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generic_approximant_taylor.py
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R6746 RationalROMPy
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/>.
#
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
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.
homogeneized: Whether to homogeneize Dirichlet BCs. Defaults to False.
verbosity(optional): Verbosity level. Defaults to 10.
Attributes:
HFEngine: HF problem solver.
mu0: Default parameter.
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;
- '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
=
{},
homogeneized
:
bool
=
False
,
verbosity
:
int
=
10
):
self
.
_preInit
()
self
.
_addParametersToList
([
"E"
,
"Emax"
,
"sampleType"
])
super
()
.
__init__
(
HFEngine
=
HFEngine
,
mu0
=
mu0
,
approxParameters
=
approxParameters
,
homogeneized
=
homogeneized
,
verbosity
=
verbosity
)
self
.
_postInit
()
def
setupSampling
(
self
):
"""Setup sampling engine."""
if
not
hasattr
(
self
,
"sampleType"
):
return
if
self
.
sampleType
==
"ARNOLDI"
:
from
rrompy.sampling.linear_problem.sampling_engine_arnoldi
\
import
SamplingEngineArnoldi
super
()
.
setupSampling
(
SamplingEngineArnoldi
)
elif
self
.
sampleType
==
"KRYLOV"
:
from
rrompy.sampling.linear_problem.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
,
homogeneized
=
self
.
homogeneized
)
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
())
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
self
.
sampleType
!=
"ARNOLDI"
or
self
.
homogeneized
!=
homogeneized
:
return
super
()
.
normApprox
(
mu
,
homogeneized
)
return
np
.
linalg
.
norm
(
self
.
getApproxReduced
(
mu
,
homogeneized
))
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