/
read_simulation_data.py
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/
read_simulation_data.py
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from builtins import next
import matplotlib.pyplot as plt
import numpy as np
import itertools
def read_simulation_data(file_path_name):
variable_dictionary = {}
simulation_properties = {}
f = open(file_path_name, 'r')
line = f.readline()
while '-----------------' not in line:
line = line[0:-1]
simulation_properties['title'] = line
line = f.readline()
line = f.readline()
while '-----------------' not in line:
line = line[0:-1]
line_data = line.split(': ')
simulation_properties[line_data[0]] = line_data[1]
line = f.readline()
line = f.readline()
while '-----------------' not in line:
line = line[0:-1]
line_data = line.split(': ')
simulation_properties[line_data[0]] = float(line_data[1])
line = f.readline()
line = f.readline()
while '-----------------' not in line:
line = line[0:-1]
line_data = line.split(': ')
simulation_properties[line_data[0]] = float(line_data[1].split(' ')[0])
line = f.readline()
line = f.readline()
a_variable = None
an_uncertainty_set = None
a_method = None
while line != '':
if line[0] != '\t':
line = line[0:-2]
line_data = line.split(' = ')
a_variable = float(line_data[1])
variable_dictionary[a_variable] = {}
line = f.readline()
elif line[1] != '\t':
a_method = line[1:-2]
variable_dictionary[a_variable].update({a_method: {}})
line = f.readline()
elif line[2] != '\t':
an_uncertainty_set = line[2:-2]
variable_dictionary[a_variable][a_method].update({an_uncertainty_set: {}})
line = f.readline()
else:
line = line[3:-1]
line_data = line.split(': ')
variable_dictionary[a_variable][a_method][an_uncertainty_set].update(
{line_data[0]: float(line_data[1].split(' ')[0])})
line = f.readline()
return variable_dictionary, simulation_properties
def objective_proboffailure_vs_gamma(gammas, objective_values, objective_name, objective_units, min_obj,
max_obj, prob_of_failure, title, objective_stddev = None):
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
lines1 = ax1.plot(gammas, objective_values, 'r--', label=objective_name)
if objective_stddev:
inds = np.nonzero(np.ones(len(gammas)) - prob_of_failure)[0]
uppers = [objective_values[ind] + objective_stddev[ind] for ind in inds]
lowers = [objective_values[ind] - objective_stddev[ind] for ind in inds]
x = [gammas[ind] for ind in inds]
ax1.fill_between(x, lowers, uppers,
alpha=0.5, edgecolor='#CC4F1B', facecolor='#FF9848')
lines2 = ax2.plot(gammas, prob_of_failure, 'b-', label='Prob. of Fail.')
ax1.set_xlabel(r'Uncertainty Set Scaling Factor $\Gamma$', fontsize=16)
ax1.set_ylabel(objective_name + ' (' + objective_units.capitalize() + ')', fontsize=16)
ax2.set_ylabel("Probability of Failure", fontsize=16)
ax1.set_ylim([min_obj, max_obj])
# ax2.set_ylim([0, 1])
plt.title(title, fontsize=16)
lines = lines1 + lines2
labs = [l.get_label() for l in lines]
ax1.legend(lines, labs, loc="upper center", fontsize=16, numpoints=1)
plt.show(block=False)
def generate_comparison_plots(relative_objective_values, objective_name, relative_number_of_constraints,
relative_setup_times, relative_solve_times, uncertainty_set, methods):
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
x = np.arange(len(methods))
lines1 = ax1.bar(x + [0.2] * len(methods),
relative_objective_values,
[0.25] * len(methods), color='r', label=objective_name)
lines2 = ax2.bar(x + [0.5] * len(methods),
relative_number_of_constraints,
[0.25] * len(methods), color='b', label='No. of Cons.')
