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square_pod.py
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Created
Sun, May 12, 19:50
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5 KB
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text/x-python
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Tue, May 14, 19:50 (2 d)
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blob
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17640455
Attached To
R6746 RationalROMPy
square_pod.py
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import
numpy
as
np
from
rrompy.hfengines.linear_problem.bidimensional
import
\
HelmholtzSquareDomainProblemEngine
as
HSDPE
from
rrompy.reduction_methods.centered
import
RationalPade
as
RP
from
rrompy.reduction_methods.distributed
import
RationalInterpolant
as
RI
from
rrompy.reduction_methods.centered
import
RBCentered
as
RBC
from
rrompy.reduction_methods.distributed
import
RBDistributed
as
RBD
from
rrompy.parameter.parameter_sampling
import
(
QuadratureSampler
as
QS
,
QuadratureSamplerTotal
as
QST
,
ManualSampler
as
MS
,
RandomSampler
as
RS
)
verb
=
5
size
=
6
show_sample
=
False
ignore_forcing
=
True
#ignore_forcing = False
MN
=
5
R
=
(
MN
+
2
)
*
(
MN
+
1
)
//
2
S
=
[
int
(
np
.
ceil
(
R
**
.
5
))]
*
2
samples
=
"centered"
samples
=
"centered_fake"
samples
=
"distributed"
algo
=
"rational"
#algo = "RB"
sampling
=
"quadrature"
sampling
=
"quadrature_total"
sampling
=
"random"
if
size
==
1
:
# small
mu0
=
[
4
**
.
5
,
1.5
**
.
5
]
mutar
=
[
5
**
.
5
,
1.75
**
.
5
]
murange
=
[[
2
**
.
5
,
1.
**
.
5
],
[
6
**
.
5
,
2.
**
.
5
]]
elif
size
==
2
:
# medium
mu0
=
[
4
**
.
5
,
1.75
**
.
5
]
mutar
=
[
5
**
.
5
,
1.25
**
.
5
]
murange
=
[[
1
**
.
5
,
1.
**
.
5
],
[
7
**
.
5
,
2.5
**
.
5
]]
elif
size
==
3
:
# fat
mu0
=
[
6
**
.
5
,
4
**
.
5
]
mutar
=
[
2
**
.
5
,
2.5
**
.
5
]
murange
=
[[
0
**
.
5
,
2
**
.
5
],
[
12
**
.
5
,
6
**
.
5
]]
elif
size
==
4
:
# crowded
mu0
=
[
10
**
.
5
,
2
**
.
5
]
mutar
=
[
9
**
.
5
,
2.25
**
.
5
]
murange
=
[[
8
**
.
5
,
1.5
**
.
5
],
[
12
**
.
5
,
2.5
**
.
5
]]
elif
size
==
5
:
# tall
mu0
=
[
11
**
.
5
,
2.25
**
.
5
]
mutar
=
[
10.5
**
.
5
,
2.5
**
.
5
]
murange
=
[[
10
**
.
5
,
1.5
**
.
5
],
[
12
**
.
5
,
3
**
.
5
]]
elif
size
==
6
:
# taller
mu0
=
[
11
**
.
5
,
2.25
**
.
5
]
mutar
=
[
10.5
**
.
5
,
2.5
**
.
5
]
murange
=
[[
10
**
.
5
,
1.25
**
.
5
],
[
12
**
.
5
,
3.25
**
.
5
]]
elif
size
==
7
:
# low
mu0
=
[
7
**
.
5
,
.
75
**
.
5
]
mutar
=
[
8
**
.
5
,
1
**
.
5
]
murange
=
[[
6
**
.
5
,
.
25
**
.
5
],
[
8
**
.
5
,
1.25
**
.
5
]]
aEff
=
1.25
bEff
=
1.
-
aEff
murangeEff
=
[[(
aEff
*
murange
[
0
][
0
]
**
2.
+
bEff
*
murange
[
1
][
0
]
**
2.
)
**
.
5
,
(
aEff
*
murange
[
0
][
1
]
**
2.
+
bEff
*
murange
[
1
][
1
]
**
2.
