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RROMPy/rrompy/reduction_methods/lagrange/generic_approximant_lagrange.py
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generic_approximant_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.reduction_methods.base.generic_approximant import (
GenericApproximant)
from rrompy.utilities.base.types import DictAny, HFEng
from rrompy.utilities.base import purgeDict, verbosityDepth
__all__ = ['GenericApproximantLagrange']
class GenericApproximantLagrange(GenericApproximant):
"""
ROM Lagrange interpolant computation for parametric problems (ABSTRACT).
Args:
HFEngine: HF problem solver.
mu0(optional): Default parameter. Defaults to 0.
approxParameters(optional): Dictionary containing values for main
parameters of approximant. Recognized keys are:
- 'POD': whether to compute POD of snapshots; defaults to True;
- 'S': total number of samples current approximant relies upon;
- 'sampler': sample point generator; defaults to uniform sampler on
[0, 1].
Defaults to empty dict.
homogeneized: Whether to homogeneize Dirichlet BCs. Defaults to False.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
mus: Array of snapshot parameters.
ws: Array of snapshot weigths.
homogeneized: Whether to homogeneize Dirichlet BCs.
approxParameters: Dictionary containing values for main parameters of
approximant. Recognized keys are in parameterList.
parameterList: Recognized keys of approximant parameters:
- 'POD': whether to compute POD of snapshots;
- 'S': total number of snapshots current approximant relies upon;
- 'sampler': sample point generator.
extraApproxParameters: List of approxParameters keys in addition to
mother class's.
S: Number of solution snapshots over which current approximant is
based upon.
sampler: Sample point generator.
POD: Whether to compute POD of snapshots.
samplingEngine: Sampling engine.
uHF: High fidelity solution with wavenumber lastSolvedHF as numpy
complex vector.
lastSolvedHF: Wavenumber corresponding to last computed high fidelity
solution.
uApp: Last evaluated approximant as numpy complex vector.
lastApproxParameters: List of parameters corresponding to last
computed approximant.
"""
def __init__(self, HFEngine:HFEng, mu0 : complex = 0.,
approxParameters : DictAny = {}, homogeneized : bool = False,
verbosity : int = 10):
self._preInit()
self._addParametersToList(["S", "sampler"])
super().__init__(HFEngine = HFEngine, mu0 = mu0,
approxParameters = approxParameters,
homogeneized = homogeneized,
verbosity = verbosity)
self._postInit()
def setupSampling(self):
"""Setup sampling engine."""
if not hasattr(self, "POD"): return
if self.POD:
from rrompy.sampling.linear_problem.sampling_engine_lagrange_pod \
import SamplingEngineLagrangePOD
super().setupSampling(SamplingEngineLagrangePOD)
else:
from rrompy.sampling.linear_problem.sampling_engine_lagrange \
import SamplingEngineLagrange
super().setupSampling(SamplingEngineLagrange)
@property
def mus(self):
"""Value of mus. Its assignment may reset snapshots."""
return self._mus
@mus.setter
def mus(self, mus):
musOld = self.mus if hasattr(self, 'mus') else None
self._mus = np.array(mus)
_, musCounts = np.unique(self._mus, return_counts = True)
if len(np.where(musCounts > 1)[0]) > 0:
raise Exception("Repeated sample points not allowed.")
if (musOld is None or len(self.mus) != len(musOld)
or not np.allclose(self.mus, musOld, 1e-14)):
self.resetSamples()
self.autoNode = None
@property
def approxParameters(self):
"""Value of approximant parameters. Its assignment may change S."""
return self._approxParameters
@approxParameters.setter
def approxParameters(self, approxParams):
approxParameters = purgeDict(approxParams, self.parameterList,
dictname = self.name() + ".approxParameters",
baselevel = 1)
approxParametersCopy = purgeDict(approxParameters, ["S", "sampler"],
True, True, baselevel = 1)
GenericApproximant.approxParameters.fset(self, approxParametersCopy)
keyList = list(approxParameters.keys())
if "S" in keyList:
self.S = approxParameters["S"]
elif hasattr(self, "S"):
self.S = self.S
else:
self.S = 2
if "sampler" in keyList:
self.sampler = approxParameters["sampler"]
elif not hasattr(self, "S"):
from rrompy.utilities.parameter_sampling import QuadratureSampler
self.sampler = QuadratureSampler([0., 1.], "UNIFORM")
del QuadratureSampler
@property
def S(self):
"""Value of S."""
return self._S
@S.setter
def S(self, S):
if S <= 0: raise ArithmeticError("S must be positive.")
if hasattr(self, "S"): Sold = self.S
else: Sold = -1
self._S = S
self._approxParameters["S"] = self.S
if Sold != self.S:
self.resetSamples()
@property
def sampler(self):
"""Value of sampler."""
return self._sampler
@sampler.setter
def sampler(self, sampler):
if 'generatePoints' not in dir(sampler):
raise Exception("Sampler type not recognized.")
if hasattr(self, '_sampler'):
samplerOld = self.sampler
self._sampler = sampler
self._approxParameters["sampler"] = self.sampler
if not 'samplerOld' in locals() or samplerOld != self.sampler:
self.resetSamples()
def computeSnapshots(self):
"""Compute snapshots of solution map."""
if self.samplingEngine.samples is None:
if self.verbosity >= 5:
verbosityDepth("INIT", "Starting computation of snapshots.")
self.mus, self.ws = self.sampler.generatePoints(self.S)
self.mus = np.array([x[0] for x in self.mus])
self.samplingEngine.iterSample(self.mus,
homogeneized = self.homogeneized)
if self.verbosity >= 5:
verbosityDepth("DEL", "Done computing snapshots.")
def checkComputedApprox(self) -> bool:
"""
Check if setup of new approximant is not needed.
Returns:
True if new setup is not needed. False otherwise.
"""
return (self.samplingEngine.samples is not None
and super().checkComputedApprox())
def normApprox(self, mu:complex, homogeneized : bool = False) -> float:
"""
Compute norm of approximant at arbitrary parameter.
Args:
mu: Target parameter.
homogeneized(optional): Whether to remove Dirichlet BC. Defaults to
False.
Returns:
Target norm of approximant.
"""
if not self.POD or self.homogeneized != homogeneized:
return super().normApprox(mu, homogeneized)
return np.linalg.norm(self.getApproxReduced(mu))
def computeScaleFactor(self):
"""Compute parameter rescaling factor."""
self.scaleFactor = .5 * np.abs(self.HFEngine.rescaling(
self.sampler.lims[0][0])
- self.HFEngine.rescaling(
self.sampler.lims[1][0]))
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