dcost = "dike cost ($)"
rcost = 'resistance cost ($)'
wcost = 'withdrawal cost ($)'
damage = "damage cost ($)"
access = "accessible to waterfront (m)"
investLim = 6e10
netCostLim = 8e10
damageLim = 8e10
#from pyborg import BorgMOEA
from rhodium import *
from rhodium.ffi import NativeModel
#from pyborg import BorgMOEA
import matplotlib.pyplot as plt
import matplotlib.pylab
model_DH = NativeModel("iCOW.dylib", "evaluateDamageOverTime")
model_DH.parameters = [
Parameter(RH, default_value=100.0, type="double"),
Parameter(RP, default_value=RPD, type="double"),
Parameter(WH, default_value=100.0, type="double"),
Parameter(DB, default_value=100.0, type="double"),
Parameter(DH, default_value=100.0, type="double"),
Parameter("years", default_value=50, type="int"),
Parameter("sampled futures", default_value=sampledFutures, type="int")
]
model_DH.responses = [
Response(totalDamage, Response.MINIMIZE, type="double", asarg=True),
Response(investmentCost, Response.MINIMIZE, type="double", asarg=True),
Response(r, Response.INFO, type="double", asarg=True),
DBAxlable = DBlabel + ' (m)'
investLim = 100000e6 / dollarUnit
netCostLim = 80000e6 / dollarUnit
damageLim = 8000e7 / dollarUnit
investLim2 = 3200e6 / dollarUnit
investLim3 = 3200e7 / dollarUnit
#from pyborg import BorgMOEA
from rhodium import *
from rhodium.ffi import NativeModel
#from pyborg import BorgMOEA
import matplotlib.pyplot as plt
import matplotlib.pylab
model_DH = NativeModel("iCOW.dylib", "evaluateDamageOverTime")
model_DH.parameters = [
Parameter(RH, default_value=100.0, type="double"),
Parameter(RP, default_value=RPD, type="double"),
Parameter(WH, default_value=100.0, type="double"),
Parameter(DB, default_value=100.0, type="double"),
Parameter(DH, default_value=100.0, type="double"),
Parameter("years", default_value=50, type="int"),
Parameter("sampled futures", default_value=sampledFutures, type="int")
]
model_DH.responses = [
Response(totalDamage, Response.MINIMIZE, type="double", asarg=True),
#Response(floodEvents, Response.INFO, type="double", asarg=True),
#Response(breechEvents, Response.INFO, type="double", asarg=True),
from rhodium import *
from rhodium.ffi import NativeModel
# Provide the DLL/SO file and the function name. The appropriate extension,
# such as .dll or .so, will be automatically added.
model = NativeModel("lake", "lake_problem")
# List the inputs. The order matters!
model.parameters = [Parameter("pollution_limit", type="double*"),
Parameter("b", default_value=0.42, type="double"),
Parameter("q", default_value=2, type="double"),
Parameter("mean", default_value=0.02, type="double"),
Parameter("stdev", default_value=0.001, type="double"),
Parameter("delta", default_value=0.98, type="double")]
# List all outputs. We use asarg=True to handle the outputs as arguments to the C
# function.
model.responses = [Response("max_P", Response.MINIMIZE, type="double", asarg=True),
Response("utility", Response.MAXIMIZE, type="double", asarg=True),
Response("inertia", Response.MAXIMIZE, type="double", asarg=True),
Response("reliability", Response.MAXIMIZE, type="double", asarg=True)]
# Specify the levers
model.levers = [RealLever("pollution_limit", 0.0, 0.1, length=100)]
# Optimize the model using Rhodium
output = optimize(model, "NSGAII", 10000)
print(output)