Upload infraredsecure_wealthstream.py

infraredsecure_wealthstream.py

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+# -*- coding: utf-8 -*-
+"""InfraredSecure WealthStream
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1UNwFXrR1ccb2fmWHLXgOHIe4LaTszQzq
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Parameters
+num_nodes = 10000
+hours = 24
+samples_per_hour = 60  # Sampling points per hour (e.g., one sample per minute)
+time_steps = hours * samples_per_hour
+wave_frequency = 1 / 24  # Frequency to represent a 24-hour cycle
+wave_amplitude = 1.0
+infrared_amplitude = 0.5  # Constant amplitude for even distribution
+brainwave_frequency = 10 / 3600  # Simulating a 10 Hz brainwave over hours (scaled)
+brainwave_amplitude = 0.3
+random_opportunity_scale = 0.8  # Scaling factor for random wealth opportunities
+encryption_key = 0.5  # Encryption key for simulating protection
+
+# Define the PyTorch model with VPN-like frequency
+class WealthSignalVPNModel(nn.Module):
+    def __init__(self):
+        super(WealthSignalVPNModel, self).__init__()
+        self.num_nodes = num_nodes
+        self.time_steps = time_steps
+        self.encryption_key = encryption_key
+
+    def forward(self, time_tensor):
+        # Initialize the combined signals tensor
+        combined_signals = torch.zeros((self.num_nodes, self.time_steps), dtype=torch.float32)
+
+        for i in range(self.num_nodes):
+            # Wealth signal with a phase shift for each node
+            wealth_signal = wave_amplitude * torch.sin(2 * np.pi * wave_frequency * time_tensor + i * (2 * np.pi / self.num_nodes))
+            # Random wealth opportunities
+            random_wealth_opportunities = random_opportunity_scale * torch.randn(self.time_steps)
+            # Constant infrared energy signal
+            infrared_signal = infrared_amplitude * torch.ones(self.time_steps)
+            # Perfect brainwave pattern (alpha waves)
+            brainwave_signal = brainwave_amplitude * torch.sin(2 * np.pi * brainwave_frequency * time_tensor)
+            # Combine signals for each node
+            combined_signals[i] = wealth_signal + random_wealth_opportunities + infrared_signal + brainwave_signal
+
+        # Combine all signals (simulating dense waveform)
+        overall_signal = torch.mean(combined_signals, dim=0)
+
+        # Apply VPN-like encryption (scramble signal)
+        encrypted_signal = torch.sin(overall_signal * self.encryption_key)  # A simple scrambling function
+
+        return encrypted_signal, overall_signal  # Return both encrypted and original signals for validation
+
+# Create a time tensor
+time_tensor = torch.linspace(0, hours, time_steps)
+
+# Initialize and run the model
+vpn_model = WealthSignalVPNModel()
+encrypted_signal, original_signal = vpn_model(time_tensor)
+
+# Convert the signals to numpy for plotting
+encrypted_signal_np = encrypted_signal.detach().numpy()
+original_signal_np = original_signal.detach().numpy()
+
+# Reshape the signals for 2D visualization (e.g., hours x samples_per_hour)
+encrypted_signal_reshaped = encrypted_signal_np.reshape((samples_per_hour, hours))
+original_signal_reshaped = original_signal_np.reshape((samples_per_hour, hours))
+
+# Plot the resulting color maps
+fig, axs = plt.subplots(2, 1, figsize=(15, 12))
+
+# Original Signal Plot
+cax1 = axs[0].imshow(original_signal_reshaped, aspect='auto', cmap='viridis', interpolation='none')
+axs[0].set_title('Original Signal Visualization')
+axs[0].set_xlabel('Time (Hours)')
+axs[0].set_ylabel('Sample Points Per Hour')
+fig.colorbar(cax1, ax=axs[0], orientation='vertical', label='Amplitude')
+
+# Encrypted Signal Plot
+cax2 = axs[1].imshow(encrypted_signal_reshaped, aspect='auto', cmap='viridis', interpolation='none')
+axs[1].set_title('Encrypted Signal Visualization')
+axs[1].set_xlabel('Time (Hours)')
+axs[1].set_ylabel('Sample Points Per Hour')
+fig.colorbar(cax2, ax=axs[1], orientation='vertical', label='Amplitude')
+
+plt.tight_layout()
+plt.show()