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#!/usr/bin/python3
# Copyright (C) 2016 EDF
# All Rights Reserved
# This code is published under the GNU Lesser General Public License (GNU LGPL)
import numpy as np
import math
import StOptReg as reg
import StOptGrids
import StOptGlobal
import Simulators as sim
import Optimizers as opt
import Utils
import dp.DynamicProgrammingByRegressionDist as dynmpi
import dp.SimulateRegressionControlDist as srtmpi
import unittest
import importlib
accuracyClose = 1e5
class testGasStorageSwitchCostMpiHighLevelTest(unittest.TestCase):
def testGasStorageSwitchCostMpiHighLevel(self):
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
world = MPI.COMM_WORLD
# storage
###############
maxLevelStorage = 360000.
injectionRateStorage = 60000.
withdrawalRateStorage = 45000.
injectionCostStorage = 0.35
withdrawalCostStorage = 0.35
switchingCostStorage = 4.
maturity = 1.
nstep = 10
# define a a time grid
timeGrid = StOptGrids.OneDimRegularSpaceGrid(0., maturity / nstep, nstep)
futValues = []
# periodicity factor
iPeriod = 52
for i in range(nstep + 1):
futValues.append(50. + 20 * math.sin((math.pi * i * iPeriod) / nstep))
# define the future curve
futureGrid = Utils.FutureCurve(timeGrid, futValues)
# one dimensional factors
nDim = 1
sigma = np.zeros(nDim) + 0.94
mr = np.zeros(nDim) + 0.29
# number of simulations
nbsimulOpt = 20000
# grid
#####
nGrid = 40
lowValues = np.zeros(1, dtype = float)
step = np.zeros(1, dtype = float) + maxLevelStorage / nGrid
nbStep = np.zeros(1, dtype = np.int32) + nGrid
grid = StOptGrids.RegularSpaceGrid(lowValues, step, nbStep)
# no actualization
rate=0.
# a backward simulator
######################
bForward = False
backSimulator = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate, maturity, nstep, nbsimulOpt, bForward)
# optimizer
############
storage = opt.OptimizeGasStorageSwitchingCostMeanReverting(injectionRateStorage, withdrawalRateStorage, injectionCostStorage, withdrawalCostStorage, switchingCostStorage)
# regressor
##########
nMesh = 4
nbMesh = np.zeros(1, dtype = np.int32) + nMesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = Utils.ZeroPayOff()
# initial values
initialStock = np.zeros(1) + maxLevelStorage
initialRegime = 0 # here do nothing (no injection, no withdrawal)
# Optimize
###########
fileToDump = "CondExpGasSwiCostHLMpi"
bOneFile = True
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptimMpi = dynmpi.DynamicProgrammingByRegressionDist(grid, storage, regressor, vFunction, initialStock, initialRegime, fileToDump, bOneFile)
print("valOP", valueOptimMpi)
world.barrier()
nbsimulSim = 40000
bForward = True
forSimulator = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate,maturity, nstep, nbsimulSim, bForward)
storage.setSimulator(forSimulator)
valSimuMpi = srtmpi.SimulateRegressionControlDist(grid, storage, vFunction, initialStock, initialRegime, fileToDump, bOneFile)
print("valSimuMpi", valSimuMpi)
if world.rank == 0:
self.assertAlmostEqual(valueOptimMpi, valSimuMpi, None, "Re-adjust tolerance edge please", accuracyClose)
print("Optim", valueOptimMpi, "valSimuMpi", valSimuMpi)
return valueOptimMpi
def test_switchingVaryingRegimeStorageMpi(self):
moduleMpi4Py=importlib.util.find_spec('mpi4py')
if (moduleMpi4Py is not None):
from mpi4py import MPI
world = MPI.COMM_WORLD
# storage
###############
maxLevelStorage = 360000.
injectionRateStorage = 60000.
withdrawalRateStorage = 45000.
injectionCostStorage = 0.35
withdrawalCostStorage = 0.35
switchingCostStorage = 4.
maturity = 1.
nstep = 10
# define a a time grid
timeGrid = StOptGrids.OneDimRegularSpaceGrid(0., maturity / nstep, nstep)
futValues = []
# periodicity factor
iPeriod = 52
for i in range(nstep + 1):
futValues.append(50. + 20 * math.sin((math.pi * i * iPeriod) / nstep))
# define the future curve
futureGrid = Utils.FutureCurve(timeGrid, futValues)
# regime values allowed
#######################
tvalues = np.zeros(6)
tvalues[0] = 0.
tvalues[1] = 1e-3
tvalues[2] = 1. / 4 + 1e-3
tvalues[3] = 1. / 2 + 1e-3
tvalues[4] = 3. / 4. + 1e-3
tvalues[5] = 1.
timeRegimes = StOptGrids.OneDimSpaceGrid(tvalues)
regValues = []
regValues.append(3)
regValues.append(3)
regValues.append(1)
regValues.append(3)
regValues.append(2)
regValues.append(3)
regime = Utils.RegimeCurve(timeRegimes, regValues)
# one dimensional factors
nDim = 1
sigma = np.zeros(nDim) + 0.94
mr = np.zeros(nDim) + 0.29
# number of simulations
nbsimulOpt = 20000
# grid
#####
nGrid = 40
lowValues = np.zeros(1, dtype = float)
step = np.zeros(1, dtype = float) + maxLevelStorage / nGrid
nbStep = np.zeros(1, dtype = np.int32) + nGrid
grid = StOptGrids.RegularSpaceGrid(lowValues, step, nbStep)
# no actualization
rate = 0.
# a backward simulator
######################
bForward = False
backSimulator = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate, maturity, nstep, nbsimulOpt, bForward)
# optimizer
############
storage = opt.OptimizeGasStorageSwitchingCostMeanReverting(injectionRateStorage, withdrawalRateStorage, injectionCostStorage, withdrawalCostStorage, switchingCostStorage, regime)
# regressor
##########
nMesh = 4
nbMesh = np.zeros(1, dtype = np.int32) + nMesh
regressor = reg.LocalLinearRegression(nbMesh)
# final value
vFunction = Utils.ZeroPayOff()
# initial values
initialStock = np.zeros(1) + maxLevelStorage
initialRegime = 0 # here do nothing (no injection, no withdrawal)
# Optimize
###########
fileToDump = "CondExpGas"
bOneFile = True
# link the simulations to the optimizer
storage.setSimulator(backSimulator)
valueOptimMpi = dynmpi.DynamicProgrammingByRegressionDist(grid, storage, regressor, vFunction, initialStock, initialRegime, fileToDump, bOneFile)
print("valOP", valueOptimMpi)
world.barrier()
# a forward simulator
#####################
nbsimulSim = 40000
bForward = True
forSimulator = sim.MeanRevertingSimulator(futureGrid, sigma, mr, rate, maturity, nstep, nbsimulSim, bForward)
# link the simulations to the optimizer
storage.setSimulator(forSimulator)
valSimuMpi = srtmpi.SimulateRegressionControlDist(grid, storage, vFunction, initialStock, initialRegime, fileToDump, bOneFile)
print("valSimuMpi", valSimuMpi)
if world.rank == 0:
self.assertAlmostEqual(valueOptimMpi, valSimuMpi, None, "Re-adjust tolerance edge please", accuracyClose)
print("valOP", valueOptimMpi, "valSimuMpi", valSimuMpi)
if __name__ == '__main__':
unittest.main()
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