def save(self, saving_method='dict', fn=None):
# log saving method
awelogger.logger.info('Saving trial ' + self.__name + ' using ' +
saving_method)
# set savefile name to trial name if unspecified
if not fn:
fn = self.__name
# choose correct function for saving method
if saving_method == 'awe':
self.save_to_awe(fn)
elif saving_method == 'dict':
self.save_to_dict(fn)
else:
awelogger.logger.error(
saving_method + ' is not a supported saving method. Trial ' +
self.__name + ' could not be saved!')
# log that save is complete
awelogger.logger.info('Trial (%s) saved.', self.__name)
awelogger.logger.info('')
awelogger.logger.info(print_op.hline('&'))
awelogger.logger.info(print_op.hline('&'))
awelogger.logger.info('')
awelogger.logger.info('')
def solve_homotopy(self, nlp, model, options, final_homotopy_step,
visualization):
logging.info('solve with homotopy procedure...')
logging.info('')
# do not consider homotopy steps after specified final_homotopy_step
final_index = self.__schedule['homotopy'].index(final_homotopy_step)
homotopy_schedule = self.__schedule['homotopy'][:final_index + 1]
self.__solve_succeeded = True
# iterate over homotopy schedule
for step_name in homotopy_schedule:
if self.__solve_succeeded:
timer = time.time()
self.solve_specific_homotopy_step(step_name, nlp, model,
options, visualization)
self.update_runtime_info(timer, step_name)
logging.info(print_op.hline('#'))
return None
def solve_with_vortex_linearization_setup(self, nlp, model, options, final_homotopy_step, visualization):
awelogger.logger.info('solve set-up problem with vortex linearization...')
awelogger.logger.info('')
self.__solve_succeeded = True
# solve set-up problem with homotopy (omitting the induction steps)
self.solve_homotopy(nlp, model, options, final_homotopy_step, visualization)
awelogger.logger.info(print_op.hline('#'))
return None
def check_display(check_condition, check_name, label_pass, label_warning):
logging.debug('')
logging.debug(print_op.hline('*'))
if check_condition:
logging.debug('{0:>15}:'.format(check_name + ' PASSED') + ' ' +
label_pass)
else:
logging.debug('{0:>15}:'.format(check_name + ' WARNING') + ' ' +
label_warning)
return None
def solve_general_homotopy_step(self, step_name, counter, options, nlp,
model, solver, visualization):
if self.__solve_succeeded:
logging.info(print_op.hline("#"))
logging.info(self.__schedule['labels'][step_name][counter])
logging.info('')
[self.__cost_update_counter, self.__p_fix_num
] = scheduling.update_cost(self.__schedule, step_name, counter,
self.__cost_update_counter,
self.__p_fix_num)
[self.__bound_update_counter, self.__V_bounds
] = scheduling.update_bounds(self.__schedule, step_name, counter,
self.__bound_update_counter,
self.__V_bounds, model, nlp)
# hand over the parameters to the solver
self.__arg['p'] = self.__p_fix_num
# bounds on x
self.__arg['ubx'] = self.__V_bounds['ub']
self.__arg['lbx'] = self.__V_bounds['lb']
# solve
self.__solution = solver(**self.__arg)
self.__stats = solver.stats()
# add up iterations of multi-step homotopies
if step_name not in list(self.__iterations.keys()):
self.__iterations[step_name] = 0.
self.__iterations[step_name] += self.__stats['iter_count']
self.generate_outputs(nlp, self.__solution)
self.allow_next_homotopy_step()
diagnostics.print_runtime_values(self.__stats)
diagnostics.print_homotopy_values(nlp, self.__solution,
self.__p_fix_num)
diagnostics.health_check(nlp, self.__solution, self.__arg, options,
self.__solve_succeeded)
if step_name in self.__debug_locations or self.__debug_locations == 'all':
V_plot = nlp.V(self.__solution['x'])
self.__make_debug_plot(V_plot, nlp, visualization, step_name)
return None
def solve_with_vortex_linearization_iterative(self, nlp, formulation, model, options, vortex_linearization_file, final_homotopy_step, visualization):
awelogger.logger.info('solve iterative problem with vortex linearization...')
awelogger.logger.info('')
self.__solve_succeeded = True
warmstart_solution_dict = save_op.extract_solution_dict_from_file(vortex_linearization_file)
self.modify_args_for_warmstart(nlp, formulation, model, options, visualization, warmstart_solution_dict=warmstart_solution_dict)
self.modify_schedule_for_vortex_linearization_iterative(final_homotopy_step, nlp, model)
# solve homotopy with warmstart
self.solve_homotopy(nlp, model, options, final_homotopy_step, visualization)
awelogger.logger.info(print_op.hline('#'))
return None
def solve_from_warmstart(self, nlp, formulation, model, options, warmstart_file, final_homotopy_step, visualization):
awelogger.logger.info('solve from warmstart...')
awelogger.logger.info('')
self.__solve_succeeded = True
warmstart_solution_dict = save_op.extract_solution_dict_from_file(warmstart_file)
self.modify_args_for_warmstart(nlp, formulation, model, options, visualization, warmstart_solution_dict = warmstart_solution_dict)
self.modify_schedule_for_warmstart(final_homotopy_step, warmstart_solution_dict, nlp, model)
# solve homotopy with warmstart
self.solve_homotopy(nlp, model, options, final_homotopy_step, visualization)
awelogger.logger.info(print_op.hline('#'))
return None
def solve_from_warmstart(self, nlp, model, options, warmstart_file,
final_homotopy_step, visualization):
logging.info('solve from warmstart...')
logging.info('')
self.__solve_succeeded = True
warmstart_trial = self.extract_warmstart_trial(warmstart_file)
self.set_warmstart_args(warmstart_trial, nlp)
self.define_warmstart_schedule(final_homotopy_step, warmstart_trial,
nlp, model)
# solve homotopy with warmstart
self.solve_homotopy(nlp, model, options, final_homotopy_step,
visualization)
logging.info(print_op.hline('#'))
return None