{"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\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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-16T20:51:05.127546Z","iopub.execute_input":"2024-09-16T20:51:05.128047Z","iopub.status.idle":"2024-09-16T20:51:11.189466Z","shell.execute_reply.started":"2024-09-16T20:51:05.127998Z","shell.execute_reply":"2024-09-16T20:51:11.187881Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1200 with 4 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