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pod.py
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Created
Tue, Apr 30, 14:09
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5 KB
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text/x-python
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Thu, May 2, 14:09 (1 d, 23 h)
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17359194
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R6746 RationalROMPy
pod.py
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import
numpy
as
np
from
diapason_engine
import
DiapasonEngine
,
DiapasonEngineDamped
from
rrompy.reduction_methods.standard
import
RationalInterpolant
as
Pade
from
rrompy.reduction_methods.standard
import
ReducedBasis
as
RB
from
rrompy.parameter.parameter_sampling
import
QuadratureSampler
as
QS
verb
=
100
sol
=
"single"
sol
=
"sweep"
algo
=
"Pade"
#algo = "RB"
polyBasis
=
"LEGENDRE"
polyBasis
=
"CHEBYSHEV"
#polyBasis = "MONOMIAL"
dampingEta
=
0.
*
1e4
/
2.
/
np
.
pi
ktar
=
1.e4
# [Hz]
k0s
=
[
2.5e2
,
1.0e4
]
#k0s = np.array([2.5e3, 1.5e4])
#k0s = np.array([5.0e4, 1.0e5])
k0s
=
[
2.0e5
,
3.0e5
]
k0
=
np
.
mean
(
np
.
power
(
k0s
,
2.
))
**
.
5
theta
=
20.
*
np
.
pi
/
180.
phi
=
10.
*
np
.
pi
/
180.
c
=
3.e2
rho
=
8e3
*
(
2.
*
np
.
pi
)
**
2.
E
=
1.93e11
nu
=
.
3
T
=
1e6
###
if
np
.
isclose
(
dampingEta
,
0.
):
rescalingExp
=
2.
solver
=
DiapasonEngine
(
kappa
=
k0
,
c
=
c
,
rho
=
rho
,
E
=
E
,
nu
=
nu
,
T
=
T
,
theta
=
theta
,
phi
=
phi
,
meshNo
=
1
,
degree_threshold
=
8
,
verbosity
=
0
)
else
:
rescalingExp
=
1.
solver
=
DiapasonEngineDamped
(
kappa
=
k0
,
c
=
c
,
rho
=
rho
,
E
=
E
,
nu
=
nu
,
T
=
T
,
theta
=
theta
,
phi
=
phi
,
dampingEta
=
dampingEta
,
meshNo
=
1
,
degree_threshold
=
8
,
verbosity
=
0
)
params
=
{
'N'
:
39
,
'M'
:
39
,
'S'
:
40
,
'POD'
:
True
,
'polybasis'
:
polyBasis
,
'sampler'
:
QS
(
k0s
,
"CHEBYSHEV"
,
rescalingExp
)}
#,
# 'robustTol':1e-16}
if
algo
==
"Pade"
:
approx
=
Pade
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
else
:
params
.
pop
(
"N"
)
params
.
pop
(
"M"
)
params
.
pop
(
"polybasis"
)
# params.pop("robustTol")
approx
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
approx
.
setupApprox
()
if
sol
==
"single"
:
approx
.
outParaviewTimeDomainSamples
(
filename
=
"out/outSamples{}"
.
format
(
dampingEta
),
forceNewFile
=
False
,
folders
=
True
)
nameBase
=
"{}_{}"
.
format
(
ktar
,
dampingEta
)
approx
.
outParaviewTimeDomainApprox
(
ktar
,
omega
=
2.
*
np
.
pi
*
ktar
,
filename
=
"out/outTApp{}"
.
format
(
nameBase
),
forceNewFile
=
False
,
folder
=
True
)
approx
.
outParaviewTimeDomainHF
(
ktar
,
omega
=
2.
*
np
.
pi
*
ktar
,
filename
=
"out/outTHF{}"
.
format
(
nameBase
),
forceNewFile
=
False
,
folder
=
True
)
approx
.
outParaviewTimeDomainErr
(
ktar
,
omega
=
2.
*
np
.
pi
*
ktar
,
filename
=
"out/outTErr{}"
.
format
(
nameBase
),
forceNewFile
=
False
,
folder
=
True
)
approx
.
outParaviewTimeDomainRes
(
ktar
,
omega
=
2.
*
np
.
pi
*
ktar
,
filename
=
"out/outTRes{}"
.
format
(
nameBase
),
forceNewFile
=
False
,
folder
=
True
)
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
)))
poles
=
approx
.
getPoles
()
print
(
'Poles:'
,
poles
)
if
sol
==
"sweep"
:
k0s
=
np
.
linspace
(
k0s
[
0
],
k0s
[
1
],
100
)
kl
,
kr
=
min
(
k0s
),
max
(
k0s
)
approx
.
samplingEngine
.
verbosity
=
0
approx
.
trainedModel
.
verbosity
=
0
approx
.
verbosity
=
0
kl
,
kr
=
np
.
real
(
kl
),
np
.
real
(
kr
)
from
matplotlib
import
pyplot
as
plt
normApp
=
np
.
zeros
(
len
(
k0s
))
norm
=
np
.
zeros_like
(
normApp
)
err
=
np
.
zeros_like
(
normApp
)
res
=
np
.
zeros_like
(
normApp
)
# errApp = np.zeros_like(normApp)
fNorm
=
approx
.
normRHS
(
k0s
[
0
])
for
j
in
range
(
len
(
k0s
)):
normApp
[
j
]
=
approx
.
normApprox
(
k0s
[
j
])
norm
[
j
]
=
approx
.
normHF
(
k0s
[
j
])
err
[
j
]
=
approx
.
normErr
(
k0s
[
j
])
/
norm
[
j
]
res
[
j
]
=
approx
.
normRes
(
k0s
[
j
])
/
fNorm
# errApp[j] = res[j] / np.min(np.abs(k0s[j] - poles))
# errApp *= np.mean(err) / np.mean(errApp)
plt
.
figure
()
plt
.
semilogy
(
k0s
,
norm
)
plt
.
semilogy
(
k0s
,
normApp
,
'--'
)
plt
.
semilogy
(
np
.
real
(
approx
.
mus
),
1.05
*
np
.
max
(
norm
)
*
np
.
ones_like
(
approx
.
mus
,
dtype
=
float
),
'rx'
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
res
)
plt
.
xlim
([
kl
,
kr
])
plt
.
grid
()
plt
.
show
()
plt
.
close
()
plt
.
figure
()
plt
.
semilogy
(
k0s
,
err
)
# plt.semilogy(k0s, errApp)
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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