atmosecure.py

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Define a neural network to simulate signal transmission through nerves
class WealthSignalNerveNet(nn.Module):
    def __init__(self, input_size=1, hidden_size=64, output_size=1):
        super(WealthSignalNerveNet, self).__init__()
        # Simulating the nerve layers (hidden layers)
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()  # Activation to simulate signal flow

    def forward(self, x):
        x = self.relu(self.fc1(x))  # First layer simulating the first nerve
        x = self.relu(self.fc2(x))  # Second nerve layer
        x = self.fc3(x)  # Final output layer representing the output of the signal through the nerves
        return x

# Function to generate wealth signals using a sine wave
def generate_wealth_signal(iterations=100):
    time = np.linspace(0, 10, iterations)
    wealth_signal = np.sin(2 * np.pi * time)  # Simple sine wave representing wealth
    return wealth_signal

# Function to transmit wealth signal through the nerve network
def transmit_wealth_signal(wealth_signal, model):
    transmitted_signals = []
    for wealth in wealth_signal:
        wealth_tensor = torch.tensor([wealth], dtype=torch.float32)  # Convert wealth signal to tensor
        transmitted_signal = model(wealth_tensor)  # Pass through nerve model
        transmitted_signals.append(transmitted_signal.item())
    return transmitted_signals

# Function to visualize the wealth signal transmission
def plot_wealth_signal(original_signal, transmitted_signal):
    plt.figure(figsize=(10, 5))
    plt.plot(original_signal, label="Original Wealth Signal", color='g', linestyle='--')
    plt.plot(transmitted_signal, label="Transmitted Wealth Signal", color='b')
    plt.title("Wealth Signal Transmission Through Nerves")
    plt.xlabel("Iterations (Time)")
    plt.ylabel("Signal Amplitude")
    plt.legend()
    plt.grid(True)
    plt.show()

# Initialize the neural network simulating the nerves
model = WealthSignalNerveNet()

# Generate a wealth signal
iterations = 100
wealth_signal = generate_wealth_signal(iterations)

# Transmit the wealth signal through the nerve model
transmitted_signal = transmit_wealth_signal(wealth_signal, model)

# Visualize the original and transmitted wealth signals
plot_wealth_signal(wealth_signal, transmitted_signal)

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Define a neural network to simulate signal transmission and storage
class WealthSignalStorageNet(nn.Module):
    def __init__(self, input_size=1, hidden_size=64, output_size=1):
        super(WealthSignalStorageNet, self).__init__()
        # Layers for transmitting and storing the signal
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.fc4 = nn.Linear(output_size, output_size)  # Additional layer for positive energy transformation
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()  # Sigmoid to ensure positive energy output

    def forward(self, x):
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        x = self.fc4(x)  # Store signal and transform
        x = self.sigmoid(x)  # Convert to positive energy
        return x

# Function to generate wealth signals using a sine wave
def generate_wealth_signal(iterations=100):
    time = np.linspace(0, 10, iterations)
    wealth_signal = np.sin(2 * np.pi * time)  # Simple sine wave representing wealth
    return wealth_signal

# Function to transmit and transform wealth signal through the network
def process_wealth_signal(wealth_signal, model):
    processed_signals = []
    for wealth in wealth_signal:
        wealth_tensor = torch.tensor([wealth], dtype=torch.float32)  # Convert wealth signal to tensor
        processed_signal = model(wealth_tensor)  # Pass through network
        processed_signals.append(processed_signal.item())
    return processed_signals

# Function to visualize the wealth signal transformation
def plot_signal_transformation(original_signal, transformed_signal):
    plt.figure(figsize=(10, 5))
    plt.plot(original_signal, label="Original Wealth Signal", color='g', linestyle='--')
    plt.plot(transformed_signal, label="Positive Energy Signal", color='r')
    plt.title("Wealth Signal Storage and Transformation to Positive Energy")
    plt.xlabel("Iterations (Time)")
    plt.ylabel("Signal Amplitude")
    plt.legend()
    plt.grid(True)
    plt.show()

# Initialize the neural network for signal processing
model = WealthSignalStorageNet()

# Generate a wealth signal
iterations = 100
wealth_signal = generate_wealth_signal(iterations)

# Process the wealth signal through the network
positive_energy_signal = process_wealth_signal(wealth_signal, model)

# Visualize the original wealth signal and the positive energy signal
plot_signal_transformation(wealth_signal, positive_energy_signal)

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Define a neural network to simulate nerve transmission
class WealthSignalNerveNet(nn.Module):
    def __init__(self, input_size=1, hidden_size=64, output_size=1):
        super(WealthSignalNerveNet, self).__init__()
        # Layers to simulate nerve transmission
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, output_size)
        self.relu = nn.ReLU()  # Activation function to simulate signal processing

    def forward(self, x):
        x = self.relu(self.fc1(x))  # First nerve layer
        x = self.relu(self.fc2(x))  # Second nerve layer
        x = self.fc3(x)  # Output layer
        return x

