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plotting.py
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plotting.py
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# plotting.py
import matplotlib.pyplot as plt
import numpy as np
from math_functions import kalman_filter, gaussian_function
def plot_best_cost_progress(temperature_progress, best_cost_progress_list, squared_best_cost, slow_time_list, plot_average=False, C_func = False):
if plot_average:
fig = plt.figure(figsize=(10, 5))
ax1 = fig.add_subplot(1, 2, 1)
line_style = '--'
color = 'black'
line_width = 1
label = None
# Calculate the average best cost for each temperature progress
average_best_cost = np.mean(best_cost_progress_list, axis=0)
for best_cost in best_cost_progress_list:
ax1.plot(temperature_progress,best_cost, line_style, color = color,linewidth=line_width, label = label)
ax1.plot(temperature_progress, average_best_cost, 'r-', linewidth=3, label='Average C$_{total}$')
ax1.legend(loc='best')
# Add a legend for the dashed lines
ax1.plot([], [], 'k--', label='Single solution (one at a time)')
ax1.legend(loc='best')
ax1.set_xlabel('Temperature')
ax1.set_ylabel('C$_{total}$', fontsize=12)
ax1.legend()
ax1.grid(True)
# Set the x-axis and y-axis to logarithmic scale
ax1.set_xscale('log')
smoothed_average_best_cost = kalman_filter(average_best_cost, process_variance=1e-5, measurement_variance=0.1)
# Calculate the derivative of the smoothed average best cost
derivative_average_best_cost = np.gradient(smoothed_average_best_cost, np.log(temperature_progress))
# Plot the derivative of the smoothed average best cost
ax2 = fig.add_subplot(1, 2, 2)
ax2.plot(np.exp(temperature_progress)[30:], derivative_average_best_cost[30:], 'g-', linewidth=2, label='Derivative of Smoothed Average Best Cost')
ax2.set_xlabel('Temperature')
ax2.set_ylabel('Derivative')
ax2.set_title('Derivative of Smoothed Average Best Cost')
ax2.legend()
ax2.grid(True)
ax2.set_xscale('log')
ax2.set_xlim(np.exp(-15),np.exp(20))
ax2.set_ylim(0,2000)
plt.show()
else:
fig = plt.figure(figsize=(15, 5))
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)
for i, (slow_time, best_cost_progress, squared_progress) in enumerate(zip(slow_time_list, best_cost_progress_list,squared_best_cost)):
line_style = '-'
color = f'C{i}' # Cycle through default colors
line_width = 2
label = f'initial velocity ={slow_time}'
ax1.plot(temperature_progress, best_cost_progress, linestyle=line_style, color=color, linewidth=line_width, label=label)
#ax1.set_title('Best Cost Progress over Temperature for Different Slowdown Factors')
ax1.set_xlabel('Temperature')
ax1.set_ylabel('C$_{total}$', fontsize=12)
ax1.set_xscale('log')
ax1.legend()
ax1.grid(True)
ax1.set_xlim(1e-5,1e6)
smoothed_average_best_cost = kalman_filter(best_cost_progress, process_variance=1e-5, measurement_variance=0.1)
derivative_average_best_cost = np.gradient(smoothed_average_best_cost, np.log(temperature_progress))
ax2.plot((temperature_progress)[30:], derivative_average_best_cost[30:], linestyle=line_style, color=color, linewidth=line_width, label=label)
ax2.set_xlabel('Temperature')
ax2.set_ylabel('Derivative')
#ax2.set_title('Derivative of Smoothed Average Best Cost')
ax2.legend()
ax2.grid(True)
ax2.set_xscale('log')
ax2.set_xlim(1e-5,1e6)
ax2.set_ylim(0,2000)
variance = kalman_filter(squared_progress - np.square(best_cost_progress), process_variance=0.01, measurement_variance=0.1)#/np.square(temperature_progress)
ax3.plot(temperature_progress, variance, linestyle=line_style, color=color, linewidth=line_width, label=label)
#ax3.set_title('Variance')
ax3.set_xlabel('Temperature')
ax3.set_ylabel('Variance')
plt.legend()
plt.grid(True)
ax3.set_xscale('log')
ax3.set_xlim(1e-5,1e6)
plt.show()
def plot_average_best_cost_derivative(temperature_progress, average_best_cost, process_variance=0.01, measurement_variance=0.1):
# Apply Kalman filter to smooth the data
smoothed_average_best_cost = kalman_filter(average_best_cost, process_variance, measurement_variance)
# Calculate the derivative of the smoothed average best cost
derivative_average_best_cost = np.gradient(smoothed_average_best_cost, temperature_progress)
