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RROMPy/tests/3_reduction_methods_1D/rational_interpolant_1d.py
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rational_interpolant_1d.py
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# Copyright (C) 2018-2020 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_fft import matrixFFT
from rrompy.reduction_methods import RationalInterpolant as RI
from rrompy.parameter.parameter_sampling import (QuadratureSampler as QS,
ManualSampler as MS)
from rrompy.parameter import checkParameterList
def test_monomials(capsys):
mu = 1.5
solver = matrixFFT()
params = {"POD": False, "S": 10, "QTol": 1e-6, "interpTol": 1e-3,
"polybasis": "MONOMIAL", "sampler": QS([1.5, 6.5], "UNIFORM")}
approx = RI(solver, 4., approxParameters = params, verbosity = 10)
approx.setupApprox()
out, err = capsys.readouterr()
assert "below tolerance. Reducing N" in out
assert "poorly conditioned. Reducing M " in out
assert len(err) == 0
assert np.isclose(approx.normErr(mu)[0], 2.8e-4, atol = 1e-2)
def test_well_cond():
mu = 1.5
solver = matrixFFT()
params = {"POD": True, "S": 10, "QTol": 1e-14, "interpTol": 1e-10,
"polybasis": "CHEBYSHEV", "sampler": QS([1., 7.], "CHEBYSHEV")}
approx = RI(solver, 4., approxParameters = params, verbosity = 0)
approx.setupApprox()
poles = approx.getPoles()
for lambda_ in np.arange(1, 8):
assert np.isclose(np.min(np.abs(poles - lambda_)), 0., atol = 1e-4)
for mu in approx.mus:
assert np.isclose(approx.normErr(mu)[0], 0., atol = 1e-8)
def test_hermite():
mu = 1.5
solver = matrixFFT()
sampler0 = QS([1., 7.], "CHEBYSHEV")
points = checkParameterList(np.tile(sampler0.generatePoints(4)(0), 3))
params = {"POD": True, "S": 12, "polybasis": "CHEBYSHEV",
"sampler": MS([1., 7.], points = points)}
approx = RI(solver, 4., approxParameters = params, verbosity = 0)
approx.setupApprox()
poles = approx.getPoles()
for lambda_ in np.arange(1, 8):
assert np.isclose(np.min(np.abs(poles - lambda_)), 0., atol = 1e-4)
for mu in approx.mus:
assert np.isclose(approx.normErr(mu)[0], 0., atol = 1e-8)
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