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RROMPy/tests/test_4_reduction_methods_multiD/rational_interpolant_greedy_2d.py
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rational_interpolant_greedy_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 pytest
import numpy as np
from matrix_random import matrixRandom
from rrompy.reduction_methods.distributed_greedy import \
RationalInterpolantGreedy as RIG
@pytest.mark.xfail
def test_samples_at_poles():
solver = matrixRandom()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 4, "nTestPoints": 100,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-5,
"errorEstimatorKind": "exact"}
approx = RIG(solver, 4., params, verbosity = 0)
approx.greedy()
for mu in approx.mus:
assert np.isclose(approx.normErr(mu)[0] / (1e-15+approx.normHF(mu)[0]),
0., atol = 1e-4)
poles = approx.getPoles()
for lambda_ in range(2, 7):
assert np.isclose(np.min(np.abs(poles - lambda_)), 0., atol = 1e-3)
assert np.isclose(np.min(np.abs(np.array(approx.mus(0)) - lambda_)),
0., atol = 1e-1)
@pytest.mark.xfail
def test_maxIter():
solver = matrixRandom()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 5, "nTestPoints": 500,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-6, "maxIter": 10,
"errorEstimatorKind": "basic"}
approx = RIG(solver, 4., params, verbosity = 0)
approx.input = lambda: "N"
approx.greedy()
assert len(approx.mus) == 10
_, _, maxEst = approx.getMaxErrorEstimator(approx.muTest)
assert maxEst > 1e-6
@pytest.mark.xfail
def test_load_copy(capsys):
mu = 3.
solver = matrixRandom()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 4, "nTestPoints": 100,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-5,
"errorEstimatorKind": "exact"}
approx1 = RIG(solver, 4., params, verbosity = 100)
approx1.greedy()
err1 = approx1.normErr(mu)[0]
out, err = capsys.readouterr()
assert "Solving HF model for mu =" in out
assert len(err) == 0
approx2 = RIG(solver, 4., params, verbosity = 100)
approx2.setTrainedModel(approx1)
approx2.setHF(mu, approx1.uHF)
err2 = approx2.normErr(mu)[0]
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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