import torch
import torch.nn as nn
import numpy as np
import matplot.pyplot as plt

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulatoin_frequency = 50

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)
    pwm_signal = np.where(sine_wave > np.random.rand(len(time)), 1, 0)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmission(stored_signal, phase_shift):
    transmitted_signal = np.roll(stored_signal, phase_shift)

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signal = infrared_storage(spwm_signal, infrared_voltage)

transmitted_signal = directional_transmission(infrared_stored_signal, phase_shift=100)
plt.figure(figsize=(15, 8))

plt.subplot(3, 1, 1)
plt.plot(time, spwm_signal, color='blue', label='SPWM Signal')
plt.title('Sinusoidal Pulse Width Modulation (SPWM) Signal')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.subplot(3, 1, 2)
plt.plot(time, infrared_stored_signal, color='red', label='Infrared Stored Signal')
plt.title('Data Stored using Infrared Voltage Energy')
plt.xlabel('Time (s)')
plt.ylabel('Voltage')
plt.grid(True)
plt.legend()

plt.subplot(3, 1, 3)
plt.plot(time, tranmitted_signal, color='green', label=Transmitted Signal')
plt.title('Transmitted Signal towards a Given Direction')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.tight_layout()
plt.show()

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulation_frequency = 50
attenuation_factor = 0.5 
noise_intensity = 0.2
multi_path_delay = 50
multi_path_amplitude = 0.3

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)
    pwn_signal = np.where(sine_wave > np.random.rand(len(time)), 1, 0)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmission(stored_signal, phase_shift):
    transmitted_signal = np.roll(stored_signal, phase_shift)
    return transmitted_signal

def attenuate_signal(signal, attenuation_factor):
    attenuation = np.exp(-attenuation_factor * np.arange(len(signal)) / len(signal))
    attenuated_signal = signal * attenuation
    return attenuated_signal

def add_noise(signal, noise_intensity):
    noise = noise_intensity * np.random.randn(len(signal))
    return noisy_signal

def multi_path_effects(signal, delaym amplitude):
    delayed_signal = np.roll(signal, delay) * amplitude
    combined_signal = signal + delayed_signal
    return combined_signal

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signal = infrared_storage(spwm_signal, infrared_voltage)

transmitted_signal = directional_transmission(infreared_stored_signal, phase_shift=100)

attenuated_signal = attenuate_signal(transmitted_signal, attenuation_factor)

noisy_signal, add_noise(attenuated_signal, noise_intensity)

final_signal = multi_path_effects(noisy_signal, multi_path_delay, mutli_path_amplitude

plt.figure(figsize=(15, 12))

plt.subplot(4, 1, 1)
plt.plot(time, spwm_signal, color='blue', label='SPWM Signal')
plt.title('Sinusodial Pulse Width Modulation (SPWM) Signal')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.subplot(4, 1, 2)
plt.plot(time, infrared_stored_signal, color='red', label='Infrared Stored Signal')
plt.title('Data Stored using Infrared Voltage Energy')
plt.xlabel('Time (s)')
plt.ylabel('Voltage')
plt.grid(True)
plt.legend()

plt.subplot(4, 1, 3)
plt.plot(time, transmited_signal, color='green', label='Transmitted Signal')
plt.title('Transmitted Signal towards a Given Direction')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.subplot(4, 1, 4)
plt.plot(time, final_signal, color='purple', lable='Final Signal with Attenuation, Noise, and Multi-Path Effects')
plt.title('Final Signal in Dense Space')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.tight_layout()
plt.show()

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animatoin import FuncAnimation

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulation_frequency = 50
atenuation_factor = 0.5
noise_intensity = 0.2
multi_path_delay = 50
multi_path_amplitude = 0.3

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)

    pwn_signal = np.where(sine_wave > np.random.rand(len(time)), 1, 0)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmission(stored_signal, phase_shift):
      transmitted_signal = np.roll(stored_signal, phase_shift)
      return transmitted_signal

def add_noise(signal, noise_intensity):
    noise = noise_intensity * np.random.randn(len(signal))
    noisy_signal = signal + noise
    return noisy_signal

def multi_path_effects(signal, delay, amplitude):
  delayed_signal = np.roll(signal, delay) * amplitude
  combined_signal = signal + delayed_signal
  return combined_signal

