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('_')