import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation def generate_brainwave(frequency, t, phase_shift=0): return np.sin(2 * np.pi * frequency * t + phase_shift) def portal_organize(frequencies): # Example of organizing the data: averaging and normalizing organized_data = np.mean(frequencies, axis=0) normalized_data = (organized_data - np.min(organized_data)) / (np.max(organized_data) - np.min(organized_data)) return normalized_data def scramble_data(data, delay): # Simulate a delay by shifting the data and adding a scrambling effect scrambled_data = np.roll(data, delay) # Shift data by 'delay' samples scrambled_data += np.random.normal(0, 0.1, size=data.shape) # Add noise as a scrambling effect return scrambled_data def update(frame, lines, t, duration, sampling_rate): # Adjust time shifts for different movement speeds t_shifted1 = t + frame / (sampling_rate * 1.0) # First layer speed (base) t_shifted2 = t + frame / (sampling_rate * 1.25) # Second layer speed (slightly faster) t_shifted3 = t + frame / (sampling_rate * 1.75) # Third layer speed (moderately faster) t_shifted4 = t + frame / (sampling_rate * 2.25) # Fourth layer speed (fastest) t_shifted5 = t + frame / (sampling_rate * 3.0) # Fifth layer speed (fastest) t_shifted6 = t + frame / (sampling_rate * 4.0) # Sixth layer speed (with delay) # First layer: Alpha and Beta waves with financial frequencies alpha_wave = generate_brainwave(10, t_shifted1) # Alpha (10 Hz) beta_wave = generate_brainwave(20, t_shifted1) # Beta (20 Hz) financial_wave = generate_brainwave(15, t_shifted1, phase_shift=0.5) # Financial frequency (15 Hz) combined_wave1 = (alpha_wave + beta_wave) / 2 # Transfer mechanism: Influence the second layer based on the first layer's financial frequencies influence_factor = (financial_wave - np.mean(financial_wave)) / np.std(financial_wave) # Update frequencies based on influence factor theta_frequency = 6 + influence_factor # Adjusted Theta frequency gamma_frequency = 40 + influence_factor # Adjusted Gamma frequency theta_wave = generate_brainwave(theta_frequency, t_shifted2, phase_shift=0.3) gamma_wave = generate_brainwave(gamma_frequency, t_shifted2, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 # Transfer mechanism: Use second layer data to influence the third layer transfer_factor = np.mean(theta_wave) # Transfer factor based on mean value of theta wave delta_frequency = 2 + transfer_factor # Adjusted Delta frequency high_beta_frequency = 30 + transfer_factor # Adjusted High Beta frequency delta_wave = generate_brainwave(delta_frequency, t_shifted3, phase_shift=1.0) high_beta_wave = generate_brainwave(high_beta_frequency, t_shifted3, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 # Transfer mechanism: Use third layer data to influence the fourth layer transfer_factor_3_to_4 = np.mean(delta_wave) # Transfer factor based on mean value of delta wave mu_frequency = 12 + transfer_factor_3_to_4 # Adjusted Mu frequency low_gamma_frequency = 50 + transfer_factor_3_to_4 # Adjusted Low Gamma frequency mu_wave = generate_brainwave(mu_frequency, t_shifted4, phase_shift=2.0) low_gamma_wave = generate_brainwave(low_gamma_frequency, t_shifted4, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 # Mirror effect: Reflect the fourth layer's wave around the x-axis mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 # Combine data from the first four layers for the fifth layer transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 # Incorporate the transaction data into the fifth layer retained_frequency = generate_brainwave(60, t_shifted5, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t_shifted5, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 # Security layer: Delay and scramble the data for the sixth layer delay = 100 # Number of samples to delay scrambled_wave6 = scramble_data(combined_wave5, delay) # Apply the portal to the scrambled sixth layer