Upload 5733_252_159 (1).py

5733_252_159 (1).py

@@ -0,0 +1,667 @@
+# -*- coding: utf-8 -*-
+"""5733.252.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1tAXux50pAJVnm9bD6q5i20TFu4-eyI38
+"""
+
+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()