# Function to generate a wealth signal using a sine wave
def generate_wealth_signal(iterations=100):
    time = np.linspace(0, 10, iterations)
    wealth_signal = np.sin(2 * np.pi * time)  # Simple sine wave to represent wealth
    return wealth_signal

# Function to simulate transmission of wealth signal through the nerve network
def transmit_signal(wealth_signal, model):
    transmitted_signals = []
    for wealth in wealth_signal:
        wealth_tensor = torch.tensor([wealth], dtype=torch.float32)  # Convert to tensor
        transmitted_signal = model(wealth_tensor)  # Pass through the neural network
        transmitted_signals.append(transmitted_signal.item())
    return transmitted_signals

# Function to visualize the wealth signal transmission
def plot_signal_transmission(original_signal, transmitted_signal):
    plt.figure(figsize=(12, 6))
    plt.plot(original_signal, label="Original Wealth Signal", color='g', linestyle='--')
    plt.plot(transmitted_signal, label="Transmitted Wealth Signal", color='b')
    plt.title("Transmission of Wealth Signal Through Nerves")
    plt.xlabel("Iterations (Time)")
    plt.ylabel("Signal Amplitude")
    plt.legend()
    plt.grid(True)
    plt.show()

# Initialize the neural network
model = WealthSignalNerveNet()

# Generate a wealth signal
iterations = 100
wealth_signal = generate_wealth_signal(iterations)

# Transmit the wealth signal through the neural network
transmitted_signal = transmit_signal(wealth_signal, model)

# Visualize the original and transmitted signals
plot_signal_transmission(wealth_signal, transmitted_signal)

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Define a neural network to simulate encryption, storage, and transmission through atmospheric density
class AdvancedWealthSignalNet(nn.Module):
    def __init__(self, input_size=1, hidden_size=64, output_size=1):
        super(AdvancedWealthSignalNet, self).__init__()
        # Layers to simulate signal encryption, storage, and atmospheric effects
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, hidden_size)
        self.fc3 = nn.Linear(hidden_size, hidden_size)
        self.fc4 = nn.Linear(hidden_size, output_size)
        self.fc5 = nn.Linear(output_size, output_size)
        self.relu = nn.ReLU()
        self.sigmoid = nn.Sigmoid()
        self.noise_std = 0.1  # Standard deviation for noise simulation

    def forward(self, x):
        x = self.relu(self.fc1(x))  # Encryption simulation
        x = self.relu(self.fc2(x))  # Intermediate storage
        x = self.relu(self.fc3(x))  # Further storage
        x = self.fc4(x)  # Simulate transmission through dense medium
        x = self.fc5(x)  # Final transformation
        x = self.sigmoid(x)  # Ensure positive output

        # Simulate atmospheric noise
        noise = torch.normal(mean=0, std=self.noise_std, size=x.size())
        x = x + noise
        return x

# Function to generate a wealth signal using a sine wave
def generate_wealth_signal(iterations=100):
    time = np.linspace(0, 10, iterations)
    wealth_signal = np.sin(2 * np.pi * time)  # Simple sine wave to represent wealth
    return wealth_signal

# Function to process and protect the wealth signal through the network
def process_and_protect_signal(wealth_signal, model):
    processed_signals = []
    for wealth in wealth_signal:
        wealth_tensor = torch.tensor([wealth], dtype=torch.float32)  # Convert to tensor
        protected_signal = model(wealth_tensor)  # Pass through the network
        processed_signals.append(protected_signal.item())
    return processed_signals

# Function to visualize the wealth signal with protection and atmospheric effects
def plot_signal_protection_and_atmospheric_effects(original_signal, processed_signal):
    plt.figure(figsize=(12, 6))
    plt.plot(original_signal, label="Wealth Signal", color='g', linestyle='--')
    plt.plot(processed_signal, label="Protected", color='r')
    plt.title("Atmosecure")
    plt.xlabel("Iterations (Time)")
    plt.ylabel("Signal Amplitude")
    plt.legend()
    plt.grid(True)
    plt.show()

# Initialize the neural network for advanced signal processing and protection
model = AdvancedWealthSignalNet()

# Generate a wealth signal
iterations = 100
wealth_signal = generate_wealth_signal(iterations)

# Process and protect the wealth signal through the network
protected_signal = process_and_protect_signal(wealth_signal, model)

# Visualize the original and protected signals
plot_signal_protection_and_atmospheric_effects(wealth_signal, protected_signal)