portal_data = portal_organize(np.array([scrambled_wave6, beta_high_wave])) # Update the data of the lines lines[0].set_ydata(combined_wave1) lines[1].set_ydata(combined_wave2) lines[2].set_ydata(combined_wave3) lines[3].set_ydata(combined_mirrored_wave4) # Updated to use mirrored wave lines[4].set_ydata(combined_wave5) lines[5].set_ydata(scrambled_wave6) lines[6].set_ydata(portal_data) return lines # Define parameters duration = 5 # seconds sampling_rate = 1000 # samples per second t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False) # Initialize the plot fig, ax = plt.subplots() alpha_wave = generate_brainwave(10, t) beta_wave = generate_brainwave(20, t) financial_wave = generate_brainwave(15, t, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 theta_wave = generate_brainwave(6, t, phase_shift=0.3) gamma_wave = generate_brainwave(40, t, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 delta_wave = generate_brainwave(2, t, phase_shift=1.0) high_beta_wave = generate_brainwave(30, t, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 mu_wave = generate_brainwave(12, t, phase_shift=2.0) low_gamma_wave = generate_brainwave(50, t, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 # Apply mirror effect to the fourth layer mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 # Combine data from the first four layers for the fifth layer transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 # Incorporate the transaction data into the fifth layer retained_frequency = generate_brainwave(60, t, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 # Security layer: Delay and scramble the data for the sixth layer delay = 100 # Number of samples to delay scrambled_wave6 = scramble_data(combined_wave5, delay) portal_data = portal_organize(np.array([scrambled_wave6, beta_high_wave])) line1, = ax.plot(t, combined_wave1, label="Alpha & Beta Layer with Financial Frequencies", color='blue') line2, = ax.plot(t, combined_wave2, label="Theta & Gamma Layer (Influenced)", linestyle='--') line3, = ax.plot(t, combined_wave3, label="Delta & High Beta Layer (Transferred)", linestyle=':') line4, = ax.plot(t, combined_mirrored_wave4, label="Mu & Low Gamma Layer (Mirrored)", linestyle='-.') line5, = ax.plot(t, combined_wave5, label="Retained Frequency in Fifth Layer", linestyle='-') line6, = ax.plot(t, scrambled_wave6, label="Delayed and Scrambled Sixth Layer", linestyle='--', color='red') line7, = ax.plot(t, portal_data, label="Portal Output", linestyle=':', color='purple') ax.set_xlim(0, duration) ax.set_ylim(-2, 2) ax.set_title("6517.159.252") ax.set_xlabel("Time (s)") ax.set_ylabel("Amplitude") ax.legend() # Create the animation ani = FuncAnimation(fig, update, frames=range(200), fargs=([line1, line2, line3, line4, line5, line6, line7], t, duration, sampling_rate), blit=True) plt.show() import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from scipy.fftpack import fft, ifft def generate_brainwave(frequency, t, phase_shift=0): return np.sin(2 * np.pi * frequency * t + phase_shift) def portal_organize(frequencies): organized_data = np.mean(frequencies, axis=0) normalized_data = (organized_data - np.min(organized_data)) / (np.max(organized_data) - np.min(organized_data)) return normalized_data def scramble_data(data, delay): scrambled_data = np.roll(data, delay) scrambled_data += np.random.normal(0, 0.1, size=data.shape) return scrambled_data def encrypt_data(data): # Simulate encryption using Fourier Transform (for complexity) encrypted_data = fft(data) return encrypted_data def decrypt_data(data): # Simulate decryption using Inverse Fourier Transform decrypted_data = ifft(data) return decrypted_data.real def detect_anomalies(data): # Simple anomaly detection: Check for irregular spikes in the data anomalies = np.abs(np.diff(data)) > np.mean(np.abs(np.diff(data))) + 2 * np.std(np.abs(np.diff(data))) return anomalies def