ax1.set_ylabel("Scaled Average Cost", fontsize=16)
ax1.set_ylim([min(relative_objective_values) - 0.1*min(relative_objective_values),
max(relative_objective_values) + 0.1*max(relative_objective_values)])
ax2.set_ylabel("Scaled Number of Constraints", fontsize=16)
plt.xticks(x + .35, methods)
ax1.tick_params(axis='x', which='major', labelsize=14)
plt.title(uncertainty_set.capitalize() + ' Uncertainty Set', fontsize=18)
lines = [lines1, lines2]
labs = [l.get_label() for l in lines]
leg = ax1.legend(lines, labs, loc="lower right", ncol=1)
leg.remove()
ax2.add_artist(leg)
plt.show(block=False)
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
x = np.arange(len(methods))
lines1 = ax1.bar(x + [0.2] * len(methods),
relative_setup_times,
[0.25] * len(methods), color='r', label='Setup Time')
lines2 = ax2.bar(x + [0.5] * len(methods),
relative_solve_times,
[0.25] * len(methods), color='b', label='Solve Time')
ax1.set_ylabel("Scaled Setup Time", fontsize=16)
ax2.set_ylabel("Scaled Solve Time", fontsize=16)
plt.xticks(x + .35, methods)
ax1.tick_params(axis='x', which='major', labelsize=14)
plt.title(uncertainty_set.capitalize() + ' Uncertainty Set', fontsize=18)
lines = [lines1, lines2]
labs = [l.get_label() for l in lines]
leg = ax1.legend(lines, labs, loc="lower right", ncol=1)
leg.remove()
ax2.add_artist(leg)
plt.show(block=False)
def generate_performance_vs_pwl_plots(numbers_of_linear_sections, method_performance_dictionary,
objective_name, objective_units, uncertainty_set,
worst_case_or_average):
plt.figure()
marker = itertools.cycle(('s', '*', 'o', '.', ','))
for method in method_performance_dictionary:
plt.plot(numbers_of_linear_sections, method_performance_dictionary[method], marker=next(marker),
linestyle='', label=method)
plt.xlabel("Number of Piecewise-linear Sections", fontsize=16)
plt.ylabel(objective_name + '(' + objective_units + ')', fontsize=16)
plt.title('The ' + worst_case_or_average + ' Performance: ' + uncertainty_set.capitalize() + ' Uncertainty Set',
fontsize=16)
plt.legend(loc=0, numpoints=1)
plt.show(block=False)
def generate_variable_gamma_plots(variable_gamma_file_path_name):
dictionary_gamma, properties_gamma = read_simulation_data(variable_gamma_file_path_name)
gammas = list(dictionary_gamma.keys())
gammas.sort()
methods = list(list(dictionary_gamma.values())[0].keys())
uncertainty_sets = list(list(dictionary_gamma.values())[0].values())[0].keys()
min_obj = min([dictionary_gamma[gamma][method][uncertainty_set]['Average performance']
for gamma in gammas
for method in methods
for uncertainty_set in uncertainty_sets])
max_obj = max([dictionary_gamma[gamma][method][uncertainty_set]['Average performance']
for gamma in gammas
for method in methods
for uncertainty_set in uncertainty_sets])
for uncertainty_set in uncertainty_sets:
for method in methods:
objective_values = [dictionary_gamma[gamma][method][uncertainty_set]['Average performance'] for gamma in
gammas]
prob_of_failure = [dictionary_gamma[gamma][method][uncertainty_set]['Probability of failure'] for gamma in
gammas]
objective_proboffailure_vs_gamma(gammas, objective_values, properties_gamma['Objective'],
properties_gamma['Units'], min_obj, max_obj,
prob_of_failure,
method + ' Formulation: ' + uncertainty_set.capitalize() + ' Uncertainty Set')
rel_objective_values = [
dictionary_gamma[gammas[-1]][method][uncertainty_set]['Relative average performance']
for method in methods]
rel_num_of_cons = [dictionary_gamma[gammas[-1]][method][uncertainty_set]['Relative number of constraints']
for method in methods]
rel_setup_times = [dictionary_gamma[gammas[-1]][method][uncertainty_set]['Relative setup time']
for method in methods]
rel_solve_times = [dictionary_gamma[gammas[-1]][method][uncertainty_set]['Relative solve time']
for method in methods]
generate_comparison_plots(rel_objective_values, properties_gamma['Objective'], rel_num_of_cons, rel_setup_times,
rel_solve_times, uncertainty_set, methods)
def generate_variable_pwl_plots(variable_pwl_file_path_name):
dictionary_pwl, properties_pwl = read_simulation_data(variable_pwl_file_path_name)
numbers_of_linear_sections = list(dictionary_pwl.keys())
numbers_of_linear_sections.sort()
methods = list(list(dictionary_pwl.values())[0].keys())
uncertainty_sets = list(list(dictionary_pwl.values())[0].values())[0].keys()
for uncertainty_set in uncertainty_sets:
method_average_objective_dictionary = \
{method: [dictionary_pwl[number_of_linear_sections][method][uncertainty_set]['Average performance']
for number_of_linear_sections in numbers_of_linear_sections] for method in methods}
method_worst_objective_dictionary = \
{method: [dictionary_pwl[number_of_linear_sections][method][uncertainty_set]['Worst-case performance']
for number_of_linear_sections in numbers_of_linear_sections] for method in methods}
generate_performance_vs_pwl_plots(numbers_of_linear_sections, method_average_objective_dictionary,
properties_pwl['Objective'], properties_pwl['Units'], uncertainty_set,
'Average')
generate_performance_vs_pwl_plots(numbers_of_linear_sections, method_worst_objective_dictionary,
properties_pwl['Objective'], properties_pwl['Units'], uncertainty_set,
'Worst-case')
def generate_all_plots(variable_gamma_file_path_name, variable_pwl_file_path_name):
generate_variable_gamma_plots(variable_gamma_file_path_name)
generate_variable_pwl_plots(variable_pwl_file_path_name)
if __name__ == '__main__':
pass