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Author: MuonRay

qoppav4.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 31 17:15:19 2024
+
+@author: ektop
+"""
+
+import networkx as nx
+import numpy as np
+from random import uniform
+from math import pi
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+alpha = 1  # coupling strength
+Dt = 0.01  # Delta t
+m = 1  # Assume mass as 1 for simplicity
+
+def initialize():
+    global g, nextg
+    g = nx.karate_club_graph()
+    for i in list(g.nodes()):
+        g.node[i]['theta'] = 2 * pi * np.random.random()
+        g.node[i]['omega'] = 1. + uniform(-0.05, 0.05)
+    nextg = g.copy()
+
+def observe():
+    global g
+    plt.clf()
+    nx.draw(g, cmap=plt.cm.hsv, vmin=-1, vmax=1,
+            node_color=[np.sin(g.node[i]['theta']) for i in list(g.nodes())],
+            pos=nx.spring_layout(g))
+    plt.title('Network Visualization')
+    plt.show()
+
+def gauss_mouse_map(phase):
+    return np.sin(phase)
+
+def update():
+    global g, nextg, chaotic_numbers_data, timestamps, frequency_shifts, action_derivative_values
+    chaotic_numbers = []
+    angular_accelerations = np.zeros(len(g.nodes()))
+    action_derivative = 0  # Initialize action derivative for this timestep
+    previous_angular_velocities = np.array([g.node[i]['omega'] for i in g.nodes()])
+
+    for i in list(g.nodes()):
+        theta_i = g.node[i]['theta']
+        omega_i = g.node[i]['omega']
+        
+        # Update angular position using Euler's method
+        nextg.node[i]['theta'] = theta_i + omega_i * Dt + (alpha * (
+                np.sum(np.sin(g.node[j]['theta'] - theta_i) for j in g.neighbors(i))
+                / g.degree(i))) * Dt
+        
+        angular_accelerations[i] = (nextg.node[i]['theta'] - theta_i) / Dt
+        
+        chaotic_number = gauss_mouse_map(g.node[i]['theta'])
+        chaotic_numbers.append(chaotic_number)
+
+    # Compute derivative of action
+    for i in range(len(g.nodes())):
+        action_derivative += 0.5 * m * previous_angular_velocities[i] * angular_accelerations[i]
+
+    action_derivative_values.append(action_derivative)  # Store action derivative over time
+        # Calculate frequency shifts
+    if len(chaotic_numbers_data) > 0:
+        previous_chaotic_numbers = chaotic_numbers_data[-1]
+        frequency_shift = [chaotic_numbers[j] - previous_chaotic_numbers[j] for j in range(len(chaotic_numbers))]
+        frequency_shifts.append(np.mean(frequency_shift))  # Store the average frequency shift over time
+    else:
+        frequency_shifts.append(0)  # No shift initially
+    
+
+    # Update the states
+    g, nextg = nextg, g
+    chaotic_numbers_data.append(chaotic_numbers)
+    timestamps.append(len(chaotic_numbers_data))
+
+def initialize_and_update():
+    initialize()
+    update()
+
+import pycxsimulator
+
+# Initialize lists to store data
+chaotic_numbers_data = []
+frequency_shifts = []
+action_derivative_values = []  # List to store action derivatives over time
+timestamps = []  # Initialize timestamps
+
+# Run the simulation
+pycxsimulator.GUI().start(func=[initialize, observe, update])
+
+
+# Create scatter plot of chaotic number values vs timestamps
+plt.figure(figsize=(12, 5))
+for i, chaotic_numbers in enumerate(chaotic_numbers_data):
+    colors = ['r' if num >= 0 else 'b' for num in chaotic_numbers]
+    plt.scatter([timestamps[i]] * len(chaotic_numbers), chaotic_numbers, color=colors, alpha=0.5)
+
+plt.xlabel('Timestamp')
+plt.ylabel('Chaotic Number Value')
+plt.title('Scatter Plot of Chaotic Number Values vs Timestamp')
+plt.show()
+
+
+# Create scatter plot of frequency shifts
+plt.figure(figsize=(12, 5))
+for i, shift in enumerate(frequency_shifts):
+    plt.scatter(timestamps[i], shift, color='g', alpha=0.5)
+
+plt.xlabel('Timestamp')
+plt.ylabel('Average Frequency Shift')
+plt.title('Scatter Plot of Frequency Shifts vs Timestamp')
+plt.show()
+
+
+# New: Plot action derivatives over time
+plt.figure(figsize=(12, 5))
+plt.plot(timestamps, action_derivative_values, marker='o', linestyle='-')
+plt.title('Action Derivative over Time')
+plt.xlabel('Timestamp')
+plt.ylabel('Action Derivative')
+plt.grid()
+plt.show()
+
+# New: Create scatter plot of action derivative vs chaotic numbers
+plt.figure(figsize=(12, 5))
+for i, chaotic_numbers in enumerate(chaotic_numbers_data):
+    for j in range(len(chaotic_numbers)):
+        plt.scatter(action_derivative_values[i], chaotic_numbers[j], color='red', alpha=0.5)
+
+plt.title('Chaotic Numbers vs Action Derivative')
+plt.xlabel('Action Derivative')
+plt.ylabel('Chaotic Number Value')
+plt.grid()
+plt.show()
+
+
+# New: Plot frequency shifts vs actions
+plt.figure(figsize=(12, 5))
+plt.scatter(action_derivative_values, frequency_shifts, color='orange', alpha=0.5)
+plt.title('Frequency Shifts vs Action Derivative')
+plt.xlabel('Action Derivative')
+plt.ylabel('Average Frequency Shift')
+plt.grid()
+plt.show()
+
+
+
+
+
+# Assuming you have the following variables defined
+# timestamps, action_derivative_values, frequency_shifts
+
+# Create a figure
+fig = plt.figure(figsize=(12, 8))
+
+# Create a 3D scatter plot
+ax = fig.add_subplot(111, projection='3d')
+
+# Create a scatter plot, using timestamps, action derivatives, and chaotic numbers
+scatter = ax.scatter(timestamps, action_derivative_values, frequency_shifts, 
+                     c=action_derivative_values, cmap='viridis', alpha=0.5)
+
+# Add titles and labels
+ax.set_title('3D Visualization of Action Derivative, Frequency Shift, va Timestamps')
+ax.set_xlabel('Timestamp')
+ax.set_ylabel('Action Derivative')
+ax.set_zlabel('Frequency Shift Value')
+
+# Show color bar for reference
+plt.colorbar(scatter, label='Action Derivative')
+
+# Show the plot
+plt.show()
+
+
+
+