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ift
starblade
Commits
1385b975
Commit
1385b975
authored
Apr 25, 2018
by
Jakob Knollmueller
Browse files
remove stuff
parent
e091563a
Changes
4
Hide whitespace changes
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Side-by-side
demos/KL_demo.py
deleted
100644 → 0
View file @
e091563a
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2017-2018 Max-Planck-Society
# Author: Jakob Knollmueller
#
# Starblade is being developed at the Max-Planck-Institut fuer Astrophysik
import
numpy
as
np
from
astropy.io
import
fits
from
matplotlib
import
pyplot
as
plt
from
multiprocessing
import
Pool
import
nifty4
as
ift
from
nifty4.library.nonlinearities
import
PositiveTanh
import
starblade
as
sb
from
starblade.starblade_energy
import
StarbladeEnergy
from
starblade.starblade_kl
import
StarbladeKL
def
power_update
(
KL_energy
):
power
=
0.
for
energy
in
KL_energy
.
energy_list
:
power
+=
ift
.
power_analyze
(
FFT
.
inverse_times
(
energy
.
s
),
binbounds
=
p_space
.
binbounds
)
power
/=
len
(
KL_energy
.
energy_list
)
return
power
if
__name__
==
'__main__'
:
#specifying location of the input file:
path
=
'data/hst_05195_01_wfpc2_f702w_pc_sci.fits'
path
=
'data/frame-u-006174-2-0094.fits'
# path = 'data/frame-g-002821-6-0141.fits'
path
=
'data/frame-g-007812-6-0100.fits'
path
=
'data/frame-i-004874-3-0692.fits'
# data = fits.open(path)[1].data
data
=
fits
.
open
(
path
)[
0
].
data
#[1000:,1250:]
data
-=
data
.
min
()
-
0.001
# data = np.exp(2*(1.-plt.imread('data/sdss.png').T[0]))
# data = (plt.imread('data/m51_3.jpg').T[0])
# data = (plt.imread('data/12_FBP.png').T[0])
#
# data = data.clip(min=0.001)
data
=
np
.
ndarray
.
astype
(
data
,
float
)
vmin
=
np
.
log
(
data
.
min
()
+
0.01
)
vmax
=
np
.
log
(
data
.
max
())
plt
.
imsave
(
'data.png'
,
np
.
log
(
data
))
postanh
=
PositiveTanh
()
alpha
=
1.5
s_space
=
ift
.
RGSpace
(
data
.
shape
,
distances
=
len
(
data
.
shape
)
*
[
1
])
h_space
=
s_space
.
get_default_codomain
()
data
=
ift
.
Field
(
s_space
,
val
=
data
)
FFT
=
ift
.
FFTOperator
(
h_space
,
target
=
s_space
)
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
h_space
,
logarithmic
=
False
)
p_space
=
ift
.
PowerSpace
(
h_space
,
binbounds
=
binbounds
)
initial_spectrum
=
ift
.
power_analyze
(
FFT
.
inverse_times
(
ift
.
log
(
data
)),
binbounds
=
p_space
.
binbounds
)
initial_spectrum
/=
(
p_space
.
k_lengths
+
1.
)
**
4
update_power
=
True
initial_x
=
ift
.
Field
(
s_space
,
val
=-
1.
)
alpha
=
ift
.
Field
(
s_space
,
val
=
alpha
)
q
=
ift
.
Field
(
s_space
,
val
=
1e-30
)
ICI
=
ift
.
GradientNormController
(
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-3
)
inverter
=
ift
.
ConjugateGradient
(
controller
=
ICI
)
parameters
=
dict
(
data
=
data
,
power_spectrum
=
initial_spectrum
,
alpha
=
alpha
,
q
=
q
,
inverter
=
inverter
,
FFT
=
FFT
,
newton_iterations
=
5
,
update_power
=
update_power
)
current_x
=
initial_x
for
i
in
range
(
10
):
Starblade
=
StarbladeEnergy
(
position
=
current_x
,
parameters
=
parameters
)
samples
=
[]
for
i
in
range
(
3
):
sample
=
Starblade
.
curvature
.
inverse
.
draw_sample
()
samples
.
append
(
sample
)
problem
=
StarbladeKL
(
current_x
,
samples
,
parameters
)
controller
=
ift
.
