| | |
| | """HoloWealth |
| | |
| | Automatically generated by Colab. |
| | |
| | Original file is located at |
| | https://colab.research.google.com/drive/1lObCKG_uGdcldMmKDoHnuSd34OUy4EmH |
| | """ |
| |
|
| | import torch |
| | import numpy as np |
| | import matplotlib.pyplot as plt |
| | from matplotlib.animation import FuncAnimation |
| |
|
| | waveform_size = 100 |
| | frequency = 0.5 |
| | amplitude = 5.0 |
| | direction_angle = np.pi / 4 |
| | total_time_hours = 24 |
| | time_steps = 240 |
| |
|
| | time_interval = total_time_hours / time_steps |
| |
|
| | 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) |
| |
|
| | def infinite_waveform(t): |
| | return amplitude * torch.cos(2 * np.pi * frequency * (X * torch.cos(direction) + Y * torch.sin(direction_angle)) + 2 * np.pi * t) |
| |
|
| | wealth_data = torch.rand(waveform_size, waveform_size) * 100 |
| | total_wealth_energy = wealth_data ** 2 |
| |
|
| | noise_mask = torch.randn(waveform_size, waveform_size) * 0.1 |
| | protected_wealth_energy = total_wealth_energy + noise_mask |
| |
|
| | wealth_energy_per_time = protected_wealth_energy / time_steps |
| |
|
| | fig, ax = plt.subplots(figsize=(8, 6)) |
| | signal_plot = ax.imshow(torch.zeros(waveform_size, waveform_size).numpy(), cmap='plasma', origin='lower') |
| | plt.colorbar(signal_plot, ax=ax, label='Signal Intensity') |
| | ax.set_title("HoloWealth") |
| | ax.set_xlabel('X Axis') |
| | ax.set_ylabel('Y Axis') |
| |
|
| | def update(t): |
| | wave = infinite_waveform(t * time_interval) |
| | combined_signal = wave * wealth_energy_per_time |
| | signal_plot.set_data(combined_signal.numpy()) |
| | ax.set_title(f"Signal at Time Step: {t}/{time_steps}") |
| |
|
| | ani = FuncAnimation(fig, update, frames=time_steps, interval=100, repeat=False) |
| |
|
| | plt.show() |