update(frame, lines, t, duration, sampling_rate): t_shifted1 = t + frame / (sampling_rate * 1.0) t_shifted2 = t + frame / (sampling_rate * 1.25) t_shifted3 = t + frame / (sampling_rate * 1.75) t_shifted4 = t + frame / (sampling_rate * 2.25) t_shifted5 = t + frame / (sampling_rate * 3.0) t_shifted6 = t + frame / (sampling_rate * 4.0) alpha_wave = generate_brainwave(10, t_shifted1) beta_wave = generate_brainwave(20, t_shifted1) financial_wave = generate_brainwave(15, t_shifted1, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 influence_factor = (financial_wave - np.mean(financial_wave)) / np.std(financial_wave) theta_frequency = 6 + influence_factor gamma_frequency = 40 + influence_factor theta_wave = generate_brainwave(theta_frequency, t_shifted2, phase_shift=0.3) gamma_wave = generate_brainwave(gamma_frequency, t_shifted2, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 transfer_factor = np.mean(theta_wave) delta_frequency = 2 + transfer_factor high_beta_frequency = 30 + transfer_factor delta_wave = generate_brainwave(delta_frequency, t_shifted3, phase_shift=1.0) high_beta_wave = generate_brainwave(high_beta_frequency, t_shifted3, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 transfer_factor_3_to_4 = np.mean(delta_wave) mu_frequency = 12 + transfer_factor_3_to_4 low_gamma_frequency = 50 + transfer_factor_3_to_4 mu_wave = generate_brainwave(mu_frequency, t_shifted4, phase_shift=2.0) low_gamma_wave = generate_brainwave(low_gamma_frequency, t_shifted4, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t_shifted5, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t_shifted5, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) # Encrypt the scrambled wave data encrypted_wave6 = encrypt_data(scrambled_wave6) # Detect any anomalies (simulating security breach detection) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): print("Security Alert: Anomalies detected in the data!") portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) lines[0].set_ydata(combined_wave1) lines[1].set_ydata(combined_wave2) lines[2].set_ydata(combined_wave3) lines[3].set_ydata(combined_mirrored_wave4) lines[4].set_ydata(combined_wave5) lines[5].set_ydata(scrambled_wave6) lines[6].set_ydata(portal_data) return lines duration = 5 sampling_rate = 1000 t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False) fig, ax = plt.subplots() alpha_wave = generate_brainwave(10, t) beta_wave = generate_brainwave(20, t) financial_wave = generate_brainwave(15, t, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 theta_wave = generate_brainwave(6, t, phase_shift=0.3) gamma_wave = generate_brainwave(40, t, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 delta_wave = generate_brainwave(2, t, phase_shift=1.0) high_beta_wave = generate_brainwave(30, t, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 mu_wave = generate_brainwave(12, t, phase_shift=2.0) low_gamma_wave = generate_brainwave(50, t, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) encrypted_wave6 = encrypt_data(scrambled_wave6) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): print("Security Alert: Anomalies detected in the data!") portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) line1, = ax.plot(t, combined_wave1, label="Alpha & Beta Layer with Financial Frequencies", color='blue') line2, = ax.plot(t, combined_wave2, label="Theta & Gamma Layer (Influenced)", linestyle='--') line3, = ax.plot(t, combined_wave3, label="Delta & High Beta Layer (Transferred)", linestyle=':') line4, = ax.plot(t, combined_mirrored_wave4, label="Mu & Low Gamma Layer (Mirrored)", linestyle='-.') line5, = ax.plot(t, combined_wave5, label="Retained Frequency in Fifth Layer", linestyle='-') line6, = ax.plot(t, scrambled_wave6, label="Delayed and Scrambled Sixth Layer", linestyle='--', color='red') line7, = ax.plot(t, portal_data, label="Portal Output", linestyle=':', color='purple') ax.set_xlim(0, duration) ax.set_ylim(-2, 