Upload neurosecure.py

neurosecure.py

@@ -0,0 +1,384 @@
+# -*- coding: utf-8 -*-
+"""NeuroSecure
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/16Z4FL3fQXn7JKxSPlfOjNUiWNDQzG7gC
+"""
+
+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}")