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signal = infrared_storage(spwm_signal, infrared_voltage)
transmitted_signal = directional_transmission(infrared_stored_signal, phase_shift=100)

attenuated_signal = attenuated_signal(transmitted_signal, attenuation_factor)

noisy_signal = add_noise(attenuated_signal, noise_intensity

final_signal = multi_paht_effects(noisy_signal, multi_path_delay, multi_path_amplitude

fig, ax = plt.subplots(figsize=(15, 6))
line, = ax.plot([], [], color='purple')
ax.set_xlim(0, time_steps)
ax.set_ylim(-1.5, 1.5)
ax.set_title('Animated Signal Transmission')
ax.set_xlabel('Time Step')
ax.set_ylabel('Amplitude')
ax.grid(True)

def animate(frame):
    current_signal = np.roll(final_signal, frame)
    line.set_data(np.arange(len(current_signal)), current_signal)
    return line,

ani = FuncAnimation(fig, animate, frames=time_steps, interval=20, blit=True)

plt.show()

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulation_frequency = 50
attenuation_factor = 0.5
noise_intensity = 0.2
mutli_path_delay = 50
multi_path_amplitude = 0.3

encryption_keys = [0.5, 1.2, 0.9]

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)
    pwm_signal = np.where(sine_wave > np.random.rand(len(time)), 1, 0)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmissoin(stored_signal, phase_shift):
    transmitted_signal = np.roll(stored_signal, phase_shift)
    return transmitted_signal

def attenuate_signal(signal, attenuation_factor):
    attenuatoin = np.exp(-attenuation_factor * np.arange(len(signal)) / len(signal))
    attenuated_signal = signal * attenuation
    return attenuated_signal

def add_noise(signal, noise_intensity):
    noise = noise_intensity * np.random.randn(len(signal))
    noisy_signal = signal + noise
    return noisy_signal

def multi_path_effects(signal, delay, amplitude):
    delayed_signal = np.roll(signal, delay) * amplitude
    combined_signal = signal + delayed_signal
    return combined_signal

def layered_encryption(signal, keys):
    encrypted_signal = signal.copy()
    for key in keys:
        encrypted_signal = np.sin(encrypted_signal * key)
        return encrypted_signal

def layered_decryption(encrypted_signal, keys):
    decrypted_signal = encrypted_signal.copy()
    for key in reversed(keys):
        decrypted_signal = np.arcsin(decrypted)signal) / key
        return decrypted_signal

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signla = infrared_storage(spwm_signal, infreared_voltage)

transmitted_signal = directional_transmission(infrared_stored_signal, phase_shift=100)

attenuated_signal = attenuate_signal(transmitted_signal, attenuation_factor)

noisy_signal = add_noise(attenuated_signal, noise_intensity)

final_signal = multi_path_effects(noisy_signal, multi_path_delay, multi_path_amplitude)

encrypted_signal = layered_encryption(final_signal, encryption_keys)

decrypted_signal = layered_decryption(encrypted_signal, encryption_keys)

fig, ax = plt.subplots(2, 1, figsize=(15, 12))

ax[0].plot(np.arange(len(encrypted_signal)), encrypted_signal, color='purple')
ax[0].set_title('Encrypted Signal w/Layered VPN Protection')
ax[0].set_xlabel('Time Step')
ax[0].set_ylabel('Amplitude')
ax[0].grid(True)

ax[1].plot(np.arange(len(decrypted_signal)), decrypted_signal, color='green')
ax[1].set_title('Decrypted Signal w/Layered VPN Decryption')
ax[1].set_xlabel('Time Step')
ax[1].set_ylabel('Amplitude')
ax[1].grid(True)

plt.tight_layour()
plt.show()

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulatoin_frequency = 50
attenuation_factor = 0.5
noise_intensity = 0.2
multi_path_delay = 50
multi_path_amplitude = 0.3

encryption_keys = [0.5, 1.2, 0.9]