GradientNormController
(
name
=
"Newton"
,
tol_abs_gradnorm
=
1e-5
,
iteration_limit
=
5
)
minimizer
=
ift
.
RelaxedNewton
(
controller
=
controller
)
problem
,
convergence
=
minimizer
(
problem
)
current_x
=
problem
.
position
parameters
[
'power_spectrum'
]
=
power_update
(
problem
)
Starblade
=
StarbladeEnergy
(
position
=
current_x
,
parameters
=
parameters
)
# Starblade = sb.build_starblade(data, alpha=alpha)
# for i in range(10):
# Starblade = sb.starblade_iteration(Starblade)
#
# #plotting on logarithmic scale
plt
.
imsave
(
'diffuse_component.png'
,
(
Starblade
.
s
).
val
,
vmin
=
vmin
,
vmax
=
vmax
)
plt
.
imsave
(
'pointlike_component.png'
,
Starblade
.
u
.
val
,
vmin
=
vmin
,
vmax
=
vmax
)
Starblade
=
StarbladeEnergy
(
position
=
current_x
,
parameters
=
parameters
)
var
=
0.
mean
=
0
samps
=
30
for
i
in
range
(
samps
):
sam
=
postanh
(
Starblade
.
position
+
Starblade
.
curvature
.
inverse
.
draw_sample
())
mean
+=
sam
var
+=
sam
**
2
var
/=
samps
mean
/=
samps
var
-=
mean
**
2
mask
=
ift
.
sqrt
(
var
)
<
0.01
+
0.
plt
.
imsave
(
'masked_points.png'
,
mask
.
val
*
Starblade
.
u
.
val
,
vmin
=
vmin
,
vmax
=
vmax
)
plt
.
imsave
(
'masked_diffuse.png'
,
mask
.
val
*
Starblade
.
s
.
val
)
plt
.
imsave
(
'std.png'
,
np
.
log
(
np
.
sqrt
(
var
.
val
)
*
data
.
val
),
vmin
=-
3.3
)
# plt.figure()
# k_lenghts = Starblade.power_spectrum.domain[0].k_lengths
# plt.plot(k_lenghts, Starblade.power_spectrum.val)
# plt.title('power spectrum')
# plt.yscale('log')
# plt.xscale('log')
# plt.ylabel('power')
# plt.xscale('harmonic mode')
# plt.savefig('power_spectrum.png')
demos/clipping.py
deleted
100644 → 0
View file @
e091563a
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2017-2018 Max-Planck-Society
# Author: Jakob Knollmueller
#
# Starblade is being developed at the Max-Planck-Institut fuer Astrophysik
import
numpy
as
np
from
astropy.io
import
fits
from
matplotlib
import
pyplot
as
plt
from
scipy.ndimage.filters
import
median_filter
import
starblade
as
sb
if
__name__
==
'__main__'
:
#specifying location of the input file:
# path = 'data/hst_05195_01_wfpc2_f702w_pc_sci.fits'
# data = fits.open(path)[1].data
path
=
'data/frame-i-004874-3-0692.fits'
path
=
'data/check.fits'
# data = fits.open(path)[1].data
data
=
fits
.
open
(
path
)[
0
].
data
[
1000
:,
1250
:]
data
-=
data
.
min
()
-
0.001
data
=
data
.
clip
(
min
=
0.001
)
data_true
=
data
.
copy
()
data
=
np
.
ndarray
.
astype
(
data
,
float
)
vmin
=
np
.
log
(
data
.
min
()
+
0.01
)
vmax
=
np
.
log
(
data
.
max
())
local_size
=
4
for
i
in
range
(
5
):
for
i
in
range
(
data
.
shape
[
0
]
/
local_size
):
for
j
in
range
(
data
.
shape
[
1
]
/
local_size
):
local_data
=
data
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
local_data_median
=
np
.
median
(
local_data
)
local_data_var
=
local_data
.
var
()
local_data
=
local_data
.
clip
(
min
=
local_data_median
-
3
*
np
.
sqrt
(
local_data_var
),
max
=
local_data_median
+
3
*
np
.