)
**
.
5
],
[(
aEff
*
murange
[
1
][
0
]
**
2.
+
bEff
*
murange
[
0
][
0
]
**
2.
)
**
.
5
,
(
aEff
*
murange
[
1
][
1
]
**
2.
+
bEff
*
murange
[
0
][
1
]
**
2.
)
**
.
5
]]
solver
=
HSDPE
(
kappa
=
2.5
,
theta
=
np
.
pi
/
3
,
mu0
=
mu0
,
n
=
20
,
verbosity
=
verb
)
if
ignore_forcing
:
solver
.
nbs
=
1
rescaling
=
[
lambda
x
:
np
.
power
(
x
,
2.
)]
*
2
rescalingInv
=
[
lambda
x
:
np
.
power
(
x
,
.
5
)]
*
2
if
algo
==
"rational"
:
params
=
{
'N'
:
MN
,
'M'
:
MN
,
'S'
:
S
,
'POD'
:
True
}
if
samples
==
"distributed"
:
params
[
'polybasis'
]
=
"CHEBYSHEV"
params
[
'polybasis'
]
=
"LEGENDRE"
params
[
'polybasis'
]
=
"MONOMIAL"
params
[
'E'
]
=
MN
method
=
RI
elif
samples
==
"centered_fake"
:
params
[
'polybasis'
]
=
"MONOMIAL"
params
[
'S'
]
=
R
method
=
RI
else
:
params
[
'S'
]
=
R
method
=
RP
else
:
#if algo == "RB":
params
=
{
'R'
:
R
,
'S'
:
S
,
'POD'
:
True
}
if
samples
==
"distributed"
:
method
=
RBD
elif
samples
==
"centered_fake"
:
params
[
'S'
]
=
R
method
=
RBD
else
:
params
[
'S'
]
=
R
method
=
RBC
if
samples
==
"distributed"
:
if
sampling
==
"quadrature"
:
params
[
'sampler'
]
=
QS
(
murange
,
"CHEBYSHEV"
,
scaling
=
rescaling
,
scalingInv
=
rescalingInv
)
# params['sampler'] = QS(murange, "GAUSSLEGENDRE", scaling = rescaling,
# scalingInv = rescalingInv)
# params['sampler'] = QS(murange, "UNIFORM", scaling = rescaling,
# scalingInv = rescalingInv)
params
[
'S'
]
=
[
max
(
j
,
MN
+
1
)
for
j
in
params
[
'S'
]]
elif
sampling
==
"quadrature_total"
:
params
[
'sampler'
]
=
QST
(
murange
,
"CHEBYSHEV"
,
scaling
=
rescaling
,
scalingInv
=
rescalingInv
)
params
[
'S'
]
=
R
else
:
# if sampling == "random":
params
[
'sampler'
]
=
RS
(
murange
,
"HALTON"
,
scaling
=
rescaling
,
scalingInv
=
rescalingInv
)
params
[
'S'
]
=
R
elif
samples
==
"centered_fake"
:
params
[
'sampler'
]
=
MS
(
murange
,
points
=
[
mu0
],
scaling
=
rescaling
,
scalingInv
=
rescalingInv
)
approx
=
method
(
solver
,
mu0
=
mu0
,
approxParameters
=
params
,
verbosity
=
verb
)
approx
.
setupApprox
()
if
show_sample
:
approx
.
plotApprox
(
mutar
,
name
=
'u_app'
)
approx
.
plotHF
(
mutar
,
name
=
'u_HF'
)
approx
.
plotErr
(
mutar
,
name
=
'err'
)
approx
.
plotRes
(
mutar
,
name
=
'res'
)
appErr
,
solNorm
=
approx
.
normErr
(
mutar
),
approx
.
normHF
(
mutar
)
resNorm
,
RHSNorm
=
approx
.
normRes
(
mutar
),
approx
.
normRHS
(
mutar
)
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
algo
==
"rational"
:
from
plot_zero_set
import
plotZeroSet
plotZeroSet
(
murangeEff
,
approx
,
mu0
,
100
,
[
2.
,
2.
])
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