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)
    threshold = np.mean(sine_wave_
    pwm_signal = np.where(sine_wave > threshold, 1, 0)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmissoin(stored_signal, phase_shift):
    transmitted_signal = np.roll(stored_signal, phase_shift)
    return transmitted_signal

def attenuate_signal(signal, attenuation_factor):
    attenuation = np.exp(-attenuation_factor * np.arange(len(signal)) / len(signal))
    attenuated_signal = signal * attenuation
    return attenuated_signal

def add_noise(signal, noise_intensity):
    noise = noise_intensity * np.random.randn(len(signal))
    noisy_signal = signal + noise
    return noisy signal

def multi_path_effects(signal, delay, amplitude):
    delayed_signal = np.roll(signal, delay) * amplitude
    combined_signal = signal + delayed_signal
    return combined_signal

def layered_encryption(signal, keys):
    encrypted_signal = signal.copy()
    for key in keys:
        encrypted_signal = np.sin(encrypted_signal * key)
    return encrypted_signal

def layered_decryption(encrypted_signal, keys):
    decrypted_signal = encrypted_signal.copy()
    for key in reversed(keys):
        decrypted_signal = np.arcsin(decrypted_signal) / key
    return decrypted_signal

def validate_encryption(original_signal, encrypted_signal, decrypted_signal):
    assert np.allclose(original_signal, decrypted_signal, atol=1e-2), "Decryption failed to recover the original signal."

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signal = infrared_storage(spwm_signal, infrared_voltage)

transmitted_signal = directional_transmission(infrared_stored_signal, phase_shift=100)

attenuated_signal = attenuate_signal(transmitted_signal, attenuation_factor)

noisy_signal = add_noise(attenuated_signal, noise_intensity)

final_signal = multi_path_effects(noisy_signal, multi_path_delay, multi_path_amplitude)

encrypted_signal = layered_encryption(final_signal, encryption_keys)

decrypted_signal = layered_decryption(encrypted_signal, encryption_keys)

fig, ax= plt.subplots(2, 1, figsize=(15, 12))

ax[0].plot(np.arant(len(encrypted_signal)), encrypted_signal, color='purple')
ax[0].set_title('Encrypted Signal w/Layered VPN Protection')
ax[0].set_xlabel('Time Step')
ax[0].set_ylabel('Amplitude')
ax[0].grid(True)

ax[1].plot(np.arange(len(decrypted_signal)), decrypted_signal, color='green')
ax[1].set_title('Decrypted Signal w/Layered VPN Decryption')
ax[1].set_xlabel('Time Step')
ax[1].set_ylabel('Amplitude')
ax[1].grid(True)

plt.tight_layout()
plt.show()

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

num_nodes = 100
time_steps = 1000
frequency = 1
amplitude = 1.0
sampling_rate = 1000
infrared_voltage = 0.7
pulse_width_modulation_frequency = 50
attenuation_factor = 0.5
noise_intensity = 0.2
multi_path_delay = 50
multi_path_amplitude = 0.3

encryption_keys = [0.5, 1.2, 0.9]

def generate_spwm_signal(time, frequency, amplitude):
    sine_wave = amplitude * np.sin(2 * np.pi * frequency * time)
    threshold = np.mean(sine_wave)
    pwm_signal = np.where(sine_wave)
    return pwm_signal

def infrared_storage(pwm_signal, voltage):
    stored_signal = pwm_signal * voltage
    return stored_signal

def directional_transmission(stored_signal, phase_shift):
    transmitted_signal = np.roll(stored_signal, phase_shift)
    return transmited_signal

def attenuate_signal(signal, attenuation_factor):
    attenuation = np.exp(-attenuation_factor * np.arange(len(signal)) / len(signal))
    attenuated_signal = signal * attenuation
    return attenuated_signal

def add_noise(signal, noise_intensity):
    noise = noise_intensity * np.random.randn(len(signal))
    noisy_signal = signal + noise
    return noisy_signal

