}]
uncertainty_sets = ['ellipsoidal']
# Loading pof results
gamma = {}
gamma['solutions'] = {}
for i in range(nGammas):
for j in range(len(methods)):
for k in range((len(uncertainty_sets))):
gamma['solutions'][gammas[i], methods[j]['name'],
uncertainty_sets[k]] = pickle.load(
open(
"gammaResults\\" + str(
(gammas[i], methods[j]['name'],
uncertainty_sets[k])), 'rb'))
gamma['solve_times'] = load_obj('gammasolve_times', 'gammaResults')
gamma['simulation_results'] = load_obj('gammasimulation_results',
'gammaResults')
gamma['number_of_constraints'] = load_obj('gammanumber_of_constraints',
'gammaResults')
# Loading directly_uncertain_vars_subs
try:
pickle_in = open('directly_uncertain_dict.pickle', 'rb')
directly_uncertain_vars_subs = pickle.load(pickle_in)
pickle_in.close()
except:
print(
'Warning: Please run pof_simulate first for consistent MC results.'
)
min_num_of_linear_sections, max_num_of_linear_sections, verbosity,
linearization_tolerance, number_of_time_average_solves,
uncertainty_sets, nominal_solution, directly_uncertain_vars_subs,
parallel, nominal_number_of_constraints, nominal_solve_time
] = pof_parameters()
# # Loading margins results
margin = {}
nmargins = len(gammas)
margins = np.linspace(0., 1., nmargins)
margin['solutions'] = {}
for i in range(nmargins):
margin['solutions'][margins[i]] = pickle.load(
open("marginResults/" + str(margins[i]), 'rb'))
margin['number_of_constraints'] = load_obj('marginnumber_of_constraints',
'marginResults')
margin['simulation_results'] = load_obj('marginsimulation_results',
'marginResults')
# Plotting of cost and probability of failure
objective_name = 'Total fuel weight'
objective_units = 'N'
title = ''
filteredResult = margin['solutions']
filteredSimulations = margin['simulation_results']
objective_varkey = 'W_{f_m}'
objective_costs = []
pofs = []
objective_stddev = []
for i in sorted(filteredResult.keys()):
uncertainty_sets, nominal_solution, directly_uncertain_vars_subs,
parallel, nominal_number_of_constraints, nominal_solve_time
] = pof_parameters()
# Restricting method and uncertainty set to Best Pairs, ellipsoidal for demonstration
methods = [{
'name': 'Best Pairs',
'twoTerm': True,
'boyd': False,
'simpleModel': False
}]
uncertainty_sets = ['ellipsoidal']
# # Loading goal programming results
delta = {}
deltas = load_obj('deltas' + uncertainty_sets[0], 'goalResults')
ndeltas = len(deltas)
delta['solutions'] = {}
for i in range(ndeltas):
delta['solutions'][deltas[i], methods[0]['name'],
uncertainty_sets[0]] = pickle.load(
open(
"goalResults/" + str(
(deltas[i], methods[0]['name'],
uncertainty_sets[0])), 'rb'))
delta['solve_times'] = load_obj('deltasolve_times' + uncertainty_sets[0],
'goalResults')
delta['simulation_results'] = load_obj(
'deltasimulation_results' + uncertainty_sets[0], 'goalResults')
delta['number_of_constraints'] = load_obj(
'deltanumber_of_constraints' + uncertainty_sets[0], 'goalResults')