2) ax.set_title("Complex Backend Security with Encrypted Waves and Anomaly Detection") ax.set_xlabel("Time (s)") ax.set_ylabel("Amplitude") ax.legend() ani = FuncAnimation(fig, update, frames=range(200), fargs=([line1, line2, line3, line4, line5, line6, line7], t, duration, sampling_rate), blit=True) plt.show() import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from scipy.fftpack import fft, ifft def generate_brainwave(frequency, t, phase_shift=0): return np.sin(2 * np.pi * frequency * t + phase_shift) def portal_organize(frequencies): organized_data = np.mean(frequencies, axis=0) normalized_data = (organized_data - np.min(organized_data)) / (np.max(organized_data) - np.min(organized_data)) return normalized_data def scramble_data(data, delay): scrambled_data = np.roll(data, delay) scrambled_data += np.random.normal(0, 0.1, size=data.shape) return scrambled_data def encrypt_data(data): encrypted_data = fft(data) return encrypted_data def decrypt_data(data): decrypted_data = ifft(data) return decrypted_data.real def detect_anomalies(data): anomalies = np.abs(np.diff(data)) > np.mean(np.abs(np.diff(data))) + 2 * np.std(np.abs(np.diff(data))) return anomalies def detect_anomalies(data): # Simple anomaly detection: Check for irregular spikes in the data anomalies = np.abs(np.diff(data)) > np.mean(np.abs(np.diff(data))) + 2 * np.std(np.abs(np.diff(data))) # Pad the anomalies array with False to match the size of the data array anomalies = np.concatenate((anomalies, [False])) return anomalies def update(frame, lines, t, duration, sampling_rate): t_shifted1 = t + frame / (sampling_rate * 1.0) t_shifted2 = t + frame / (sampling_rate * 1.25) t_shifted3 = t + frame / (sampling_rate * 1.75) t_shifted4 = t + frame / (sampling_rate * 2.25) t_shifted5 = t + frame / (sampling_rate * 3.0) t_shifted6 = t + frame / (sampling_rate * 4.0) alpha_wave = generate_brainwave(10, t_shifted1) beta_wave = generate_brainwave(20, t_shifted1) financial_wave = generate_brainwave(15, t_shifted1, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 influence_factor = (financial_wave - np.mean(financial_wave)) / np.std(financial_wave) theta_frequency = 6 + influence_factor gamma_frequency = 40 + influence_factor theta_wave = generate_brainwave(theta_frequency, t_shifted2, phase_shift=0.3) gamma_wave = generate_brainwave(gamma_frequency, t_shifted2, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 transfer_factor = np.mean(theta_wave) delta_frequency = 2 + transfer_factor high_beta_frequency = 30 + transfer_factor delta_wave = generate_brainwave(delta_frequency, t_shifted3, phase_shift=1.0) high_beta_wave = generate_brainwave(high_beta_frequency, t_shifted3, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 transfer_factor_3_to_4 = np.mean(delta_wave) mu_frequency = 12 + transfer_factor_3_to_4 low_gamma_frequency = 50 + transfer_factor_3_to_4 mu_wave = generate_brainwave(mu_frequency, t_shifted4, phase_shift=2.0) low_gamma_wave = generate_brainwave(low_gamma_frequency, t_shifted4, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t_shifted5, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t_shifted5, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) encrypted_wave6 = encrypt_data(scrambled_wave6) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): print("Security Alert: Anomalies detected in the data!") scrambled_wave6 = clear_anomalies(scrambled_wave6, anomalies) portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) lines[0].set_ydata(combined_wave1) lines[1].set_ydata(combined_wave2) lines[2].set_ydata(combined_wave3) lines[3].set_ydata(combined_mirrored_wave4) lines[4].set_ydata(combined_wave5) lines[5].set_ydata(scrambled_wave6) lines[6].set_ydata(portal_data) return lines duration = 5 sampling_rate = 1000 t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False) fig, ax = plt.subplots() alpha_wave = generate_brainwave(10, t) beta_wave = generate_brainwave(20, t) financial_wave = generate_brainwave(15, t, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 theta_wave = generate_brainwave(6, t, phase_shift=0.3) gamma_wave = generate_brainwave(40, t, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 delta_wave = generate_brainwave(2, t, phase_shift=1.0) high_beta_wave = generate_brainwave(30, t, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 mu_wave = generate_brainwave(12, t, phase_shift=2.0) low_gamma_wave = generate_brainwave(50, t, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) encrypted_wave6 = encrypt_data(scrambled_wave6) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): print("Security Alert: Anomalies detected in the data!") scrambled_wave6 = clear_anomalies(scrambled_wave6, anomalies) portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) line1, = ax.plot(t, combined_wave1, label="Alpha & Beta Layer with Financial Frequencies", color='blue') line2, = ax.plot(t, combined_wave2, label="Theta & Gamma Layer (Influenced)", linestyle='--') line3, = ax.plot(t, combined_wave3, label="Delta & High Beta Layer (Transferred)", linestyle=':') line4, = ax.plot(t, combined_mirrored_wave4, label="Mu & Low Gamma Layer (Mirrored)", linestyle='-.') line5, = ax.plot(t, combined_wave5, label="Retained Frequency in Fifth Layer", linestyle='-') line6, = ax.plot(t, scrambled_wave6, label="Delayed and Scrambled Sixth Layer", linestyle='--', color='red') line7, = ax.plot(t, portal_data, label="Portal Output", linestyle=':', color='purple') ax.set_xlim(0, duration) ax.set_ylim(-2, 2) ax.set_title("TRCSIngenuity") ax.set_xlabel("Time (s)") ax.set_ylabel("Amplitude") ax.legend() ani = FuncAnimation(fig, update, frames=range(200), fargs=([line1, line2, line3, line4, line5, line6, line7], t, duration, sampling_rate), blit=True) plt.show() import numpy as np import matplotlib.pyplot as plt from matplotlib.animation import FuncAnimation from scipy.fftpack import fft, ifft def generate_brainwave(frequency, t, phase_shift=0): return np.sin(2 * np.pi * frequency * t + phase_shift) def portal_organize(frequencies): organized_data = np.mean(frequencies, axis=0) normalized_data = (organized_data - np.min(organized_data)) / (np.max(organized_data) - np.min(organized_data)) return normalized_data def scramble_data(data, delay): scrambled_data = np.roll(data, delay) scrambled_data += np.random.normal(0, 0.1, size=data.shape) return scrambled_data def encrypt_data(data): encrypted_data = fft(data) return encrypted_data def decrypt_data(data): decrypted_data = ifft(data) return decrypted_data.real def detect_anomalies(data): # Simple anomaly detection: Check for irregular spikes in the data anomalies = np.abs(np.diff(data)) > np.mean(np.abs(np.diff(data))) + 2 * np.std(np.abs(np.diff(data))) # Pad the anomalies array with False to match the size of the data array anomalies = np.concatenate(([False], anomalies)) # prepend False instead of appending return anomalies def clear_anomalies(data, anomalies): correction_frequency = np.cos(2 * np.pi * 2 * np.arange(len(data)) / len(data)) # A low-frequency cosine wave data[anomalies] -= correction_frequency[:len(data[anomalies])] return data def update(frame, lines, t, duration, sampling_rate): t_shifted1 = t + frame / (sampling_rate * 1.0) t_shifted2 = t + frame / (sampling_rate * 1.25) t_shifted3 = t + frame / (sampling_rate * 1.75) t_shifted4 = t + frame / (sampling_rate * 2.25) t_shifted5 = t + frame / (sampling_rate * 3.0) t_shifted6 = t + frame / (sampling_rate * 4.0) alpha_wave = generate_brainwave(10, t_shifted1) beta_wave = generate_brainwave(20, t_shifted1) financial_wave = generate_brainwave(15, t_shifted1, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 influence_factor = (financial_wave - np.mean(financial_wave)) / np.std(financial_wave) theta_frequency = 6 + influence_factor gamma_frequency = 40 + influence_factor