def multi_path_effects(signal, delay, amplitude):
    delayed_signal = np.roll(signal, delay) * amplitude
    combined_signal = signal + delayed_signal
    return combined_signal

def layered_encryption(signal, keys):
    encrypted_signal = signal.copy()
    for key in keys:
        encrypted_signal = np.sin(encrypted_signal * key) 
        return encrypted_signal

def layered_decryption(encrypted_signal, keys):
    decrypted_signal = encrypted_signal.copy()
    for key in reveresed(keys):
        decrypted_signal = np.arcsin(decrypted_signal) / key
    return decrypted_signal

def validate_encryption(original_signal, encrypted_signal, decrypted_signal):
    assert np.allclose(original_signal, decrypted_signal, atol=1e-2), "Decryption failed to recover the original signal."

time = np.linspace(0, 1, time_steps)

spwm_signal = generate_spwm_signal(time, frequency, amplitude)

infrared_stored_signal = infrared_storage(spwm_signal, infared_voltage)

transmitted_signal = directional_transmission(infrared_stored_signal, phase_shift=100)

attenuated_signal = attenuate_signal(transmitted_signal, attenuation_factor)

noisy_signal = add_noise(attenuated_signal, noise_intensity)

final_signal = multi_path_effects(noisy_signal, multi_path_delay, multi_path_amplitude_

encrypted_signal = layered_encryption(final_signal, encryption_keys)

decrypted_signal = layered_decryption(encrypted_signal, encryption_keys)

fig, ax = plt.subplots(2, 1, figsize=(15, 12))

ax[0].plot(np.arange(len(encrypted_signal)), encrypted_signal, color='purple')
ax[0].set_title('Encrypted Signal w/Layered VPN Protection')
ax[0].set_xlabel('Time Step')
ax[0].set_ylabel('Amplitude')
ax[0].grid(True)

ax[1].plot(np.arange(len(decrypted_signal)), decrypted_signal, color='green')
ax[1].set_title('Decrypted Signal w/Layered VPN Decryption')
ax[1].set_xlabel('Time Step')
ax[1].set_ylabel('Amplitude')
ax[1].grid(True)

plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt
import numpy as np

def gradient_color(signal, cmap='viridis'):
    norm = plt.Normalizer(signal.min(), signal.max())
    colors = plt.get_cmap(cmap)norm(signal))
    return colors

time = np.arange(len(final_signal))

colors = gradient_color(final_signal)

fig, ax = plt.subplots(figsize(15, 6))

ax.plot(time, final_signal, color='blue', label='Final Signal')

reflection_factor = 0.3
reflection = final_signal * reflection_factor
reflection_color = 'lightblue'

ax.plot(time, -reflection - reflection.min(), color=reflection_color, linestyle='--', alpha=0.6, label='Signal Reflection')

for i in range*len(final_signal) - 1):
    ax.plot(time[i:i+2], final_signal[i:i+2], color=colors[i], lw=2)

ax.set_title('Final Signal with Reflection and Color Gradient')
ax.set_ylabel('Amplitude')
ax.legend()
ax.grid(True)

plt.show()

import matplotlib.pyplot as plt
iport numpy as np
import matplotlib.colors as mcolors

def gradient_color(signal, cmap='viridis'):
    norm = plt.Normalize(signal.min(), signal.max())
    colors = plt.get_cmap(cmap(norm(signal))
    return colors

time = np.arange(len(final_signal))

colors = gradient_color(final_signal)

fig, ax = plt.subplots(figsize=(15, 6))

for i in range(len(final_signal) - 1):
    ax.plot(time[i:i+2], final_signal[i:i+2], color=colors[i], lw=2)

ax.plot(time, final_signal, color='blue', alpha=0.5, label='Signal')

reflection_factor = 0.3
reflection = final_signal * reflection_factor
reflection_color = 'lightblue'

ax.plot(time, -reflection - reflectoin.min(), color=reflection_color, linestyle='--', alpha=0.6, lable='Reflection')

ax.set_title('PulseWavefront')
ax.set_xlabel('Time Step')
ax.set_ylabel('Amplitude')
ax.legend()
ax.grid(True)

plt.show()