Homec4science
Diffusion RationalROMPy (feature_greedy_coarsening) Browse

RROMPy/tests/test_3_reduction_methods/rational_interpolant_greedy.py
2aa049ac9d03feature_greedy_coarseni…

Last Change
Raw File

rational_interpolant_greedy.py
View Options

# 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.distributed_greedy import \
RationalInterpolantGreedy as RIG
def test_lax_tolerance(capsys):
solver = matrixFFT()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 4,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-2}
approx = RIG(solver, 4., params, verbosity = 10)
approx.greedy()
out, err = capsys.readouterr()
assert "Done computing snapshots (final snapshot count: 10)." in out
assert len(err) == 0
assert np.isclose(approx.normErr(0), .0077041389, rtol = 1e-3)
def test_samples_at_poles():
solver = matrixFFT()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 4, "nTestPoints": 100,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-5}
approx = RIG(solver, 4., params, verbosity = 0)
approx.greedy()
for mu in approx.mus:
assert np.isclose(approx.normErr(mu) / (1e-15 + approx.normHF(mu)),
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(approx.mus - lambda_)), 0.,
atol = 1e-1)
def test_maxIter():
solver = matrixFFT()
params = {"POD": True, "muBounds": [1.5, 6.5], "S": 5, "nTestPoints": 500,
"polybasis": "CHEBYSHEV", "greedyTol": 1e-6, "maxIter": 10}
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
c4science · Help