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ift
NIFTy
Commits
dcf8a19c
Commit
dcf8a19c
authored
May 16, 2017
by
Martin Reinecke
Browse files
remove spurious file
parent
f35278d7
Pipeline
#12514
passed with stage
in 5 minutes and 39 seconds
Changes
1
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
demos/wiener_filter_harmonic - Kopie.py
deleted
100644 → 0
View file @
f35278d7
from
nifty
import
*
from
mpi4py
import
MPI
import
plotly.offline
as
py
import
plotly.graph_objs
as
go
comm
=
MPI
.
COMM_WORLD
rank
=
comm
.
rank
def
plot_maps
(
x
,
name
):
trace
=
[
None
]
*
len
(
x
)
keys
=
x
.
keys
()
field
=
x
[
keys
[
0
]]
domain
=
field
.
domain
[
0
]
shape
=
len
(
domain
.
shape
)
max_n
=
domain
.
shape
[
0
]
*
domain
.
distances
[
0
]
step
=
domain
.
distances
[
0
]
x_axis
=
np
.
arange
(
0
,
max_n
,
step
)
if
shape
==
1
:
for
ii
in
xrange
(
len
(
x
)):
trace
[
ii
]
=
go
.
Scatter
(
x
=
x_axis
,
y
=
x
[
keys
[
ii
]].
val
.
get_full_data
(),
name
=
keys
[
ii
])
fig
=
go
.
Figure
(
data
=
trace
)
py
.
plot
(
fig
,
filename
=
name
)
elif
shape
==
2
:
for
ii
in
xrange
(
len
(
x
)):
py
.
plot
([
go
.
Heatmap
(
z
=
x
[
keys
[
ii
]].
val
.
get_full_data
().
real
)],
filename
=
keys
[
ii
])
else
:
raise
TypeError
(
"Only 1D and 2D field plots are supported"
)
def
plot_power
(
x
,
name
):
layout
=
go
.
Layout
(
xaxis
=
dict
(
type
=
'log'
,
autorange
=
True
),
yaxis
=
dict
(
type
=
'log'
,
autorange
=
True
)
)
trace
=
[
None
]
*
len
(
x
)
keys
=
x
.
keys
()
field
=
x
[
keys
[
0
]]
domain
=
field
.
domain
[
0
]
x_axis
=
domain
.
kindex
for
ii
in
xrange
(
len
(
x
)):
trace
[
ii
]
=
go
.
Scatter
(
x
=
x_axis
,
y
=
x
[
keys
[
ii
]].
val
.
get_full_data
(),
name
=
keys
[
ii
])
fig
=
go
.
Figure
(
data
=
trace
,
layout
=
layout
)
py
.
plot
(
fig
,
filename
=
name
)
np
.
random
.
seed
(
42
)
if
__name__
==
"__main__"
:
distribution_strategy
=
'not'
# setting spaces
npix
=
np
.
array
([
500
])
# number of pixels
total_volume
=
1.
# total length
# setting signal parameters
lambda_s
=
.
05
# signal correlation length
sigma_s
=
10.
# signal variance
#setting response operator parameters
length_convolution
=
.
025
exposure
=
1.
# calculating parameters
k_0
=
4.
/
(
2
*
np
.
pi
*
lambda_s
)
a_s
=
sigma_s
**
2.
*
lambda_s
*
total_volume
# creation of spaces
# x1 = RGSpace([npix,npix], distances=total_volume / npix,
# zerocenter=False)
# k1 = RGRGTransformation.get_codomain(x1)
x1
=
HPSpace
(
32
)
k1
=
HPLMTransformation
.
get_codomain
(
x1
)
p1
=
PowerSpace
(
harmonic_partner
=
k1
,
logarithmic
=
False
)
# creating Power Operator with given spectrum
spec
=
(
lambda
k
:
a_s
/
(
1
+
(
k
/
k_0
)
**
2
)
**
2
)
p_field
=
Field
(
p1
,
val
=
spec
)
S_op
=
create_power_operator
(
k1
,
spec
)
# creating FFT-Operator and Response-Operator with Gaussian convolution
Fft_op
=
FFTOperator
(
domain
=
x1
,
target
=
k1
,
domain_dtype
=
np
.
float64
,
target_dtype
=
np
.
complex128
)
R_op
=
ResponseOperator
(
x1
,
sigma
=
[
length_convolution
],
exposure
=
[
exposure
])
# drawing a random field
sk
=
p_field
.
power_synthesize
(
real_signal
=
True
,
mean
=
0.
)
s
=
Fft_op
.
adjoint_times
(
sk
)
signal_to_noise
=
1
N_op
=
DiagonalOperator
(
R_op
.
target
,
diagonal
=
s
.
var
()
/
signal_to_noise
,
bare
=
True
)
n
=
Field
.
from_random
(
domain
=
R_op
.
target
,
random_type
=
'normal'
,
std
=
s
.
std
()
/
np
.
sqrt
(
signal_to_noise
),
mean
=
0.
)
d
=
R_op
(
s
)
+
n
# Wiener filter
j
=
Fft_op
.
times
(
R_op
.
adjoint_times
(
N_op
.
inverse_times
(
d
)))
D
=
HarmonicPropagatorOperator
(
S
=
S_op
,
N
=
N_op
,
R
=
R_op
)
mk
=
D
(
j
)
m
=
Fft_op
.
adjoint_times
(
mk
)
# z={}
# z["signal"] = s
# z["reconstructed_map"] = m
# z["data"] = d
# z["lambda"] = R_op(s)
# z["j"] = j
#
# plot_maps(z, "Wiener_filter.html")
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