def computeAharm_offline():
aRefListSingle, _, _, _, _ = algorithms.computeCSI_offline(
world, Nepsilon // NCoarse, 0, boundaryConditions, model)
return [
world.NWorldFine / (world.NWorldCoarse * np.sum(1. / aRefSingle))
for aRefSingle in aRefListSingle
]
return np.abs(aharmT - aharmT_combined)
error_harmList = list(map(compute_errorHarmT, range(world.NtCoarse)))
return np.max(error_harmList)
for Nc in NList:
NCoarse = np.array([Nc])
NCoarseElement = NFine // NCoarse
world = World(NCoarse, NCoarseElement, boundaryConditions)
xpFine = util.pCoordinates(NFine)
ffunc = lambda x: 8 * np.pi**2 * np.sin(2 * np.pi * x)
f = ffunc(xpFine).flatten()
aRefList, KmsijList, muTPrimeList, _, _ = algorithms.computeCSI_offline(
world, Nepsilon // NCoarse, k, boundaryConditions, model)
aharmList = computeAharm_offline()
harmErrorList = np.zeros((len(pList), NSamples))
absErrorList = np.zeros((len(pList), NSamples))
relErrorList = np.zeros((len(pList), NSamples))
ii = 0
for p in pList:
for N in range(NSamples):
aPert = build_coefficient.build_randomcheckerboard(
Nepsilon, NFine, alpha, beta, p)
MFull = fem.assemblePatchMatrix(world.NWorldFine, world.MLocFine)
basis = fem.assembleProlongationMatrix(world.NWorldCoarse,
world.NCoarseElement)
beta = 1.
pList = [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1]
percentage_comp = 0.15
model = {'name': 'check', 'alpha': alpha, 'beta': beta}
np.random.seed(123)
NCoarseElement = NFine // NCoarse
world = World(NCoarse, NCoarseElement, boundaryConditions)
xpFine = util.pCoordinates(NFine)
ffunc = lambda x: 8 * np.pi**2 * np.sin(2 * np.pi * x[:, 0]) * np.cos(
2 * np.pi * x[:, 1])
f = ffunc(xpFine).flatten()
aRefList, KmsijList,muTPrimeList, timeBasis, timeMatrixList, correctorsList\
= algorithms.computeCSI_offline(world, Nepsilon // NCoarse,k,boundaryConditions,model,correctors=True)
aRef = np.copy(aRefList[-1])
KmsijRef = np.copy(KmsijList[-1])
muTPrimeRef = muTPrimeList[-1]
correctorsRef = correctorsList[-1]
print('offline time for new approach {}'.format(
timeBasis + np.sum(np.array(timeMatrixList))))
print('offline time for perturbed LOD {}'.format(timeMatrixList[-1]))
relerr_comb = np.zeros((len(pList), NSamples))
relerr_noup = np.zeros((len(pList), NSamples))
relerr_up = np.zeros((len(pList), NSamples))
relerr_comb_H1Coarse = np.zeros((len(pList), NSamples))
relerr_comb_H1Fine = np.zeros((len(pList), NSamples))
relerr_comb_energyCoarse = np.zeros((len(pList), NSamples))