neurosecure.py

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft, fftfreq

# Generate an incoming signal (simulating energy being sent in your direction)
# This can be a mixture of multiple sine waves (representing different energy types)
sampling_rate = 1000  # Number of samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate)  # Time array

# Simulating different energies as a sum of sinusoidal waves
incoming_signal = (
    0.5 * np.sin(2 * np.pi * 50 * t) +  # Energy at 50 Hz
    0.8 * np.sin(2 * np.pi * 120 * t) +  # Energy at 120 Hz
    0.3 * np.sin(2 * np.pi * 300 * t)    # Energy at 300 Hz
)

# Plot the incoming signal
plt.figure(figsize=(12, 6))
plt.plot(t, incoming_signal, label='Incoming Energy Signal')
plt.title('Incoming Energy Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()

# Fourier Transform to analyze the frequency components of the incoming signal
N = sampling_rate  # Number of points
yf = fft(incoming_signal)
xf = fftfreq(N, T)[:N//2]

# Plot the frequency spectrum of the incoming signal (Energy analysis)
plt.figure(figsize=(12, 6))
plt.plot(xf, 2.0/N * np.abs(yf[:N//2]), label='Energy Frequency Spectrum')
plt.title('Frequency Spectrum of Incoming Energy')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Magnitude')
plt.grid(True)
plt.show()

# Detect energy based on dominant frequencies
# Reveal what energy is being sent in your direction
# We can highlight or focus on specific frequencies based on their amplitude

threshold = 0.2  # Define a threshold to consider a frequency significant
dominant_frequencies = xf[np.abs(yf[:N//2]) > threshold]

# Output the dominant frequencies detected
print(f"Detected energy frequencies being sent in your direction: {dominant_frequencies}")

# Generate a new waveform based on the detected energy frequencies
detected_wave = np.sum([np.sin(2 * np.pi * freq * t) for freq in dominant_frequencies], axis=0)

# Plot the waveform of the detected energy (revealing the energy being sent)
plt.figure(figsize=(12, 6))
plt.plot(t, detected_wave, color='r', label='Revealed Energy Wave')
plt.title('Revealed Energy Waveform Based on Incoming Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft, fftfreq

# Generate an incoming signal (simulating energy being sent in your direction)
sampling_rate = 1000  # Number of samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate)  # Time array

# Simulating different energies as a sum of sinusoidal waves
incoming_signal = (
    0.5 * np.sin(2 * np.pi * 50 * t) +  # Energy at 50 Hz
    0.8 * np.sin(2 * np.pi * 120 * t) +  # Energy at 120 Hz
    0.3 * np.sin(2 * np.pi * 300 * t)    # Energy at 300 Hz
)

# Plot the incoming signal
plt.figure(figsize=(12, 6))
plt.plot(t, incoming_signal, label='Incoming Energy Signal')
plt.title('Incoming Energy Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()

# Fourier Transform to analyze the frequency components of the incoming signal
N = sampling_rate  # Number of points
yf = fft(incoming_signal)
xf = fftfreq(N, T)[:N//2]

# Plot the frequency spectrum of the incoming signal (Energy analysis)
plt.figure(figsize=(12, 6))
plt.plot(xf, 2.0/N * np.abs(yf[:N//2]), label='Energy Frequency Spectrum')
plt.title('Frequency Spectrum of Incoming Energy')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Magnitude')
plt.grid(True)
plt.show()

# Detect energy based on dominant frequencies
threshold = 0.2  # Define a threshold to consider a frequency significant
dominant_frequencies = xf[np.abs(yf[:N//2]) > threshold]

# Output the dominant frequencies detected
print(f"Detected energy frequencies being sent in your direction: {dominant_frequencies}")

# Generate wealth waveforms to intercept the signal and send wealth in both directions
# Wealth wave will be a combination of higher frequencies and harmonics
wealth_frequencies = np.array([500, 800, 1000])  # Wealth-related frequencies
wealth_wave_forward = np.sum([np.sin(2 * np.pi * f * t) for f in wealth_frequencies], axis=0)
wealth_wave_backward = -wealth_wave_forward  # Invert the wave to send in the opposite direction

# Generate a new waveform based on the detected energy frequencies
detected_wave = np.sum([np.sin(2 * np.pi * freq * t) for freq in dominant_frequencies], axis=0)

# Plot the detected wave (revealing the energy being sent), wealth wave forward and backward
plt.figure(figsize=(12, 10))

