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TaylorPoles.py
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Created
Mon, Aug 26, 04:05
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2 KB
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text/x-python
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Wed, Aug 28, 04:05 (2 d)
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Attached To
R6746 RationalROMPy
TaylorPoles.py
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import
numpy
as
np
from
rrompy.hfengines.scipy
import
HelmholtzSquareBubbleProblemEngine
as
HSBPE
from
rrompy.reduction_methods.taylor
import
ApproximantTaylorPade
as
Pade
from
rrompy.reduction_methods.taylor
import
ApproximantTaylorRB
as
RB
from
rrompy.utilities.base
import
squareResonances
verb
=
0
k0
=
(
12
+
0.j
)
**
.
5
Nmin
,
Nmax
=
2
,
10
Nvals
=
np
.
arange
(
Nmin
,
Nmax
+
1
,
2
)
params
=
{
'N'
:
Nmin
,
'M'
:
0
,
'Emax'
:
Nmax
,
'POD'
:
True
,
'sampleType'
:
'Arnoldi'
}
#, 'robustTol':1e-14}
#boolCon = lambda x : np.abs(np.imag(x)) < 1e-1 * np.abs(np.real(x)
# - np.real(z0))
#cleanupParameters = {'boolCondition':boolCon, 'residueCheck':True}
solver
=
HSBPE
(
kappa
=
12
**
.
5
,
theta
=
np
.
pi
/
3
,
n
=
25
,
verbosity
=
verb
)
solver
.
omega
=
np
.
real
(
k0
)
approxP
=
Pade
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
#,
# equilibration = True, cleanupParameters = cleanupParameters)
approxR
=
RB
(
solver
,
mu0
=
k0
,
approxParameters
=
params
,
verbosity
=
verb
)
rP
,
rE
=
[
None
]
*
len
(
Nvals
),
[
None
]
*
len
(
Nvals
)
verbose
=
1
for
j
,
N
in
enumerate
(
Nvals
):
if
verbose
>
0
:
print
(
'N = E = {}'
.
format
(
N
))
approxP
.
approxParameters
=
{
'N'
:
N
,
'E'
:
N
}
approxR
.
approxParameters
=
{
'R'
:
N
,
'E'
:
N
}
if
verbose
>
1
:
print
(
approxP
.
approxParameters
)
print
(
approxR
.
approxParameters
)
rP
[
j
]
=
approxP
.
getPoles
()
rE
[
j
]
=
approxR
.
getPoles
()
if
verbose
>
2
:
print
(
rP
)
print
(
rE
)
from
matplotlib
import
pyplot
as
plt
plotRows
=
int
(
np
.
ceil
(
len
(
Nvals
)
/
3
))
fig
,
axes
=
plt
.
subplots
(
plotRows
,
3
,
figsize
=
(
15
,
3.5
*
plotRows
))
for
j
,
N
in
enumerate
(
Nvals
):
i1
,
i2
=
int
(
np
.
floor
(
j
/
3
)),
j
%
3
axes
[
i1
,
i2
]
.
set_title
(
'N = E = {}'
.
format
(
N
))
axes
[
i1
,
i2
]
.
plot
(
np
.
real
(
rP
[
j
]),
np
.
imag
(
rP
[
j
]),
'Xb'
,
label
=
"Pade'"
,
markersize
=
8
)
axes
[
i1
,
i2
]
.
plot
(
np
.
real
(
rE
[
j
]),
np
.
imag
(
rE
[
j
]),
'Pr'
,
label
=
"RB"
,
markersize
=
8
)
axes
[
i1
,
i2
]
.
axhline
(
linewidth
=
1
,
color
=
'k'
)
xmin
,
xmax
=
axes
[
i1
,
i2
]
.
get_xlim
()
height
=
(
xmax
-
xmin
)
/
2.
res
=
np
.
power
(
squareResonances
(
xmin
**
2.
,
xmax
**
2.
,
False
),
.
5
)
axes
[
i1
,
i2
]
.
plot
(
res
,
np
.
zeros_like
(
res
),
'ok'
,
markersize
=
4
)
axes
[
i1
,
i2
]
.
plot
(
np
.
real
(
k0
),
np
.
imag
(
k0
),
'om'
,
markersize
=
5
)
axes
[
i1
,
i2
]
.
plot
(
np
.
real
(
k0
)
*
np
.
ones
(
2
),
1.5
*
height
*
np
.
arange
(
-
1
,
3
,
2
),
'--m'
)
axes
[
i1
,
i2
]
.
grid
()
axes
[
i1
,
i2
]
.
set_xlim
(
xmin
,
xmax
)
axes
[
i1
,
i2
]
.
set_ylim
(
-
height
,
height
)
p
=
axes
[
i1
,
i2
]
.
legend
()
plt
.
tight_layout
()
for
j
in
range
((
len
(
Nvals
)
-
1
)
%
3
+
1
,
3
):
axes
[
plotRows
-
1
,
j
]
.
axis
(
'off'
)
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