# Plot the derivative of the smoothed average best cost
plt.figure(figsize=(10, 5))
plt.plot(np.exp(temperature_progress)[30:], derivative_average_best_cost[30:], 'g-', linewidth=2, label='Derivative of Smoothed Average Best Cost')
plt.xlabel('Temperature Progress')
plt.ylabel('Derivative')
plt.title('Derivative of Smoothed Average Best Cost')
plt.legend()
plt.grid(True)
plt.xscale('log')
plt.xlim(np.exp(-15),np.exp(20))
plt.ylim(0,2000)
plt.show()
def plot_cities_and_tour(cities, tour):
x_coords = [city.x for city in cities]
y_coords = [city.y for city in cities]
plt.scatter(x_coords, y_coords, color='blue')
# Connect the cities in the tour with lines
for i in range(len(tour)):
current_city = tour[i]
next_city = tour[(i + 1) % len(tour)]
plt.plot([current_city.x, next_city.x], [current_city.y, next_city.y], color='red')
plt.title('Cities and Tour')
plt.xlabel('X Coordinate')
plt.ylabel('Y Coordinate')
plt.show()
def plot_gaussian_function(start=0, end=24, num_points=1000):
# Generate x values
x = np.linspace(start, end, num_points)
# Calculate y values
y = gaussian_function(x)
# Plotting
plt.figure(figsize=(10, 5))
plt.plot(x, y, label='Trafic function')
plt.xlabel('Time, hours')
plt.ylabel('η$_{time}$', fontsize=12)
plt.title('η$_{time}$ over time')
plt.legend()
plt.grid(True)
plt.xlim(0,24)
plt.show()
def plot_weight_over_time(salesman):
current_weight = salesman.total_weight
current_time_copy = salesman.current_time
total_travel_time = 0.0
time_array = [current_weight]
weight_array = [current_time_copy]
fuel_wasted = 0.0
route = salesman.optimal_route
for i in range(len(route)):
city = route[i]
next_city = route[(i + 1) % salesman.num_cities]
total_travel_time += salesman.calculate_travel_time(city, next_city, salesman.calculate_slowdown_factor(current_time_copy))
current_time_copy += total_travel_time
current_time_copy = current_time_copy % 24.0
current_weight -= next_city.package_weight
time_array.append(current_time_copy)
weight_array.append(current_weight)
fuel_wasted += salesman.calculate_fuel_wasted(city, next_city)
plt.plot(time_array,weight_array)
#plot_gaussian_function()
def plot_salaries(temperature_progress, total_cost_list, salary_list, gas_list, slow_time_list):
fig = plt.figure(figsize=(15, 5))
ax1 = fig.add_subplot(1, 3, 1)
ax2 = fig.add_subplot(1, 3, 2)
ax3 = fig.add_subplot(1, 3, 3)
for i, (slow_time, best_cost_progress, salary, gas) in enumerate(zip(slow_time_list, total_cost_list, salary_list, gas_list)):
line_style = '-'
color = f'C{i}' # Cycle through default colors
line_width = 2
label = f'starting_time ={slow_time}'
ax1.plot(temperature_progress, best_cost_progress, linestyle=line_style, color=color, linewidth=line_width, label=label)
ax1.plot(temperature_progress, salary+gas, linestyle=line_style, color=color, linewidth=line_width, label=None)
#ax1.set_title('Best Cost Progress over Temperature for Different Slowdown Factors')
ax1.set_xlabel('Temperature')
ax1.set_ylabel('C$_{total}$', fontsize=12)
ax1.set_xscale('log')
ax1.legend()
ax1.grid(True)
ax1.set_xlim(1e-5,1e6)
ax2.plot(temperature_progress, salary, linestyle=line_style, color=color, linewidth=line_width, label=label)
#ax1.set_title('Best Cost Progress over Temperature for Different Slowdown Factors')
ax2.set_xlabel('Temperature')
ax2.set_ylabel('Salary', fontsize=12)
ax2.set_xscale('log')
ax2.legend()
ax2.grid(True)
ax2.set_xlim(1e-5,1e6)
ax3.plot(temperature_progress, gas, linestyle=line_style, color=color, linewidth=line_width, label=label)
#ax1.set_title('Best Cost Progress over Temperature for Different Slowdown Factors')
ax3.set_xlabel('Temperature')
ax3.set_ylabel('Gas', fontsize=12)
ax3.set_xscale('log')
ax3.legend()
ax3.grid(True)
ax3.set_xlim(1e-5,1e6)
plt.show()
def plot_over_param(dif_parameters, average_best_cost_resid):
fig = plt.figure(figsize=(10, 5))
plt.plot(dif_parameters, average_best_cost_resid)
#ax1.set_title('Best Cost Progress over Temperature for Different Slowdown Factors')
plt.xlabel('Starting time, Hours')
plt.ylabel('C$_{total}$', fontsize=12)
plt.grid(True)
plt.xlim(0,24)