theta_wave = generate_brainwave(theta_frequency, t_shifted2, phase_shift=0.3) gamma_wave = generate_brainwave(gamma_frequency, t_shifted2, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 transfer_factor = np.mean(theta_wave) delta_frequency = 2 + transfer_factor high_beta_frequency = 30 + transfer_factor delta_wave = generate_brainwave(delta_frequency, t_shifted3, phase_shift=1.0) high_beta_wave = generate_brainwave(high_beta_frequency, t_shifted3, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 transfer_factor_3_to_4 = np.mean(delta_wave) mu_frequency = 12 + transfer_factor_3_to_4 low_gamma_frequency = 50 + transfer_factor_3_to_4 mu_wave = generate_brainwave(mu_frequency, t_shifted4, phase_shift=2.0) low_gamma_wave = generate_brainwave(low_gamma_frequency, t_shifted4, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t_shifted5, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t_shifted5, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) encrypted_wave6 = encrypt_data(scrambled_wave6) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): scrambled_wave6 = clear_anomalies(scrambled_wave6, anomalies) portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) for i, line in enumerate(lines): if i == 0: line.set_ydata(combined_wave1) elif i == 1: line.set_ydata(combined_wave2) elif i == 2: line.set_ydata(combined_wave3) elif i == 3: line.set_ydata(combined_mirrored_wave4) elif i == 4: line.set_ydata(combined_wave5) elif i == 5: line.set_ydata(scrambled_wave6) elif i == 6: line.set_ydata(portal_data) return lines duration = 5 sampling_rate = 1000 t = np.linspace(0, duration, int(sampling_rate * duration), endpoint=False) fig, axs = plt.subplots(7, 1, figsize=(10, 14), sharex=True) fig.tight_layout(pad=3.0) alpha_wave = generate_brainwave(10, t) beta_wave = generate_brainwave(20, t) financial_wave = generate_brainwave(15, t, phase_shift=0.5) combined_wave1 = (alpha_wave + beta_wave) / 2 theta_wave = generate_brainwave(6, t, phase_shift=0.3) gamma_wave = generate_brainwave(40, t, phase_shift=0.7) combined_wave2 = (theta_wave + gamma_wave) / 2 delta_wave = generate_brainwave(2, t, phase_shift=1.0) high_beta_wave = generate_brainwave(30, t, phase_shift=1.5) combined_wave3 = (delta_wave + high_beta_wave) / 2 mu_wave = generate_brainwave(12, t, phase_shift=2.0) low_gamma_wave = generate_brainwave(50, t, phase_shift=2.5) combined_wave4 = (mu_wave + low_gamma_wave) / 2 mirrored_wave4 = -combined_wave4 combined_mirrored_wave4 = (combined_wave4 + mirrored_wave4) / 2 transaction_data = (combined_wave1 + combined_wave2 + combined_wave3 + combined_mirrored_wave4) / 4 retained_frequency = generate_brainwave(60, t, phase_shift=3.0) + transaction_data beta_high_wave = generate_brainwave(70, t, phase_shift=3.5) combined_wave5 = (retained_frequency + beta_high_wave) / 2 delay = 100 scrambled_wave6 = scramble_data(combined_wave5, delay) encrypted_wave6 = encrypt_data(scrambled_wave6) anomalies = detect_anomalies(scrambled_wave6) if np.any(anomalies): scrambled_wave6 = clear_anomalies(scrambled_wave6, anomalies) portal_data = portal_organize(np.array([encrypted_wave6.real, beta_high_wave])) lines = [] for i, (wave, label, color) in enumerate([ (combined_wave1, "Alpha & Beta Layer with Financial Frequencies", 'blue'), (combined_wave2, "Theta & Gamma Layer (Influenced)", 'green'), (combined_wave3, "Delta & High Beta Layer (Transferred)", 'orange'), (combined_mirrored_wave4, "Mu & Low Gamma Layer (Mirrored)", 'purple'), (combined_wave5, "Retained Frequency in Fifth Layer", 'cyan'), (scrambled_wave6, "Delayed and Scrambled Sixth Layer", 'red'), (portal_data, "Portal Output", 'magenta') ]): line, = axs[i].plot(t, wave, color=color) axs[i].set_title(label) axs[i].set_ylim(-2, 2) lines.append(line) axs[-1].set_xlabel("Time (s)") ani = FuncAnimation(fig, update, frames=range(200), fargs=(lines, t, duration, sampling_rate), blit=True) plt.show()