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RROMPy/rrompy/sampling/linear_problem/sampling_engine_lagrange.py
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sampling_engine_lagrange.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 rrompy.sampling.base.sampling_engine_base import SamplingEngineBase
from rrompy.utilities.base.types import Np1D, Np2D
from rrompy.utilities.base import verbosityDepth
__all__ = ['SamplingEngineLagrange']
class SamplingEngineLagrange(SamplingEngineBase):
"""HERE"""
nameBase = 1
def postprocessu(self, u:Np1D, overwrite : bool = False):
return u
def nextSample(self, mu:complex, overwrite : bool = False,
homogeneized : bool = False) -> Np1D:
ns = self.nsamples
u = self.postprocessu(self.solveLS(mu, homogeneized = homogeneized),
overwrite = overwrite)
if overwrite:
self.samples[:, ns] = u
else:
if ns == 0:
self.samples = u[:, None]
else:
self.samples = np.hstack((self.samples, u[:, None]))
self.nsamples += 1
return u
def iterSample(self, mus:Np1D, homogeneized : bool = False) -> Np2D:
if self.verbosity >= 5:
verbosityDepth("INIT", "Starting sampling iterations.")
n = mus.size
if n <= 0:
raise Exception(("Number of samples must be positive."))
self.resetHistory()
u = self.nextSample(mus[0], homogeneized = homogeneized)
if n > 1:
self.preallocateSamples(u, n)
for j in range(1, n):
self.nextSample(mus[j], overwrite = True,
homogeneized = homogeneized)
if self.verbosity >= 5:
verbosityDepth("DEL", "Finished sampling iterations.")
return self.samples
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