Upload atmosecure.py

atmosecure.py

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+# -*- coding: utf-8 -*-
+"""Atmosecure
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1se4gbirQOBqRsx5WRQjtWBT8mqW0457o
+"""
+
+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)