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ProxPython
Commits
9813cbd6
Commit
9813cbd6
authored
Nov 09, 2019
by
markus.meier01
Browse files
Uploaded CDP_processor_ADMM, slight changes in CDP_source_loc_processor
parent
7c1b2cc7
Changes
2
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proxtoolbox/Problems/Phase/CDP_processor_ADMM.py
0 → 100644
View file @
9813cbd6
from
numpy.random
import
randn
,
random_sample
from
numpy.linalg
import
norm
from
numpy.fft
import
fft
,
ifft
,
ifft2
,
fft2
from
numpy
import
sqrt
,
conj
,
tile
,
mean
import
random
import
numpy
as
np
def
CDP_processor_ADMM
(
config
):
# Implementation of the Wirtinger Flow (WF) algorithm presented in the paper
# "Phase Retrieval via Wirtinger Flow: Theory and Algorithms"
# by E. J. Candes, X. Li, and M. Soltanolkotabi
# integrated into the ProxToolbox by
# Russell Luke, September 2016.
# The input data are coded diffraction patterns about a random complex
# valued image.
## Make image
n1
=
config
[
'Ny'
]
n2
=
config
[
'Nx'
]
# for 1D signals, this will be 1
x
=
randn
(
n1
,
n2
)
+
1j
*
randn
(
n1
,
n2
)
config
[
'truth'
]
=
x
config
[
'norm_truth'
]
=
norm
(
x
,
'fro'
)
config
[
'truth_dim'
]
=
x
.
shape
## Make masks and linear sampling operators
L
=
config
[
'product_space_dimension'
]
# Number of masks
Randomarray
=
[
1j
,

1j
,
1
,

1
]
if
n2
==
1
:
Masks
=
np
.
random
.
choice
(
Randomarray
,
(
n1
,
L
))
elif
n1
==
1
:
Masks
=
np
.
random
.
choice
(
Randomarray
,
(
L
,
n2
))
else
:
Masks
=
np
.
zeros
(
n1
,
n2
,
L
)
# Storage for L masks, each of dim n1 x n2
# Sample phases: each symbol in alphabet {1, 1, i , i} has equal prob
for
ll
in
range
(
L
):
Masks
[:,:,
ll
]
=
np
.
random
.
choice
(
Randomarray
,
(
n1
,
n2
))
# Sample magnitudes and make masks
temp
=
random_sample
(
Masks
.
shape
)
Masks
=
Masks
*
((
temp
<=
0.2
)
*
sqrt
(
3
)
+
(
temp
>
0.2
)
/
sqrt
(
2
))
config
[
'Masks'
]
=
conj
(
Masks
)
# Saving the conjugate of the mask saves on computing the conjugate
# every time the mapping A (below) is applied.
if
n2
==
1
:
# Make linear operators; A is forward map and At its scaled adjoint (At(Y)*numel(Y) is the adjoint)
A
=
lambda
I
:
fft
(
conj
(
Masks
))
*
tile
(
I
,
[
1
,
L
])
# Input is n x 1 signal, output is n x L array
At
=
lambda
Y
:
mean
(
Masks
*
ifft
(
Y
),
axis
=
1
)
# Input is n x L array, output is n x 1 signal
elif
n1
==
1
:
# Make linear operators; A is forward map and At its scaled adjoint (At(Y)*numel(Y) is the adjoint)
A
=
lambda
I
:
fft
(
conj
(
Masks
))
*
tile
(
I
,
[
L
,
1
])
# Input is 1 x n signal, output is L x n array
At
=
lambda
Y
:
mean
(
Masks
*
ifft
(
y
),
axis
=
0
)
# Input is L x n array, output is 1 x n signal
else
:
A
=
lambda
I
:
fft2
(
config
[
'Masks'
]
*
reshape
(
tile
(
I
,
[
1
,
L
]),
[
I
.
shape
[
0
],
I
.
shape
[
1
],
L
]))
# Input is n1 x n2 image, output is n1 x n2 x L array
At
=
lambda
Y
:
mean
(
Masks
*
ifft2
(
Y
),
2
)
# Input is n1 x n2 X L array, output is n1 x n2 image
# support constraint: none
config
[
'indicator_ampl'
]
=
1
config
[
'abs_illumination'
]
=
1
# Not sure if the following code is correct or if it is needed
# It is supposed to be the equivalent to input.support_idx=find(x~=0)
# in matlab which searches x for all nonzero elements and puts the
# corresponding indices in a vector. The indices are calculated as if x was a column vector
nonzerovector
=
x
.
nonzero
()
# Puts the indices of non zero elements in an array
nonzerovector
=
nonzerovector
.
transpose
#transposes the array
for
i
in
range
(
nonzerovector
.
shape
[
0
]):
config
[
'support_idx'
][
i
]
=
nonzerovector
[
i
,
1
]
*
x
.
shape
[
0
]
+
nonzerovector
[
i
,
0
]
# Is supposed to change the array output into the equivalent column vector indices
# Data
Y
=
abs
(
A
(
x
))
config
[
'rt_data'
]
=
Y
Y
=
Y
**
2
config
[
'data'
]
=
Y
config
[
'norm_data'
]
=
(
sum
(
Y
.
flatten
()))
/
Y
.
size
normest
=
sqrt
(
config
[
'norm_data'
])
# Estimate norm to scale eigenvector
config
[
'norm_rt_data'
]
=
normest
## Initialization
npower_iter
=
config
[
'warmup_iter'
]
# Number of power iterations
z0
=
randn
(
n1
,
n2
)
z0
=
z0
/
norm
(
n0
,
'fro'
)
# Initial guess
start_time
=
time
.
time
()
# Power iterations
for
tt
in
range
(
npower_iter
):
z0
=
At
(
Y
*
A
(
z0
))
z0
=
z0
/
norm
(
z0
,
'fro'
)
end_time
=
time
.
time
()
Times
=
end_time

