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anisotropic_square.py
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Sat, Oct 19, 17:39

anisotropic_square.py
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import numpy as np
import matplotlib.pyplot as plt
from itertools import product
from anisotropic_square_engine import (AnisotropicSquareEngine as engine,
AnisotropicSquareEnginePoles as plsEx)
from rrompy.sampling import SamplingEngineStandard as SES
from rrompy.reduction_methods import (NearestNeighbor as NN,
RationalInterpolantGreedyPivotedGreedy as RIGPG)
from rrompy.parameter.parameter_sampling import QuadratureSampler as QS
from rrompy.parameter import localSparseGrid as LSG
zs, Ls = [10., 50.], [.2, 1.2]
z0, L0, n = np.mean(zs), np.mean(Ls), 50
murange = [[zs[0], Ls[0]], [zs[-1], Ls[-1]]]
np.random.seed(4020)
mu = [zs[0] + np.random.rand() * (zs[-1] - zs[0]),
Ls[0] + np.random.rand() * (Ls[-1] - Ls[0])]
solver = engine(z0, L0, n)
fighandles = []
params = {"POD": True, "nTestPoints": 100, "greedyTol": 1e-4, "S": 3,
"polybasisMarginal": "MONOMIAL_WENDLAND",
"polybasis": "LEGENDRE", 'samplerPivot':QS(zs, "UNIFORM"),
'trainSetGenerator':QS(zs, "UNIFORM"),
'errorEstimatorKind':"LOOK_AHEAD_RES",
"MMarginal": 2, "SMarginal": 3,
"greedyTolMarginal": 1e-2, "samplerMarginalGrid":LSG(Ls),
"radialDirectionalWeightsMarginal": [2.], "matchingWeight": 1.}
for tol, shared in product([1., 3.], [1., 0.]):
print("Testing cutoff tolerance {} with shared ratio {}.".format(tol,
shared))
params['cutOffTolerance'] = tol
params['cutOffSharedRatio'] = shared
approx = RIGPG([0], solver, mu0 = [z0, L0], approx_state = True,
approxParameters = params, verbosity = 5)
approx.setupApprox("LAST")
print("--- Approximant ---")
approx.plotApprox(mu, name = 'u_app')
approx.plotHF(mu, name = 'u_HF')
approx.plotErr(mu, name = 'err_app')
approx.plotRes(mu, name = 'res_app')
normErr = approx.normErr(mu)[0]
normSol = approx.normHF(mu)[0]
normRes = approx.normRes(mu)[0]
normRHS = approx.normRHS(mu)[0]
print("SolNorm:\t{:.5e}\nErr_app: \t{:.5e}\nErrRel_app:\t{:.5e}".format(
normSol, normErr, normErr / normSol))
print("RHSNorm:\t{:.5e}\nRes_app: \t{:.5e}\nResRel_app:\t{:.5e}".format(
normRHS, normRes, normRes / normRHS))
print("--- Closest snapshot ---")
eng = SES(solver, verbosity = 0)
eng.nsamples = approx.samplingEngine.nsamplesCoalesced
eng.mus = approx.samplingEngine.musCoalesced
eng.samples = approx.samplingEngine.samples_fullCoalesced
paramsNN = {'S':eng.nsamples, 'POD':False,
'sampler':QS(murange, "UNIFORM")}
approxNN = NN(solver, mu0 = [z0, L0], approx_state = True,
approxParameters = paramsNN, verbosity = 0)
approxNN.setSamples(eng)
approxNN.plotApprox(mu, name = 'u_close')
approxNN.plotHF(mu, name = 'u_HF')
approxNN.plotErr(mu, name = 'err_close')
approxNN.plotRes(mu, name = 'res_close')
normErr = approxNN.normErr(mu)[0]
normSol = approxNN.normHF(mu)[0]
normRes = approxNN.normRes(mu)[0]
normRHS = approxNN.normRHS(mu)[0]
print("SolNorm:\t{:.5e}\nErr_close:\t{:.5e}\nErrRel_close:\t{:.5e}".format(
normSol, normErr, normErr / normSol))
print("RHSNorm:\t{:.5e}\nRes_close:\t{:.5e}\nResRel_close:\t{:.5e}".format(
normRHS, normRes, normRes / normRHS))
verb = approx.verbosity
approx.verbosity = 0
tspace = np.linspace(Ls[0], Ls[-1], 100)
for j, t in enumerate(tspace):
plsE = plsEx(t, 0., zs[-1])
pls = approx.getPoles([None, t])
pls[np.abs(np.imag(pls)) > 1e-5] = np.nan
if j == 0:
polesE = np.empty((len(tspace), len(plsE)))
poles = np.empty((len(tspace), len(pls)))
polesE[:] = np.nan
if len(plsE) > polesE.shape[1]:
nanR = np.empty((len(tspace), len(plsE) - polesE.shape[1]))
nanR[:] = np.nan
polesE = np.hstack((polesE, nanR))
polesE[j, : len(plsE)] = np.real(plsE)
poles[j] = np.real(pls)
approx.verbosity = verb
fighandles += [plt.figure(figsize = (17, 5))]
ax1 = fighandles[-1].add_subplot(1, 2, 1)
ax2 = fighandles[-1].add_subplot(1, 2, 2)
ax1.plot(poles, tspace)
ax1.set_ylim(Ls)
ax1.set_xlabel('mu_1')
ax1.set_ylabel('mu_2')
ax1.grid()
ax2.plot(polesE, tspace, 'k-.', linewidth = 1)
ax2.plot(poles, tspace)
for mm in approx.musMarginal:
ax2.plot(zs, [mm[0, 0]] * 2, 'k--', linewidth = 1)
ax2.set_xlim(zs)
ax2.set_ylim(Ls)
ax2.set_xlabel('mu_1')
ax2.set_ylabel('mu_2')
ax2.grid()
plt.show()
print("\n")

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