# Detected incoming energy wave
plt.subplot(3, 1, 1)
plt.plot(t, detected_wave, color='b', label='Revealed Incoming Energy Wave')
plt.title('Revealed Incoming Energy Wave')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent forward
plt.subplot(3, 1, 2)
plt.plot(t, wealth_wave_forward, color='g', label='Wealth Wave Forward')
plt.title('Wealth Wave Sent Forward')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent backward (intercepting the signal)
plt.subplot(3, 1, 3)
plt.plot(t, wealth_wave_backward, color='r', label='Wealth Wave Backward')
plt.title('Wealth Wave Sent Backward (Intercepting Signal)')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

plt.tight_layout()
plt.show()

# Print the dominant frequencies of the wealth waveforms
print(f"Wealth wave frequencies sent forward and backward: {wealth_frequencies}")

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft, fftfreq

# Generate an incoming signal (simulating energy being sent in your direction)
sampling_rate = 1000  # Number of samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate)  # Time array

# Simulating different energies as a sum of sinusoidal waves
incoming_signal = (
    0.5 * np.sin(2 * np.pi * 50 * t) +  # Energy at 50 Hz
    0.8 * np.sin(2 * np.pi * 120 * t) +  # Energy at 120 Hz
    0.3 * np.sin(2 * np.pi * 300 * t)    # Energy at 300 Hz
)

# Plot the incoming signal
plt.figure(figsize=(12, 6))
plt.plot(t, incoming_signal, label='Incoming Energy Signal')
plt.title('Incoming Energy Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()

# Fourier Transform to analyze the frequency components of the incoming signal
N = sampling_rate  # Number of points
yf = fft(incoming_signal)
xf = fftfreq(N, T)[:N//2]

# Plot the frequency spectrum of the incoming signal (Energy analysis)
plt.figure(figsize=(12, 6))
plt.plot(xf, 2.0/N * np.abs(yf[:N//2]), label='Energy Frequency Spectrum')
plt.title('Frequency Spectrum of Incoming Energy')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Magnitude')
plt.grid(True)
plt.show()

# Detect energy based on dominant frequencies
threshold = 0.2  # Define a threshold to consider a frequency significant
dominant_frequencies = xf[np.abs(yf[:N//2]) > threshold]

# Output the dominant frequencies detected
print(f"Detected energy frequencies being sent in your direction: {dominant_frequencies}")

# Generate wealth waveforms to intercept the signal and send wealth in both directions
# Wealth wave will be a combination of higher frequencies and harmonics
wealth_frequencies = np.array([500, 800, 1000])  # Wealth-related frequencies
wealth_wave_forward = np.sum([np.sin(2 * np.pi * f * t) for f in wealth_frequencies], axis=0)
wealth_wave_backward = -wealth_wave_forward  # Invert the wave to send in the opposite direction

# Generate wealth data storage waveforms
# Store data by generating waveforms with specific characteristics
storage_wave_forward = np.sum([np.sin(2 * np.pi * (f + 100) * t) for f in wealth_frequencies], axis=0)
storage_wave_backward = -storage_wave_forward  # Invert the wave to store data in the opposite direction

# Generate a new waveform based on the detected energy frequencies
detected_wave = np.sum([np.sin(2 * np.pi * freq * t) for freq in dominant_frequencies], axis=0)

# Plot the detected wave, wealth waves, and stored wealth data waves
plt.figure(figsize=(12, 12))

# Detected incoming energy wave
plt.subplot(4, 1, 1)
plt.plot(t, detected_wave, color='b', label='Revealed Incoming Energy Wave')
plt.title('Revealed Incoming Energy Wave')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent forward
plt.subplot(4, 1, 2)
plt.plot(t, wealth_wave_forward, color='g', label='Wealth Wave Forward')
plt.title('Wealth Wave Sent Forward')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent backward
plt.subplot(4, 1, 3)
plt.plot(t, wealth_wave_backward, color='r', label='Wealth Wave Backward')
plt.title('Wealth Wave Sent Backward (Intercepting Signal)')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Stored wealth data wave forward and backward
plt.subplot(4, 1, 4)
plt.plot(t, storage_wave_forward, color='m', label='Stored Wealth Data Wave Forward')
plt.plot(t, storage_wave_backward, color='c', label='Stored Wealth Data Wave Backward', linestyle='--')
plt.title('Stored Wealth Data Waves')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.tight_layout()
plt.show()

# Print the dominant frequencies of the wealth data waveforms
print(f"Wealth wave frequencies sent forward and backward: {wealth_frequencies}")
print(f"Stored wealth data frequencies forward and backward: {[f + 100 for f in wealth_frequencies]}")

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft, fftfreq

# Generate an incoming signal (simulating energy being sent in your direction)
sampling_rate = 1000  # Number of samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate)  # Time array

