Page Menu
Home
c4science
Search
Configure Global Search
Log In
Files
F66591447
squareBubbleHomog.py
No One
Temporary
Actions
Download File
Edit File
Delete File
View Transforms
Subscribe
Mute Notifications
Award Token
Subscribers
None
File Metadata
Details
File Info
Storage
Attached
Created
Tue, Jun 11, 15:56
Size
3 KB
Mime Type
text/x-python
Expires
Thu, Jun 13, 15:56 (2 d)
Engine
blob
Format
Raw Data
Handle
18245442
Attached To
R6746 RationalROMPy
squareBubbleHomog.py
View Options
import
numpy
as
np
from
rrompy.hfengines.linear_problem
import
\
HelmholtzSquareBubbleProblemEngine
as
HSBPE
from
rrompy.reduction_methods.distributed_greedy
import
\
RationalInterpolantGreedy
as
Pade
from
rrompy.reduction_methods.distributed_greedy
import
\
RBDistributedGreedy
as
RB
from
rrompy.utilities.base
import
squareResonances
from
rrompy.solver.fenics
import
L2NormMatrix
verb
=
2
timed
=
False
algo
=
"Pade"
#algo = "RB"
polyBasis
=
"LEGENDRE"
#polyBasis = "CHEBYSHEV"
#polyBasis = "MONOMIAL"
errorEstimatorKind
=
"BARE"
#errorEstimatorKind = "BASIC"
#errorEstimatorKind = "EXACT"
k0s
=
np
.
power
(
np
.
linspace
(
95
,
149
,
250
),
.
5
)
#k0s = np.power(np.linspace(95, 129, 100), .5)
#k0s = np.power(np.linspace(95, 109, 100), .5)
k0
=
np
.
mean
(
np
.
power
(
k0s
,
2.
))
**
.
5
kl
,
kr
=
min
(
k0s
),
max
(
k0s
)
polesexact
=
np
.
unique
(
np
.
power
(
squareResonances
(
kl
**
2.
,
kr
**
2.
,
False
),
.
5
))
params
=
{
'muBounds'
:[
kl
,
kr
],
'nTestPoints'
:
500
,
'Delta'
:
0
,
'greedyTol'
:
1e-2
,
'S'
:
10
,
'polybasis'
:
polyBasis
,
'errorEstimatorKind'
:
errorEstimatorKind
,
'interactive'
:
False
}
if
timed
:
verb
=
0
solver
=
HSBPE
(
kappa
=
12
**
.
5
,
theta
=
np
.
pi
/
3
,
n
=
20
,
verbosity
=
verb
)
solver
.
omega
=
np
.
real
(
k0
)
if
algo
==
"Pade"
:
approx
=
Pade
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
else
:
params
.
pop
(
'Delta'
)
params
.
pop
(
'polybasis'
)
params
.
pop
(
'errorEstimatorKind'
)
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
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
.
trainedModel
.
verbosity
=
0
approx
.
verbosity
=
0
from
matplotlib
import
pyplot
as
plt
normApp
=
np
.
zeros_like
(
k0s
)
norm
=
np
.
zeros_like
(
k0s
)
res
=
np
.
zeros_like
(
k0s
)
err
=
np
.
zeros_like
(
k0s
)
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
]))
/
approx
.
estimatorNormEngine
.
norm
(
approx
.
getRHS
(
k0s
[
j
])))
err
[
j
]
=
approx
.
normErr
(
k0s
[
j
])
/
approx
.
normHF
(
k0s
[
j
])
resApp
=
approx
.
errorEstimator
(
k0s
)
plt
.
figure
()
plt
.
semilogy
(
k0s
,
norm
)
plt
.
semilogy
(
k0s
,
normApp
,
'--'
)
plt
.
semilogy
(
polesexact
,
2.
*
np
.
max
(
norm
)
*
np
.
ones_like
(
polesexact
,
dtype
=
float
),
'm.'
)
plt
.
semilogy
(
np
.
real
(
approx
.
mus
(
0
)),
4.
*
np
.
max
(
norm
)
*
np
.
ones
(
approx
.
mus
.
shape
,
dtype
=
float
),
'rx'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
res
)
plt
.
semilogy
(
k0s
,
resApp
,
'--'
)
plt
.
semilogy
(
polesexact
,
2.
*
np
.
max
(
resApp
)
*
np
.
ones_like
(
polesexact
,
dtype
=
float
),
'm.'
)
plt
.
semilogy
(
np
.
real
(
approx
.
mus
(
0
)),
4.
*
np
.
max
(
resApp
)
*
np
.
ones
(
approx
.
mus
.
shape
,
dtype
=
float
),
'rx'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
err
)
plt
.
semilogy
(
polesexact
,
2.
*
np
.
max
(
err
)
*
np
.
ones_like
(
polesexact
,
dtype
=
float
),
'm.'
)
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
.
plot
(
np
.
real
(
polesexact
),
np
.
imag
(
polesexact
),
'm.'
)
plt
.
axis
(
'equal'
)
plt
.
grid
()
plt
.
show
()
plt
.
close
()
Event Timeline
Log In to Comment