import time from problem_formulation import get_model_for_problem_formulation import pandas as pd import seaborn as sns from matplotlib import pyplot as plt from ema_workbench.em_framework.evaluators import LHS from ema_workbench import (Model, RealParameter, ScalarOutcome) from ema_workbench import Policy, perform_experiments from ema_workbench import ema_logging ema_logging.log_to_stderr(ema_logging.INFO) import prim if __name__ == '__main__': ema_logging.log_to_stderr(ema_logging.INFO) dike_model, planning_steps = get_model_for_problem_formulation(2) # Build a user-defined scenario and policy: reference_values = { 'Bmax': 175, 'Brate': 1.5, 'pfail': 0.5, 'ID flood wave shape': 4, 'planning steps': 2 } reference_values.update( {'discount rate {}'.format(n): 3.5 for n in planning_steps}) scen1 = {} for key in dike_model.uncertainties:
from __future__ import (unicode_literals, print_function, absolute_import, division) from ema_workbench import (Model, MultiprocessingEvaluator, Policy, Scenario) from ema_workbench.em_framework.evaluators import perform_experiments from ema_workbench.em_framework.samplers import sample_uncertainties from ema_workbench.util import ema_logging import time from problem_formulation import get_model_for_problem_formulation if __name__ == '__main__': ema_logging.log_to_stderr(ema_logging.INFO) dike_model = get_model_for_problem_formulation(0) # Build a user-defined scenario and policy: reference_values = { 'Bmax': 175, 'Brate': 1.5, 'pfail': 0.5, 'discount rate': 3.5, 'ID flood wave shape': 4 } scen1 = {} for key in dike_model.uncertainties: name_split = key.name.split('_') if len(name_split) == 1: scen1.update({key.name: reference_values[key.name]})
from __future__ import (unicode_literals, print_function, absolute_import, division) from ema_workbench import (Model, MultiprocessingEvaluator, ScalarOutcome, IntegerParameter, optimize, Scenario) from ema_workbench.em_framework.optimization import EpsilonProgress from ema_workbench.util import ema_logging from problem_formulation import get_model_for_problem_formulation import matplotlib.pyplot as plt import seaborn as sns if __name__ == '__main__': ema_logging.log_to_stderr(ema_logging.INFO) model, steps = get_model_for_problem_formulation(2) reference_values = { 'Bmax': 175, 'Brate': 1.5, 'pfail': 0.5, 'discount rate 0': 3.5, 'discount rate 1': 3.5, 'discount rate 2': 3.5, 'ID flood wave shape': 4 } scen1 = {} for key in model.uncertainties: name_split = key.name.split('_')