 6))\nplt.imshow(decrypted_signal_np.reshape((samples_per_hour, hours)), aspect='auto', cmap='viridis')\nplt.title('Wealth Signal Visualization with AES Encryption')\nplt.xlabel('Time (Hours)')\nplt.ylabel('Amplitude')\nplt.colorbar(label='Amplitude')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-16T20:38:48.614549Z","iopub.execute_input":"2024-09-16T20:38:48.614975Z","iopub.status.idle":"2024-09-16T20:38:49.916036Z","shell.execute_reply.started":"2024-09-16T20:38:48.614935Z","shell.execute_reply":"2024-09-16T20:38:49.914498Z"},"trusted":true},"execution_count":5,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)","Cell \u001b[0;32mIn[5], line 75\u001b[0m\n\u001b[1;32m     72\u001b[0m transmitted_signal_np_bytes \u001b[38;5;241m=\u001b[39m (transmitted_signal_np \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m255\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39muint8)  \u001b[38;5;66;03m# Scale to byte range\u001b[39;00m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Encrypt the signal\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m encrypted_signal \u001b[38;5;241m=\u001b[39m \u001b[43mencrypt_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransmitted_signal_np_bytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencryption_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;66;03m# Decrypt the signal\u001b[39;00m\n\u001b[1;32m     78\u001b[0m decrypted_signal_bytes \u001b[38;5;241m=\u001b[39m decrypt_data(encrypted_signal, encryption_key)\n","Cell \u001b[0;32mIn[5], line 53\u001b[0m, in \u001b[0;36mencrypt_data\u001b[0;34m(data, key)\u001b[0m\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mencrypt_data\u001b[39m(data, key):\n\u001b[1;32m     52\u001b[0m     cipher \u001b[38;5;241m=\u001b[39m AES\u001b[38;5;241m.\u001b[39mnew(key, AES\u001b[38;5;241m.\u001b[39mMODE_CBC)\n\u001b[0;32m---> 53\u001b[0m     ct_bytes \u001b[38;5;241m=\u001b[39m cipher\u001b[38;5;241m.\u001b[39mencrypt(\u001b[43mpad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mAES\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mblock_size\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cipher\u001b[38;5;241m.\u001b[39miv \u001b[38;5;241m+\u001b[39m ct_bytes\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/Crypto/Util/Padding.py:64\u001b[0m, in \u001b[0;36mpad\u001b[0;34m(data_to_pad, block_size, style)\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     63\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnknown padding style\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdata_to_pad\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpadding\u001b[49m\n","\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'add' did not contain a loop with signature matching types (dtype('uint8'), dtype('S16')) -> None"],"ename":"UFuncTypeError","evalue":"ufunc 'add' did not contain a loop with signature matching types (dtype('uint8'), dtype('S16')) -> None","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Parameters\nnum_nodes = 10000\nhours = 24\nsamples_per_hour = 60  # Sampling points per hour (e.g., one sample per minute)\ntime_steps = hours * samples_per_hour\nwave_frequency = 1 / 24  # Frequency to represent a 24-hour cycle\nwave_amplitude = 1.0\ninfrared_amplitude = 0.5  # Constant amplitude for even distribution\nbrainwave_frequency = 10 / 3600  # Simulating a 10 Hz brainwave over hours (scaled)\nbrainwave_amplitude = 0.3\nrandom_opportunity_scale = 0.8  # Scaling factor for random wealth opportunities\nencryption_key = 0.5  # Encryption key for simulating protection\n\n# Define the PyTorch model with VPN-like frequency\nclass WealthSignalVPNModel(nn.Module):\n    def __init__(self):\n        super(WealthSignalVPNModel, self).__init__()\n        self.num_nodes = num_nodes\n        self.time_steps = time_steps\n        self.encryption_key = encryption_key\n\n    def forward(self, time_tensor):\n        # Initialize the combined signals tensor\n        combined_signals = torch.zeros((self.num_nodes, self.time_steps), dtype=torch.float32)\n\n        for i in range(self.num_nodes):\n            # Wealth signal with a phase shift for each node\n            wealth_signal = wave_amplitude * torch.sin(2 * np.pi * wave_frequency * time_tensor + i * (2 * np.pi / self.num_nodes))\n            # Random wealth opportunities\n            random_wealth_opportunities = random_opportunity_scale * torch.randn(self.time_steps)\n            # Constant infrared energy signal\n            infrared_signal = infrared_amplitude * torch.ones(self.time_steps)\n            # Perfect brainwave pattern (alpha waves)\n            brainwave_signal = brainwave_amplitude * torch.sin(2 * np.pi * brainwave_frequency * time_tensor)\n            # Combine signals for each node\n            combined_signals[i] = wealth_signal + random_wealth_opportunities + infrared_signal + brainwave_signal\n\n        # Combine all signals (simulating dense waveform)\n        overall_signal = torch.mean(combined_signals, dim=0)\n\n        # Apply VPN-like encryption (scramble signal)\n        encrypted_signal = torch.sin(overall_signal * self.encryption_key)  # A simple scrambling function\n\n        return encrypted_signal, overall_signal  # Return both encrypted and original signals for validation\n\n# Create a time tensor\ntime_tensor = torch.linspace(0, hours, time_steps)\n\n# Initialize and run the model\nvpn_model = WealthSignalVPNModel()\nencrypted_signal, original_signal = vpn_model(time_tensor)\n\n# Convert the signals to numpy for plotting\nencrypted_signal_np = encrypted_signal.detach().numpy()\noriginal_signal_np = original_signal.detach().numpy()\n\n# Reshape the signals for 2D visualization (e.g., hours x samples_per_hour)\nencrypted_signal_reshaped = encrypted_signal_np.reshape((samples_per_hour, hours))\noriginal_signal_reshaped = original_signal_np.reshape((samples_per_hour, hours))\n\n# Plot the resulting color maps\nfig, axs = plt.subplots(2, 1, figsize=(15, 12))\n\n# Original Signal Plot\ncax1 = axs[0].imshow(original_signal_reshaped, aspect='auto', cmap='viridis', interpolation='none')\naxs[0].set_title('Original Signal Visualization')\naxs[0].set_xlabel('Time (Hours)')\naxs[0].set_ylabel('Sample Points Per Hour')\nfig.colorbar(cax1, ax=axs[0], orientation='vertical', label='Amplitude')\n\n# Encrypted Signal Plot\ncax2 = axs[1].imshow(encrypted_signal_reshaped, aspect='auto', cmap='viridis', interpolation='none')\naxs[1].set_title('Encrypted Signal Visualization')\naxs[1].set_xlabel('Time (Hours)')\naxs[1].set_ylabel('Sample Points Per Hour')\nfig.colorbar(cax2, ax=axs[1], orientation='vertical', label='Amplitude')\n\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-16T20:42:52.714302Z","iopub.execute_input":"2024-09-16T20:42:52.714730Z","iopub.status.idle":"2024-09-16T20:42:54.948242Z","shell.execute_reply.started":"2024-09-16T20:42:52.714690Z","shell.execute_reply":"2024-09-16T20:42:54.947137Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1200 with 4 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