sqrt
(
local_data_var
))
data
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
=
local_data
background
=
np
.
empty_like
(
data
)
crowded
=
np
.
zeros_like
(
data
)
for
i
in
range
(
data
.
shape
[
0
]
/
local_size
):
for
j
in
range
(
data
.
shape
[
1
]
/
local_size
):
local_true_data
=
data_true
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
local_data
=
data
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
local_true_var
=
local_true_data
.
var
()
local_var
=
local_data
.
var
()
if
0.8
*
np
.
sqrt
(
local_true_var
)
>
np
.
sqrt
(
local_var
):
background
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
=
2.5
*
np
.
median
(
local_data
)
-
1.5
*
local_data
.
mean
()
crowded
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
=
1.
else
:
background
[
i
*
local_size
:(
1
+
i
)
*
local_size
,
j
*
local_size
:(
1
+
j
)
*
local_size
]
=
local_data
.
mean
()
background
=
median_filter
(
background
,
size
=
(
local_size
,
local_size
))
# alpha = 1.25
# Starblade = sb.build_starblade(data, alpha=alpha)
# for i in range(10):
# Starblade = sb.starblade_iteration(Starblade)
#
# plotting on logarithmic scale
# background += background.min()
plt
.
gray
()
plt
.
imsave
(
'diffuse_component.png'
,
np
.
log
(
background
))
#, vmin=vmin, vmax=vmax)
plt
.
imsave
(
'pointlike_component.png'
,
(
data_true
-
background
),
vmin
=
vmin
,
vmax
=
vmax
)
plt
.
imsave
(
'crowded.png'
,
crowded
)
# plt.figure()
# k_lenghts = Starblade.power_spectrum.domain[0].k_lengths
# plt.plot(k_lenghts, Starblade.power_spectrum.val)
# plt.title('power spectrum')
# plt.yscale('log')
# plt.xscale('log')
# plt.ylabel('power')
# plt.xscale('harmonic mode')
# plt.savefig('power_spectrum.png')
starblade/starblade_kl.py
View file @
1385b975
...
...
@@ -20,6 +20,30 @@ from nifty4 import Energy, Field, DiagonalOperator, InversionEnabler
from
starblade_energy
import
StarbladeEnergy
class
StarbladeKL
(
Energy
):
"""The Kullback-Leibler divergence for the starblade problem.
Parameters
----------
position : Field
The current position of the separation.
samples : List
A list containing residual samples.
parameters : Dictionary
Dictionary containing all relevant quantities for the inference,
data : Field
The image data.
alpha : Field
Slope parameter of the point-source prior
q : Field
Cutoff parameter of the point-source prior
power_spectrum : callable or Field
An object that contains the power spectrum of the diffuse component
as a function of the harmonic mode.
FFT : FFTOperator
An operator performing the Fourier transform
inverter : ConjugateGradient
the minimization strategy to use for operator inversion
"""
def
__init__
(
self
,
position
,
samples
,
parameters
):
super
(
StarbladeKL
,
self
).
__init__
(
position
=
position
)
...
...
starblade/sugar.py
View file @
1385b975
...
...
@@ -80,6 +80,8 @@ def starblade_iteration(starblade, samples=3):
----------
starblade : StarbladeEnergy
An instance of an Starblade Energy
samples : int
Number of samples drawn in order to estimate the KL. If zero the MAP is calculated (default: 3).
"""
controller
=
ift
.
GradientNormController
(
name
=
"Newton"
,
tol_abs_gradnorm
=
1e-8
,
...
...
@@ -152,6 +154,13 @@ def multi_starblade_iteration(MultiStarblade, processes = 1):
return
NewStarblades
def
update_power
(
energy
):
""" Calculates a new estimate of the power spectrum given a StarbladeEnergy or StarbladeKL.
For Energy the MAP estimate of the power spectrum is calculated and for KL the variational estimate.
----------
energy : StarbladeEnergy or StarbladeKL
An instance of an StarbladeEnergy or StarbladeKL
"""
if
isinstance
(
energy
,
StarbladeKL
):
power
=
0.
for
en
in
energy
.
energy_list
:
...
...
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