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RROMPy/rrompy/reduction_methods/taylor/generic_approximant_taylor.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/>.
#
from rrompy.reduction_methods.base.generic_approximant import (
GenericApproximant)
from rrompy.utilities.base.types import DictAny, HFEng
from rrompy.utilities.base import purgeDict, verbosityDepth
from rrompy.utilities.warning_manager import warn
__all__ = ['GenericApproximantTaylor']
class GenericApproximantTaylor(GenericApproximant):
"""
ROM single-point approximant 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;
- 'E': total number of derivatives current approximant relies upon;
defaults to Emax;
- 'Emax': total number of derivatives of solution map to be
computed; defaults to E;
- 'sampleType': label of sampling type; available values are:
- 'ARNOLDI': orthogonalization of solution derivatives through
Arnoldi algorithm;
- 'KRYLOV': standard computation of solution derivatives.
Defaults to 'KRYLOV'.
Defaults to empty dict.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
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;
- 'E': total number of derivatives current approximant relies upon;
- 'Emax': total number of derivatives of solution map to be
computed;
- 'sampleType': label of sampling type.
POD: Whether to compute QR factorization of derivatives.
E: Number of solution derivatives over which current approximant is
based upon.
Emax: Total number of solution derivatives to be computed.
sampleType: Label of sampling type.
initialHFData: HF problem initial data.
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 = {}, homogeneize : bool = False,
verbosity : int = 10):
self._preInit()
if not hasattr(self, "parameterList"):
self.parameterList = []
self.parameterList += ["E", "Emax", "sampleType"]
super().__init__(HFEngine = HFEngine, mu0 = mu0,
approxParameters = approxParameters,
homogeneize = homogeneize,
verbosity = verbosity)
self._postInit()
def setupSampling(self):
"""Setup sampling engine."""
if not hasattr(self, "sampleType"): return
if self.sampleType == "ARNOLDI":
from rrompy.sampling.scipy.sampling_engine_arnoldi import (
SamplingEngineArnoldi)
super().setupSampling(SamplingEngineArnoldi)
elif self.sampleType == "KRYLOV":
from rrompy.sampling.scipy.sampling_engine_krylov import (
SamplingEngineKrylov)
super().setupSampling(SamplingEngineKrylov)
else:
raise Exception("Sample type not recognized.")
@property
def approxParameters(self):
"""
Value of approximant parameters. Its assignment may change E and Emax.
"""
return self._approxParameters
@approxParameters.setter
def approxParameters(self, approxParams):
approxParameters = purgeDict(approxParams, self.parameterList,
dictname = self.name() + ".approxParameters",
baselevel = 1)
approxParametersCopy = purgeDict(approxParameters,
["E", "Emax", "sampleType"],
True, True, baselevel = 1)
GenericApproximant.approxParameters.fset(self, approxParametersCopy)
keyList = list(approxParameters.keys())
if "E" in keyList:
self._E = approxParameters["E"]
self._approxParameters["E"] = self.E
if "Emax" in keyList:
self.Emax = approxParameters["Emax"]
else:
if not hasattr(self, "Emax"):
self.Emax = self.E
else:
self.Emax = self.Emax
else:
if "Emax" in keyList:
self._E = approxParameters["Emax"]
self._approxParameters["E"] = self.E
self.Emax = self.E
else:
if not (hasattr(self, "Emax") and hasattr(self, "E")):
raise Exception("At least one of E and Emax must be set.")
if "sampleType" in keyList:
self.sampleType = approxParameters["sampleType"]
elif hasattr(self, "sampleType"):
self.sampleType = self.sampleType
else:
self.sampleType = "KRYLOV"
@property
def E(self):
"""Value of E. Its assignment may change Emax."""
return self._E
@E.setter
def E(self, E):
if E < 0: raise ArithmeticError("E must be non-negative.")
self._E = E
self._approxParameters["E"] = self.E
if hasattr(self, "Emax") and self.Emax < self.E:
warn("Prescribed E is too large. Updating Emax to E.")
self.Emax = self.E
@property
def Emax(self):
"""Value of Emax. Its assignment may reset computed derivatives."""
return self._Emax
@Emax.setter
def Emax(self, Emax):
if Emax < 0: raise ArithmeticError("Emax must be non-negative.")
if hasattr(self, "Emax"): EmaxOld = self.Emax
else: EmaxOld = -1
self._Emax = Emax
if hasattr(self, "E") and self.Emax < self.E:
warn("Prescribed Emax is too small. Updating Emax to E.")
self.Emax = self.E
else:
self._approxParameters["Emax"] = self.Emax
if (EmaxOld >= self.Emax
and self.samplingEngine.samples is not None):
self.samplingEngine.samples = self.samplingEngine.samples[:,
: self.Emax + 1]
if (self.sampleType == "ARNOLDI"
and self.samplingEngine.HArnoldi is not None):
self.samplingEngine.HArnoldi= self.samplingEngine.HArnoldi[
: self.Emax + 1,
: self.Emax + 1]
self.samplingEngine.RArnoldi= self.samplingEngine.RArnoldi[
: self.Emax + 1,
: self.Emax + 1]
else:
self.resetSamples()
@property
def sampleType(self):
"""Value of sampleType."""
return self._sampleType
@sampleType.setter
def sampleType(self, sampleType):
if hasattr(self, "sampleType"): sampleTypeOld = self.sampleType
else: sampleTypeOld = -1
try:
sampleType = sampleType.upper().strip().replace(" ","")
if sampleType not in ["ARNOLDI", "KRYLOV"]:
raise Exception("Sample type not recognized.")
self._sampleType = sampleType
except:
warn(("Prescribed sampleType not recognized. Overriding to "
"'KRYLOV'."))
self._sampleType = "KRYLOV"
self._approxParameters["sampleType"] = self.sampleType
if sampleTypeOld != self.sampleType:
self.resetSamples()
def computeDerivatives(self):
"""Compute derivatives of solution map starting from order 0."""
if self.samplingEngine.samples is None:
if self.verbosity >= 5:
verbosityDepth("INIT", "Starting computation of derivatives.")
self.samplingEngine.iterSample(self.mu0, self.Emax + 1,
homogeneize = self.homogeneize)
if self.verbosity >= 5:
verbosityDepth("DEL", "Done computing derivatives.")
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())
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