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}")