'vali_time_end')
self.calc_validation = True if self.vali_stime and self.vali_etime else False
if self.cali_stime >= self.cali_etime or (
self.calc_validation and self.vali_stime >= self.vali_etime):
raise ValueError("Wrong time settings in [CALI_Settings]!")
# 3. Parameters settings for specific optimization algorithm
self.opt_mtd = method
self.opt = None
if self.opt_mtd == 'nsga2':
self.opt = ParseNSGA2Config(cf, self.model.model_dir,
'CALI_NSGA2_Gen_%d_Pop_%d')
# 4. (Optional) Plot settings for matplotlib
self.plot_cfg = PlotConfig(cf)
if __name__ == '__main__':
cf, method = get_optimization_config("Execute parameters calibration.")
cfg = CaliConfig(cf, method=method)
print(cfg)
# test the picklable of SAConfig class.
import pickle
s = pickle.dumps(cfg)
# print(s)
new_cfg = pickle.loads(s)
print(new_cfg.model.model_dir)
self.initial_byinput = cf.getboolean(self.opt_mtd.upper(), 'inputpopulation') if \
cf.has_option(self.opt_mtd.upper(), 'inputpopulation') else False
self.input_pareto_file = None
self.input_pareto_gen = -1
if cf.has_option(self.opt_mtd.upper(), 'paretofrontsfile'):
self.input_pareto_file = cf.get(self.opt_mtd.upper(),
'paretofrontsfile')
if cf.has_option(self.opt_mtd.upper(), 'generationselected'):
self.input_pareto_gen = cf.getint(self.opt_mtd.upper(),
'generationselected')
self.scenario_dir = self.opt.out_dir + os.path.sep + 'Scenarios'
UtilClass.rmmkdir(self.scenario_dir)
# 5. (Optional) Plot settings for matplotlib
self.plot_cfg = PlotConfig(cf)
if __name__ == '__main__':
cf, method = get_optimization_config('Execute scenario analysis.')
cfg = SAConfig(cf, method=method) # type: SAConfig
# test the picklable of SAConfig class.
import pickle
s = pickle.dumps(cfg)
new_cfg = pickle.loads(s) # type: SAConfig
print(new_cfg.model.model_dir)
print('BMPs configuration unit %s, method %s' %
(new_cfg.bmps_cfg_unit, new_cfg.bmps_cfg_method))