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RROMPy/examples/pod/PadeLagrange.py
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PadeLagrange.py
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import numpy as np
from rrompy.hfengines.linear_problem import \
HelmholtzSquareBubbleProblemEngine as HSBPE
from rrompy.hfengines.linear_problem import \
HelmholtzSquareTransmissionProblemEngine as HSTPE
from rrompy.hfengines.linear_problem import \
HelmholtzBoxScatteringProblemEngine as HBSPE
from rrompy.reduction_methods.lagrange import ApproximantLagrangePade as Pade
from rrompy.utilities.parameter_sampling import QuadratureSampler as QS
testNo = 1
verb = 100
polyBasis = "CHEBYSHEV"
polyBasis = "LEGENDRE"
#polyBasis = "MONOMIAL"
homog = True
#homog = False
loadName = "PadeLagrangeModel.pkl"
if testNo in [1, -1]:
if testNo > 0:
k0s = np.power([10 + 0.j, 14 + 0.j], .5)
k0 = np.mean(np.power(k0s, 2.)) ** .5
rescaling = lambda x: np.power(x, 2.)
rescalingInv = lambda x: np.power(x, .5)
params = {'N':4, 'M':3, 'S':5, 'POD':True, 'polybasis':polyBasis}
ktar = (11 + .5j) ** .5
solver = HSBPE(kappa = 12 ** .5, theta = np.pi / 3, n = 40,
verbosity = verb)
if testNo > 0:
solver.omega = np.real(k0)
approx = Pade(solver, mu0 = k0, approxParameters = params,
verbosity = verb)
approx.sampler = QS(k0s, "CHEBYSHEV", rescaling, rescalingInv)
approx.setupApprox()
# approx.plotSamples()
else:
approx = Pade(solver, mu0 = 0, verbosity = verb)
approx.loadTrainedModel(loadName)
approx.plotApprox(ktar, name = 'u_Pade''')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
print('\nPoles Pade'':')
print(approx.getPoles())
if testNo > 0:
approx.storeTrainedModel("PadeLagrangeModel", forceNewFile = False)
print(approx.trainedModel.data.__dict__)
############
elif testNo == 2:
k0s = [3.85 + 0.j, 4.15 + 0.j]
k0 = np.mean(k0s)
ktar = 4 + 0.j
rescaling = lambda x: np.power(x, 2.)
rescalingInv = lambda x: np.power(x, .5)
params = {'N':8, 'M':9, 'S':10, 'POD':True, 'polybasis':polyBasis},
solver = HSTPE(nT = 2, nB = 1, theta = np.pi * 45/180, kappa = 4., n = 50,
verbosity = verb)
solver.omega = np.real(k0)
approx = Pade(solver, mu0 = k0, approxParameters = params,
verbosity = verb, homogeneized = homog)
approx.sampler = QS(k0s, "CHEBYSHEV", rescaling, rescalingInv)
approx.setupApprox()
# approx.plotSamples()
approx.plotApprox(ktar, name = 'u_Pade''')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
print('\nPoles Pade'':')
print(approx.getPoles())
############
elif testNo == 3:
k0s = [2, 5]
k0 = np.mean(k0s)
ktar = 4.5 - .1j
params = {'N':10, 'M':10, 'S':11, 'POD':True, 'polybasis':polyBasis}
solver = HBSPE(R = 7, kappa = 3, theta = - np.pi * 75 / 180, n = 40,
verbosity = verb)
solver.omega = np.real(k0)
approx = Pade(solver, mu0 = k0, approxParameters = params,
verbosity = verb, homogeneized = homog)
approx.sampler = QS(k0s, "CHEBYSHEV")
approx.setupApprox()
# approx.plotSamples()
approx.plotApprox(ktar, name = 'u_Pade''')
approx.plotHF(ktar, name = 'u_HF')
approx.plotErr(ktar, name = 'err')
approx.plotRes(ktar, name = 'res')
appErr, solNorm = approx.normErr(ktar), approx.normHF(ktar)
resNorm, RHSNorm = approx.normRes(ktar), approx.normRHS(ktar)
print(('SolNorm:\t{}\nErr:\t{}\nErrRel:\t{}').format(solNorm, appErr,
np.divide(appErr, solNorm)))
print(('RHSNorm:\t{}\nRes:\t{}\nResRel:\t{}').format(RHSNorm, resNorm,
np.divide(resNorm, RHSNorm)))
print('\nPoles Pade'':')
print(approx.getPoles())
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