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ReducedBasis.py
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Created
Tue, May 7, 13:09
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4 KB
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text/x-python
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Thu, May 9, 13:09 (2 d)
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R6746 RationalROMPy
ReducedBasis.py
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import
numpy
as
np
from
rrompy.hfengines.linear_problem
import
\
HelmholtzSquareBubbleProblemEngine
as
HSBPE
from
rrompy.hfengines.linear_problem
import
\
HelmholtzSquareTransmissionProblemEngine
as
HSTPE
from
rrompy.hfengines.linear_problem
import
\
HelmholtzBoxScatteringProblemEngine
as
HBSPE
from
rrompy.reduction_methods.standard
import
ReducedBasis
as
RB
from
rrompy.parameter.parameter_sampling
import
QuadratureSampler
as
QS
testNo
=
1
verb
=
100
homog
=
True
#homog = False
loadName
=
"ReducedBasisModel.pkl"
if
testNo
in
[
1
,
-
1
]:
if
testNo
>
0
:
k0s
=
np
.
power
([
10
+
0.j
,
14
+
0.j
],
.
5
)
k0
=
np
.
mean
(
np
.
power
(
k0s
,
2.
))
**
.
5
params
=
{
'S'
:
5
,
'R'
:
4
,
'POD'
:
True
,
'sampler'
:
QS
(
k0s
,
"CHEBYSHEV"
,
2.
)}
ktar
=
(
11
+
.
5j
)
**
.
5
solver
=
HSBPE
(
kappa
=
12
**
.
5
,
theta
=
np
.
pi
/
3
,
n
=
40
,
verbosity
=
verb
)
if
testNo
>
0
:
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
approx
.
setupApprox
()
# approx.plotSamples()
else
:
approx
=
RB
(
solver
,
mu0
=
0
,
approxParameters
=
{
'S'
:
1
,
'muBounds'
:[
0
,
1
]},
verbosity
=
verb
)
approx
.
loadTrainedModel
(
loadName
)
approx
.
plotApprox
(
ktar
,
name
=
'u_RB'
)
approx
.
plotHF
(
ktar
,
name
=
'u_HF'
)
approx
.
plotErr
(
ktar
,
name
=
'err'
)
approx
.
plotRes
(
ktar
,
name
=
'res'
)
appErr
,
solNorm
=
approx
.
normErr
(
ktar
),
approx
.
normHF
(
ktar
)
resNorm
,
RHSNorm
=
approx
.
normRes
(
ktar
),
approx
.
normRHS
(
ktar
)
print
((
'SolNorm:
\t
{}
\n
Err:
\t
{}
\n
ErrRel:
\t
{}'
)
.
format
(
solNorm
,
appErr
,
np
.
divide
(
appErr
,
solNorm
)))
print
((
'RHSNorm:
\t
{}
\n
Res:
\t
{}
\n
ResRel:
\t
{}'
)
.
format
(
RHSNorm
,
resNorm
,
np
.
divide
(
resNorm
,
RHSNorm
)))
if
testNo
>
0
:
approx
.
storeTrainedModel
(
"ReducedBasisModel"
,
forceNewFile
=
False
)
print
(
approx
.
trainedModel
.
data
.
__dict__
)
############
elif
testNo
==
2
:
k0s
=
[
3.85
+
0.j
,
4.15
+
0.j
]
k0
=
np
.
mean
(
k0s
)
ktar
=
4
+
.
15j
params
=
{
'S'
:
10
,
'R'
:
9
,
'POD'
:
True
,
'sampler'
:
QS
(
k0s
,
"CHEBYSHEV"
,
2.
)}
solver
=
HSTPE
(
nT
=
2
,
nB
=
1
,
theta
=
np
.
pi
*
45
/
180
,
kappa
=
4.
,
n
=
50
,
verbosity
=
verb
,
homogeneized
=
homog
)
solver
.
omega
=
np
.
real
(
k0
)
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
approx
.
setupApprox
()
# approx.plotSamples()
approx
.
plotApprox
(
ktar
,
name
=
'u_RB'
)
approx
.
plotHF
(
ktar
,
name
=
'u_HF'
)
approx
.
plotErr
(
ktar
,
name
=
'err'
)
approx
.
plotRes
(
ktar
,
name
=
'res'
)
appErr
,
solNorm
=
approx
.
normErr
(
ktar
),
approx
.
normHF
(
ktar
)
resNorm
,
RHSNorm
=
approx
.
normRes
(
ktar
),
approx
.
normRHS
(
ktar
)
print
((
'SolNorm:
\t
{}
\n
Err:
\t
{}
\n
ErrRel:
\t
{}'
)
.
format
(
solNorm
,
appErr
,
np
.
divide
(
appErr
,
solNorm
)))
print
((
'RHSNorm:
\t
{}
\n
Res:
\t
{}
\n
ResRel:
\t
{}'
)
.
format
(
RHSNorm
,
resNorm
,
np
.
divide
(
resNorm
,
RHSNorm
)))
############
elif
testNo
==
3
:
k0s
=
[
2.
,
5.
]
k0
=
np
.
mean
(
k0s
)
ktar
=
4.5
-
0.j
params
=
{
'S'
:
15
,
'R'
:
10
,
'POD'
:
True
,
'sampler'
:
QS
(
k0s
,
"CHEBYSHEV"
)}
solver
=
HBSPE
(
R
=
7
,
kappa
=
3
,
theta
=
-
np
.
pi
*
75
/
180
,
n
=
40
,
verbosity
=
verb
,
homogeneized
=
homog
)
solver
.
omega
=
np
.
real
(
k0
)
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
approx
.
setupApprox
()
# approx.plotSamples()
approx
.
plotApprox
(
ktar
,
name
=
'u_RB'
)
approx
.
plotHF
(
ktar
,
name
=
'u_HF'
)
approx
.
plotErr
(
ktar
,
name
=
'err'
)
approx
.
plotRes
(
ktar
,
name
=
'res'
)
appErr
,
solNorm
=
approx
.
normErr
(
ktar
),
approx
.
normHF
(
ktar
)
resNorm
,
RHSNorm
=
approx
.
normRes
(
ktar
),
approx
.
normRHS
(
ktar
)
print
((
'SolNorm:
\t
{}
\n
Err:
\t
{}
\n
ErrRel:
\t
{}'
)
.
format
(
solNorm
,
appErr
,
np
.
divide
(
appErr
,
solNorm
)))
print
((
'RHSNorm:
\t
{}
\n
Res:
\t
{}
\n
ResRel:
\t
{}'
)
.
format
(
RHSNorm
,
resNorm
,
np
.
divide
(
resNorm
,
RHSNorm
)))
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