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rb_centered.py
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Tue, Jul 23, 03:23

rb_centered.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_fft import matrixFFT
from rrompy.reduction_methods.centered import RBCentered as RBC
def test_R():
mu = 1.5
mu0 = 2. + 1.j
solver = matrixFFT()
uh = solver.solve(mu)
params = {"POD": True, "R": 5, "E": 10}
approx = RBC(solver, mu0, params, verbosity = 0)
approx.setupApprox()
uhP = approx.getApprox(mu)
errP = approx.getErr(mu)
errNP = approx.normErr(mu)
assert np.allclose(np.abs(errP - (uhP - uh)), 0., rtol = 1e-3)
# assert np.isclose(errNP, 0.023691832, rtol = 1e-1)
poles = approx.getPoles()
assert np.isclose(np.min(np.abs(poles - 2.)), 0., atol = 1e-4)
assert np.isclose(np.min(np.abs(poles - 1.)), 0., atol = 1e-2)
assert np.isclose(np.min(np.abs(poles - 3.)), 0., atol = 1e-2)
def test_moments():
mu0 = 2. + 1.j
solver = matrixFFT()
params = {"POD": True, "E": 10}
approx = RBC(solver, mu0, params, verbosity = 0)
approx.setupApprox()
assert np.isclose(approx.normErr(mu0), 0., atol = 1e-10)
def test_load_copy(capsys):
mu = 1.5
mu0 = 2. + 1.j
solver = matrixFFT()
params = {"POD": True, "E": 10}
approx1 = RBC(solver, mu0, params, verbosity = 100)
approx1.setupApprox()
err1 = approx1.normErr(mu)
out, err = capsys.readouterr()
assert "Solving HF model for mu =" in out
assert len(err) == 0
approx2 = RBC(solver, mu0, params, verbosity = 100)
approx2.setApprox(approx1.trainedModel)
approx2.setHF(mu, approx1.uHF)
err2 = approx2.normErr(mu)
out, err = capsys.readouterr()
assert "Solving HF model for mu =" not in out
assert len(err) == 0
assert np.isclose(err1, err2, rtol = 1e-10)

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