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squareTransmissionNonHomog.py
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Created
Sun, Sep 1, 15:33
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3 KB
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text/x-python
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Tue, Sep 3, 15:33 (1 d, 23 h)
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Attached To
R6746 RationalROMPy
squareTransmissionNonHomog.py
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import
numpy
as
np
from
rrompy.hfengines.linear_problem
import
\
HelmholtzSquareTransmissionProblemEngine
as
HSTPE
from
rrompy.reduction_methods.distributed_greedy
import
\
RationalInterpolantGreedy
as
Pade
from
rrompy.reduction_methods.distributed_greedy
import
\
RBDistributedGreedy
as
RB
from
rrompy.utilities.fenics
import
L2NormMatrix
timed
=
False
verb
=
2
algo
=
"Pade"
#algo = "RB"
polyBasis
=
"LEGENDRE"
#polyBasis = "CHEBYSHEV"
#polyBasis = "MONOMIAL"
homog
=
True
#homog = False
errorEstimatorKind
=
"BARE"
errorEstimatorKind
=
"BASIC"
#errorEstimatorKind = "EXACT"
k0s
=
np
.
power
(
np
.
linspace
(
4
,
15
,
100
),
.
5
)
k0
=
np
.
mean
(
np
.
power
(
k0s
,
2.
))
**
.
5
kl
,
kr
=
min
(
k0s
),
max
(
k0s
)
rescaling
=
lambda
x
:
np
.
power
(
x
,
2.
)
rescalingInv
=
lambda
x
:
np
.
power
(
x
,
.
5
)
params
=
{
'muBounds'
:[
kl
,
kr
],
'nTestPoints'
:
500
,
'Delta'
:
0
,
'greedyTol'
:
1e-2
,
'S'
:
5
,
'polybasis'
:
polyBasis
,
'errorEstimatorKind'
:
errorEstimatorKind
}
solver
=
HSTPE
(
nT
=
1
,
nB
=
2
,
theta
=
np
.
pi
*
20
/
180
,
kappa
=
4.
,
n
=
20
,
verbosity
=
verb
)
solver
.
omega
=
np
.
real
(
k0
)
if
algo
==
"Pade"
:
approx
=
Pade
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
,
homogeneized
=
homog
)
else
:
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
,
homogeneized
=
homog
)
approx
.
initEstimatorNormEngine
(
L2NormMatrix
(
solver
.
V
))
if
timed
:
from
time
import
clock
start_time
=
clock
()
approx
.
greedy
()
print
(
"Time: "
,
clock
()
-
start_time
)
else
:
approx
.
greedy
(
True
)
approx
.
samplingEngine
.
verbosity
=
0
approx
.
verbosity
=
0
from
matplotlib
import
pyplot
as
plt
normApp
=
np
.
zeros
(
len
(
k0s
))
norm
=
np
.
zeros_like
(
normApp
)
res
=
np
.
zeros_like
(
normApp
)
err
=
np
.
zeros_like
(
normApp
)
for
j
in
range
(
len
(
k0s
)):
normApp
[
j
]
=
approx
.
normApprox
(
k0s
[
j
])
norm
[
j
]
=
approx
.
normHF
(
k0s
[
j
])
res
[
j
]
=
(
approx
.
estimatorNormEngine
.
norm
(
approx
.
getRes
(
k0s
[
j
],
homogeneized
=
homog
))
/
approx
.
estimatorNormEngine
.
norm
(
approx
.
getRHS
(
k0s
[
j
],
homogeneized
=
homog
)))
err
[
j
]
=
approx
.
normErr
(
k0s
[
j
])
/
approx
.
normHF
(
k0s
[
j
])
resApp
=
approx
.
errorEstimator
(
k0s
)
polesApp
=
approx
.
getPoles
()
polesApp
=
polesApp
[
np
.
abs
(
np
.
imag
(
polesApp
))
<
1e-3
]
plt
.
figure
()
plt
.
semilogy
(
k0s
,
norm
)
plt
.
semilogy
(
k0s
,
normApp
,
'--'
)
plt
.
semilogy
(
np
.
real
(
approx
.
mus
),
4.
*
np
.
max
(
norm
)
*
np
.
ones_like
(
approx
.
mus
,
dtype
=
float
),
'rx'
)
plt
.
semilogy
(
np
.
real
(
polesApp
),
2.
*
np
.
max
(
norm
)
*
np
.
ones_like
(
polesApp
,
dtype
=
float
),
'k.'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
res
)
plt
.
semilogy
(
k0s
,
resApp
,
'--'
)
plt
.
semilogy
(
np
.
real
(
approx
.
mus
),
4.
*
np
.
max
(
resApp
)
*
np
.
ones_like
(
approx
.
mus
,
dtype
=
float
),
'rx'
)
plt
.
semilogy
(
np
.
real
(
polesApp
),
2.
*
np
.
max
(
resApp
)
*
np
.
ones_like
(
polesApp
,
dtype
=
float
),
'k.'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
err
)
plt
.
semilogy
(
np
.
real
(
polesApp
),
2.
*
np
.
max
(
err
)
*
np
.
ones_like
(
polesApp
,
dtype
=
float
),
'k.'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
polesApp
=
approx
.
getPoles
()
mask
=
(
np
.
real
(
polesApp
)
<
kl
)
|
(
np
.
real
(
polesApp
)
>
kr
)
print
(
"Outliers:"
,
polesApp
[
mask
])
polesApp
=
polesApp
[
~
mask
]
plt
.
figure
()
plt
.
plot
(
np
.
real
(
polesApp
),
np
.
imag
(
polesApp
),
'kx'
)
plt
.
axis
(
'equal'
)
plt
.
grid
()
plt
.
show
()
plt
.
close
()
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