# Simulating different energies as a sum of sinusoidal waves
incoming_signal = (
    0.5 * np.sin(2 * np.pi * 50 * t) +  # Energy at 50 Hz
    0.8 * np.sin(2 * np.pi * 120 * t) +  # Energy at 120 Hz
    0.3 * np.sin(2 * np.pi * 300 * t)    # Energy at 300 Hz
)

# Plot the incoming signal
plt.figure(figsize=(12, 6))
plt.plot(t, incoming_signal, label='Incoming Energy Signal')
plt.title('Incoming Energy Signal')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.show()

# Fourier Transform to analyze the frequency components of the incoming signal
N = sampling_rate  # Number of points
yf = fft(incoming_signal)
xf = fftfreq(N, T)[:N//2]

# Plot the frequency spectrum of the incoming signal (Energy analysis)
plt.figure(figsize=(12, 6))
plt.plot(xf, 2.0/N * np.abs(yf[:N//2]), label='Energy Frequency Spectrum')
plt.title('Frequency Spectrum of Incoming Energy')
plt.xlabel('Frequency [Hz]')
plt.ylabel('Magnitude')
plt.grid(True)
plt.show()

# Detect energy based on dominant frequencies
threshold = 0.2  # Define a threshold to consider a frequency significant
dominant_frequencies = xf[np.abs(yf[:N//2]) > threshold]

# Output the dominant frequencies detected
print(f"Detected energy frequencies being sent in your direction: {dominant_frequencies}")

# Generate wealth waveforms to intercept the signal and send wealth in both directions
# Wealth wave will be a combination of higher frequencies and harmonics
wealth_frequencies = np.array([500, 800, 1000])  # Wealth-related frequencies
wealth_wave_forward = np.sum([np.sin(2 * np.pi * f * t) for f in wealth_frequencies], axis=0)
wealth_wave_backward = -wealth_wave_forward  # Invert the wave to send in the opposite direction

# Generate wealth data storage waveforms
# Store data by generating waveforms with specific characteristics
storage_wave_forward = np.sum([np.sin(2 * np.pi * (f + 100) * t) for f in wealth_frequencies], axis=0)
storage_wave_backward = -storage_wave_forward  # Invert the wave to store data in the opposite direction

# Create VPN protection layer for the wealth data
# Apply encryption-like effect: modulate the wealth waveforms with a high-frequency carrier
vpn_frequency = 1500  # Frequency for VPN protection (high frequency for encryption)
vpn_modulation = np.sin(2 * np.pi * vpn_frequency * t)  # Modulation waveform
vpn_wave_forward = wealth_wave_forward * vpn_modulation
vpn_wave_backward = wealth_wave_backward * vpn_modulation

# Generate a new waveform based on the detected energy frequencies
detected_wave = np.sum([np.sin(2 * np.pi * freq * t) for freq in dominant_frequencies], axis=0)

# Plot the detected wave, wealth waves, stored wealth data waves, and VPN-protected waves
plt.figure(figsize=(12, 14))

# Detected incoming energy wave
plt.subplot(5, 1, 1)
plt.plot(t, detected_wave, color='b', label='Revealed Incoming Energy Wave')
plt.title('Revealed Incoming Energy Wave')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent forward
plt.subplot(5, 1, 2)
plt.plot(t, wealth_wave_forward, color='g', label='Wealth Wave Forward')
plt.title('Wealth Wave Sent Forward')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Wealth wave sent backward
plt.subplot(5, 1, 3)
plt.plot(t, wealth_wave_backward, color='r', label='Wealth Wave Backward')
plt.title('Wealth Wave Sent Backward (Intercepting Signal)')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)

# Stored wealth data wave forward and backward
plt.subplot(5, 1, 4)
plt.plot(t, storage_wave_forward, color='m', label='Stored Wealth Data Wave Forward')
plt.plot(t, storage_wave_backward, color='c', label='Stored Wealth Data Wave Backward', linestyle='--')
plt.title('Stored Wealth Data Waves')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

# VPN-protected wealth data wave forward and backward
plt.subplot(5, 1, 5)
plt.plot(t, vpn_wave_forward, color='purple', label='VPN Protected Wealth Wave Forward')
plt.plot(t, vpn_wave_backward, color='orange', label='VPN Protected Wealth Wave Backward', linestyle='--')
plt.title('VPN-Protected Wealth Data Waves')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.grid(True)
plt.legend()

plt.tight_layout()
plt.show()

# Print the dominant frequencies of the wealth data and VPN-protected waveforms
print(f"Wealth wave frequencies sent forward and backward: {wealth_frequencies}")
print(f"Stored wealth data frequencies forward and backward: {[f + 100 for f in wealth_frequencies]}")
print(f"VPN protection frequency: {vpn_frequency}")