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simulation.py
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simulation.py
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import numpy as np
import matplotlib.pyplot as plt
import random
import math
import seaborn as sns
from matplotlib.animation import FuncAnimation
from matplotlib.colors import Normalize, PowerNorm
from matplotlib.cm import ScalarMappable
from numba import jit
import cProfile
from cooling_functions import *
from simulated_annealing import *
sns.set_style("whitegrid")
"""
This module contains the simulation and visualization functions.
The number of particles and other parameters can be set below in the main function.
"""
# Initial configuration
def initial_configuration(num_particles):
particles = []
for i in range(num_particles):
angle = np.random.uniform(0, 2 * np.pi)
radius = np.random.uniform(0.01, 0.02)
particles.append(radius * np.array([np.cos(angle), np.sin(angle)]))
return np.array(particles)
# Update plot function for animation
def update_plot(frame, particles, scat, radius, energies, cmap, norm, time_text, table, energy_line, ax_energy, display_table):
particle_positions = particles[frame]
scat.set_offsets(particle_positions)
current_energy = energies[frame]
norm = PowerNorm(gamma=0.1, vmin=min(energies), vmax=max(energies))
colors = cmap(norm(current_energy))
scat.set_color(colors)
time_text.set_text(f"Step: {frame}\nParticles: {len(particle_positions)}")
if display_table and table is not None:
polar_coords = [cartesian_to_polar(x, y) for x, y in particle_positions]
table_data = [["Particle", "Radius", "Phi"]]
for i, (r, phi) in enumerate(polar_coords):
table_data.append([f"{i+1}", f"{r:.2f}", f"{phi:.0f}°"])
for i, row in enumerate(table_data):
for j, cell in enumerate(row):
table._cells[(i, j)].get_text().set_text(cell)
energy_line.set_data(range(frame+1), energies[:frame+1])
ax_energy.set_xlim(0, frame+1)
ax_energy.relim()
ax_energy.autoscale_view()
return scat, time_text
# Simulate and visualize the animation
def simulate_and_visualize(num_particles, radius, initial_temp, cooling_function,
max_step, tolerance, max_consecutive_iterations,
cooling_parameter, boundary_condition, max_energy):
initial_particles = initial_configuration(num_particles)
best_particles, particle_history, energies = simulated_annealing(
initial_particles,
radius,
initial_temp,
cooling_function,
max_step,
tolerance,
max_consecutive_iterations,
cooling_parameter,
boundary_condition,
max_energy
)
return best_particles, particle_history, energies
def main():
boundary_condition = "circular" # "circular" or "periodic"
num_particles = 20
# Simulation parameters
radius = 1
initial_temp = 1000
final_temp = 0.001
max_step = 0.1
tolerance = 0.001
max_consecutive_iterations = 1000
# Cooling function parameters
cooling_parameter = 0.001
cooling_function = quadratic_cooling
table = None
display_table = False
max_config_particles = maximum_energy_configuration(num_particles, radius)
max_energy = calculate_energy(max_config_particles, 1)
best_particles, particle_history, energies = simulate_and_visualize(num_particles, radius,
initial_temp, cooling_function, max_step,
tolerance, max_consecutive_iterations,
cooling_parameter, boundary_condition,
max_energy)
fig = plt.figure(figsize=(15, 6))
gs = fig.add_gridspec(1, 3, width_ratios=[3, 2, 1])
ax = fig.add_subplot(gs[0])
ax_energy = fig.add_subplot(gs[1])
ax_table = fig.add_subplot(gs[2])
ax_table.axis('tight')
ax_table.axis('off')
circle = plt.Circle((0, 0), radius, color='r', fill=False)
center = plt.Circle((0, 0), 0.01, color='r', fill=True)
ax.add_artist(circle)
ax.add_artist(center)
ax.set_xlim(-radius, radius)
ax.set_ylim(-radius, radius)
ax.set_aspect('equal', adjustable='box')
scat = ax.scatter(particle_history[0][:, 0], particle_history[0][:, 1])
cmap = plt.colormaps['coolwarm']
norm = Normalize(vmin=min(energies), vmax=max(energies))
time_text = ax.text(0.02, 0.95, '', transform=ax.transAxes)
ax_energy.set_title(f"System Energy for {num_particles} Particles\nCooling Schedule:
{format_function_name(cooling_function)}\nBoundary Condition: {boundary_condition}")
ax_energy.set_xlabel("Step")
ax_energy.set_ylabel("Energy")
energy_line, = ax_energy.plot([], [], lw=2)
ax_energy.set_xlim(0, len(particle_history))
ax_energy.set_ylim(min(energies), max(energies))
ani = FuncAnimation(fig, update_plot, frames=len(particle_history),
fargs=(particle_history, scat, radius, energies, cmap, norm, time_text, table, energy_line, ax_energy, display_table),
interval=10, repeat=False)
plt.show()
if __name__ == "__main__":
cProfile.run('main()', 'profile_stats.prof')