wealth_anchor.py

import numpy as np
import matplotlib.pyplot as plt

# Step 1: Generate brain frequencies
def generate_brain_frequency(freqs, t):
    """Generate brain frequency as a sum of sine waves to transmit wealth signals."""
    signal = np.sum([np.sin(2 * np.pi * f * t) for f in freqs], axis=0)
    return signal

# Time variables
sampling_rate = 1000  # Samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate, endpoint=False)  # Time array

# Wealth-related brainwave frequencies (arbitrary for simulation)
brain_frequencies = [8, 13, 30]  # Frequencies representing wealth signals (theta, alpha, beta waves)
wealth_signal = generate_brain_frequency(brain_frequencies, t)

# Step 2: Transmit the wealth signals through wave patterns
def transmit_signal(signal, phase_shift):
    """Transmit wealth signal through a wave pattern with a phase shift."""
    transmitted_signal = np.sin(2 * np.pi * signal + phase_shift)
    return transmitted_signal

# Phase shift to create a unique wave pattern
phase_shift = np.pi / 4  # 45-degree phase shift

# Transmit wealth signal through the brain wave patterns
transmitted_wealth_signal = transmit_signal(wealth_signal, phase_shift)

# Step 3: Visualize the wealth signal and transmitted signal
plt.figure(figsize=(12, 6))

# Original brain-based wealth signal
plt.plot(t, wealth_signal, label='Original Brain Frequency Wealth Signal', color='blue', alpha=0.6)

# Transmitted wealth signal (wave pattern)
plt.plot(t, transmitted_wealth_signal, label='Transmitted Wealth Signal (Wave Pattern)', color='green', alpha=0.8)

plt.title('Brain Frequency Wealth Signal Transmission')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Step 1: Generate brain frequencies for wealth signals
def generate_brain_frequency(freqs, t):
    """Generate brain frequency as a sum of sine waves to transmit wealth signals."""
    signal = np.sum([np.sin(2 * np.pi * f * t) for f in freqs], axis=0)
    return signal

# Time variables
sampling_rate = 1000  # Samples per second
T = 1.0 / sampling_rate  # Sampling interval
t = np.linspace(0.0, 1.0, sampling_rate, endpoint=False)  # Time array

# Wealth-related brainwave frequencies
brain_frequencies = [8, 13, 30]  # Theta, alpha, beta waves for wealth signals
wealth_signal = generate_brain_frequency(brain_frequencies, t)

# Step 2: Transmit the wealth signals through wave patterns
def transmit_signal(signal, phase_shift):
    """Transmit wealth signal through a wave pattern with a phase shift."""
    transmitted_signal = np.sin(2 * np.pi * signal + phase_shift)
    return transmitted_signal

# Apply phase shift for signal transmission
phase_shift = np.pi / 4  # 45-degree phase shift

# Transmit wealth signal through the brain wave patterns
transmitted_wealth_signal = transmit_signal(wealth_signal, phase_shift)

# Step 3: Create a storage mechanism for the transmitted wealth signal
def store_signal(signal, storage_factor):
    """Store transmitted wealth signal by damping its amplitude for storage."""
    stored_signal = storage_factor * np.sin(2 * np.pi * signal)
    return stored_signal

# Apply a storage factor to store the wealth signal
storage_factor = 0.8  # Simulating the attenuation in storage
stored_wealth_signal = store_signal(transmitted_wealth_signal, storage_factor)

# Step 4: Visualize the wealth signal, transmitted signal, and stored signal
plt.figure(figsize=(12, 6))

# Original wealth signal
plt.plot(t, wealth_signal, label='Original Brain Frequency Wealth Signal', color='blue', alpha=0.6)

# Transmitted wealth signal
plt.plot(t, transmitted_wealth_signal, label='Transmitted Wealth Signal (Wave Pattern)', color='green', alpha=0.8)

# Stored wealth signal
plt.plot(t, stored_wealth_signal, label='Stored Wealth Signal', color='red', alpha=0.6)

plt.title('Wealth Anchor')
plt.xlabel('Time [s]')
plt.ylabel('Amplitude')
plt.legend()
plt.grid(True)
plt.show()