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RROMPy/tests/reduction_methods_multiD/reduced_basis_distributed_2d.py
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reduced_basis_distributed_2d.py
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# Copyright (C) 2018 by the RROMPy authors
#
# This file is part of RROMPy.
#
# RROMPy is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# RROMPy is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with RROMPy. If not, see <http://www.gnu.org/licenses/>.
#
import numpy as np
from matrix_random import matrixRandom
from rrompy.reduction_methods.distributed import ReducedBasis as RB
from rrompy.parameter.parameter_sampling import (QuadratureSampler as QS,
ManualSampler as MS)
from rrompy.parameter import checkParameterList
def test_LS():
mu0 = [2, 3]
solver = matrixRandom()
params = {"POD": True, "R": 5, "S": [3, 3],
"sampler": QS([[0., 4.], [1., 5.]], "CHEBYSHEV")}
approx = RB(solver, mu0, params, verbosity = 0)
approx.setupApprox()
for mu in approx.mus:
assert not np.isclose(approx.normErr(mu)[0], 0., atol = 1e-7)
approx.POD = False
approx.setupApprox()
for mu in approx.mus[approx.R :]:
assert not np.isclose(approx.normErr(mu)[0], 0., atol = 1e-3)
def test_interp():
mu0 = [2, 3]
solver = matrixRandom()
params = {"POD": False, "S": [3, 3],
"sampler": QS([[0., 4.], [1., 5.]], "CHEBYSHEV")}
approx = RB(solver, mu0, params, verbosity = 0)
approx.setupApprox()
for mu in approx.mus:
assert np.isclose(approx.normErr(mu)[0], 0., atol = 1e-7)
def test_hermite():
mu0 = [2, 3]
solver = matrixRandom()
sampler0 = QS([[0., 4.], [1., 5.]], "CHEBYSHEV")
points, _ = checkParameterList(np.tile(
sampler0.generatePoints([2, 2]).data, [3, 1]))
params = {"POD": True, "S": [12],
"sampler": MS([[0., 4.], [1., 5.]], points = points)}
approx = RB(solver, mu0, params, verbosity = 0)
approx.setupApprox()
for mu in approx.mus:
assert np.isclose(approx.normErr(mu)[0], 0., atol = 1e-8)
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