start_time
z
=
normest
*
z0
# Apply scaling
if
n2
==
1
:
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
x
*
z
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
tile
(
z
,
[
1
,
L
])
elif
n1
==
1
:
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
z
*
x
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
tile
(
z
,
[
L
,
1
])
else
:
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
x
*
z
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
reshape
(
tile
(
z
,[
1
,
L
]),
[
z
[
0
].
size
,
z
[
1
].
size
,
L
])
print
(
'Run time of initialization: %.2f seconds '
,
Times
)
print
(
'Relative error after initialization: %.2f '
,
Relerrs
)
proxtoolbox/Problems/Phase/CDP_source_loc_processor.py
View file @
9813cbd6
from
numpy.random
import
randn
,
random_sample
from
numpy.linalg
import
norm
from
numpy.fft
import
fft
,
ifft
,
fft2
,
ifft2
from
numpy
import
sqrt
,
conj
,
reshape
,
tile
,
mean
,
angle
,
trace
from
numpy
import
sqrt
,
conj
,
reshape
,
tile
,
mean
,
angle
,
trace
,
nonzero
,
transpose
import
numpy
as
np
import
random
...
...
@@ 10,6 +10,8 @@ def CDP_source_loc_processor(config):
# Implementation of the Wirtinger Flow (WF) algorithm presented in the paper
# "Phase Retrieval via Wirtinger Flow: Theory and Algorithms"
# by E. J. Candes, X. Li, and M. Soltanolkotabi
# integrated into the ProxToolbox by
# Russell Luke, September 2016.
# The input data are coded diffraction patterns about a random complex
# valued image.
...
...
@@ 58,9 +60,23 @@ def CDP_source_loc_processor(config):
# support constraint: none
config
[
'indicator_ampl'
]
=
1
config
[
'abs_illumination'
]
=
1
config
[
'Illumination'
]
=
1
##suport_idx is missing###
config
[
'Illumination'
]
=
1
# Not sure if the following code is correct or if it is needed
# It is supposed to be the equivalent to input.support_idx=find(x~=0)
# in matlab which searches x for all nonzero elements and puts the
# corresponding indices in a vector. The indices are calculated as if x was a column vector
nonzerovector
=
x
.
nonzero
()
# Puts the indices of non zero elements in an array
nonzerovector
=
nonzerovector
.
transpose
#transposes the array
for
i
in
range
(
nonzerovector
.
shape
[
0
]):
config
[
'support_idx'
][
i
]
=
nonzerovector
[
i
,
1
]
*
x
.
shape
[
0
]
+
nonzerovector
[
i
,
0
]
# Is supposed to change the array output into the equivalent column vector indices
# Data
Y
=
abs
(
A
(
x
))
config
[
'rt_data'
]
=
Y
...
...
@@ 87,9 +103,16 @@ def CDP_source_loc_processor(config):
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
x
*
z
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
tile
(
z
,[
1
,
L
])
truth
=
A
(
x
)
config
[
'truth'
]
=
truth
[:,
0
]
####?####
config
[
'truth'
]
=
truth
[:,
0
]
config
[
'norm_truth'
]
=
norm
(
truth
[:,
0
],
'fro'
)
config
[
'truth_dim'
]
=
truth
[
1
,:].
size
elif
n1
==
1
:
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
z
*
x
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
tile
(
z
,
[
L
,
1
])
truth
=
A
(
x
)
config
[
'truth'
]
=
truth
[:,
0
]
config
[
'norm_truth'
]
=
norm
(
truth
[
0
,:],
'fro'
)
config
[
'truth_dim'
]
=
truth
[
0
,:].
size
else
:
Relerrs
=
norm
(
x

exp
(

1j
*
angle
(
trace
(
x
*
z
)))
*
z
,
'fro'
)
/
norm
(
x
,
'fro'
)
config
[
'u_0'
]
=
reshape
(
tile
(
z
,[
1
,
L
]),[
z
[
0
].
shape
,
z
[
1
].
shape
,
L
])
...
...
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