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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() |