Upload cs_w_edwe.py

cs_w_edwe.py

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+# -*- coding: utf-8 -*-
+"""CS w/ EDWE
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1e3OBMKsTw9vFwJPjY2IGPPBya-R5Mf2z
+"""
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Parameters for the waveform and time
+waveform_size = 100  # Size of the 2D grid (waveform)
+frequency = 0.5  # Frequency of the wave
+amplitude = 5.0  # Amplitude of the wave
+direction_angle = np.pi / 4  # Direction in radians (e.g., pi/4 is 45 degrees)
+total_time_hours = 24  # Total timespan in hours
+time_steps = 240  # Number of time steps (e.g., 240 time steps for 24 hours means 10 steps per hour)
+
+# Time step interval (e.g., 10 time steps per hour)
+time_interval = total_time_hours / time_steps
+
+# Generate a 2D grid of coordinates
+x = torch.linspace(-waveform_size // 2, waveform_size // 2, waveform_size)
+y = torch.linspace(-waveform_size // 2, waveform_size // 2, waveform_size)
+X, Y = torch.meshgrid(x, y)
+
+# First Layer: Infinite directional waveform (repeating signal over time)
+def infinite_waveform(t):
+    return amplitude * torch.cos(2 * np.pi * frequency * (X * torch.cos(torch.tensor(direction_angle)) + Y * torch.sin(torch.tensor(direction_angle))) + 2 * np.pi * t)
+
+# Second Layer: Wealth Data transformed into energy
+wealth_data = torch.rand(waveform_size, waveform_size) * 100  # Simulate random wealth values
+total_wealth_energy = wealth_data ** 2  # Convert wealth to energy
+
+# Third Layer: VPN protection (adding noise or encryption to wealth data)
+noise_mask = torch.randn(waveform_size, waveform_size) * 0.1  # Small random noise
+protected_wealth_energy = total_wealth_energy + noise_mask  # Obscure wealth data with noise
+
+# Evenly distribute wealth energy over the 24-hour period (each time step receives a fraction of wealth)
+wealth_energy_per_time = protected_wealth_energy / time_steps
+
+# Simulate the combined signal over 24 hours (even distribution of wealth energy)
+infinite_signal = torch.zeros(waveform_size, waveform_size)
+for t in range(time_steps):
+    wave = infinite_waveform(t * time_interval)  # Scale time by interval
+    infinite_signal += wave * wealth_energy_per_time  # Evenly distribute wealth energy over time
+
+# Visualize the final infinite signal that combines all layers over the 24-hour period
+plt.figure(figsize=(8, 6))
+plt.imshow(infinite_signal.numpy(), cmap='plasma', origin='lower')
+plt.title("24-Hour Combined Signal with Evenly Distributed Wealth Energy")
+plt.colorbar(label='Signal Intensity')
+plt.xlabel('X Axis')
+plt.ylabel('Y Axis')
+plt.show()