Skip to content
GitLab
Projects
Groups
Snippets
/
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
ift
starblade
Commits
b0e621a6
Commit
b0e621a6
authored
Apr 25, 2018
by
Jakob Knollmueller
Browse files
Merge branch 'master' into develop
parents
fd9e15f6
1385b975
Changes
4
Hide whitespace changes
Inline
Side-by-side
demos/demo.py
View file @
b0e621a6
...
...
@@ -25,18 +25,26 @@ 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'
# data = fits.open(path)[1].data
data
=
fits
.
open
(
path
)[
0
].
data
[
1000
:
15000
,
1250
:
1750
]
data
-=
data
.
min
()
-
0.001
# data = 1.-plt.imread('data/sdss.png').T[0]
# data = fits.open(path)[1].data
data
=
data
.
clip
(
min
=
0.0001
)
data
=
data
.
clip
(
min
=
0.001
)
data
=
np
.
ndarray
.
astype
(
data
,
float
)
vmin
=
np
.
log
(
data
.
min
()
+
0.
01
)
vmin
=
np
.
log
(
data
.
min
()
+
0.
2
)
vmax
=
np
.
log
(
data
.
max
())
plt
.
imsave
(
'data.png'
,
np
.
log
(
data
),
vmin
=
vmin
,
vmax
=
vmax
)
alpha
=
1.25
Starblade
=
sb
.
build_starblade
(
data
,
alpha
=
alpha
)
for
i
in
range
(
10
):
Starblade
=
sb
.
starblade_iteration
(
Starblade
)
Starblade
=
sb
.
starblade_iteration
(
Starblade
,
samples
=
i
)
#plotting on logarithmic scale
plt
.
imsave
(
'diffuse_component.png'
,
Starblade
.
s
.
val
,
vmin
=
vmin
,
vmax
=
vmax
)
...
...
@@ -48,5 +56,5 @@ if __name__ == '__main__':
plt
.
yscale
(
'log'
)
plt
.
xscale
(
'log'
)
plt
.
ylabel
(
'power'
)
plt
.
x
scale
(
'harmonic mode'
)
plt
.
x
label
(
'harmonic mode'
)
plt
.
savefig
(
'power_spectrum.png'
)
starblade/__init__.py
View file @
b0e621a6
from
.sugar
import
(
build_starblade
,
starblade_iteration
,
build_multi_starblade
,
multi_starblade_iteration
)
from
.starblade_kl
import
StarbladeKL
from
.starblade_energy
import
StarbladeEnergy
starblade/starblade_kl.py
0 → 100644
View file @
b0e621a6
# 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
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
)
self
.
samples
=
samples
self
.
parameters
=
parameters
self
.
energy_list
=
[]
for
sample
in
samples
:
energy
=
StarbladeEnergy
(
position
+
sample
,
parameters
)
self
.
energy_list
.
append
(
energy
)
def
at
(
self
,
position
):
return
self
.
__class__
(
position
,
samples
=
self
.
samples
,
parameters
=
self
.
parameters
)
@
property
def
value
(
self
):
value
=
0.
for
energy
in
self
.
energy_list
:
value
+=
energy
.
value
value
/=
len
(
self
.
energy_list
)
return
value
@
property
def
gradient
(
self
):
gradient
=
Field
.
zeros
(
self
.
position
.
domain
)
for
energy
in
self
.
energy_list
:
gradient
+=
energy
.
gradient
gradient
/=
len
(
self
.
energy_list
)
return
gradient
@
property
def
curvature
(
self
):
curvature
=
DiagonalOperator
(
Field
.
zeros
(
self
.
position
.
domain
))
for
energy
in
self
.
energy_list
:
curvature
+=
energy
.
curvature
curvature
*=
Field
(
self
.
position
.
domain
,
val
=
1.
/
len
(
self
.
energy_list
))
return
InversionEnabler
(
curvature
,
self
.
parameters
[
'inverter'
])
starblade/sugar.py
View file @
b0e621a6
...
...
@@ -21,9 +21,9 @@ from multiprocessing import Pool
import
nifty4
as
ift
from
.starblade_energy
import
StarbladeEnergy
from
.starblade_kl
import
StarbladeKL
def
build_starblade
(
data
,
alpha
=
1.5
,
q
=
1e-40
,
cg_iterations
=
500
,
newton_iterations
=
3
,
def
build_starblade
(
data
,
alpha
=
1.5
,
q
=
1e-40
,
cg_iterations
=
100
,
newton_iterations
=
3
,
manual_power_spectrum
=
None
):
""" Setting up the StarbladeEnergy for the given data and parameters
Parameters
...
...
@@ -69,27 +69,37 @@ def build_starblade(data, alpha=1.5, q=1e-40, cg_iterations=500, newton_iteratio
inverter
=
inverter
,
FFT
=
FFT
,
newton_iterations
=
newton_iterations
,
update_power
=
update_power
)
Starblade
=
StarbladeEnergy
(
position
=
initial_x
,
parameters
=
parameters
)
return
Starblade
def
starblade_iteration
(
starblade
):
def
starblade_iteration
(
starblade
,
samples
=
3
):
""" Performing one Newton minimization step
Parameters
----------
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
,
iteration_limit
=
starblade
.
newton_iterations
)
minimizer
=
ift
.
RelaxedNewton
(
controller
=
controller
)
energy
,
convergence
=
minimizer
(
starblade
)
sample_list
=
[]
for
i
in
range
(
samples
):
sample
=
starblade
.
curvature
.
inverse
.
draw_sample
()
sample_list
.
append
(
sample
)
if
len
(
sample_list
)
>
0
:
energy
=
StarbladeKL
(
starblade
.
position
,
samples
=
sample_list
,
parameters
=
starblade
.
parameters
)
else
:
energy
=
starblade
energy
,
convergence
=
minimizer
(
energy
)
new_position
=
energy
.
position
new_parameters
=
energy
.
parameters
if
energy
.
update_power
:
h_space
=
energy
.
correlation
.
domain
[
0
]
FFT
=
energy
.
FFT
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
h_space
,
logarithmic
=
False
)
new_power
=
ift
.
power_analyze
(
FFT
.
inverse_times
(
energy
.
s
),
binbounds
=
binbounds
)
if
energy
.
parameters
[
'update_power'
]:
new_power
=
update_power
(
energy
)
# new_power /= (new_power.domain[0].k_lengths+1.)**2
new_parameters
[
'power_spectrum'
]
=
new_power
...
...
@@ -143,6 +153,25 @@ def multi_starblade_iteration(MultiStarblade, processes = 1):
NewStarblades
.
append
(
starblade_iteration
(
starblade
))
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
:
power
+=
ift
.
power_analyze
(
energy
.
parameters
[
'FFT'
].
inverse_times
(
en
.
s
),
binbounds
=
en
.
parameters
[
'power_spectrum'
].
domain
[
0
].
binbounds
)
power
/=
len
(
energy
.
energy_list
)
else
:
power
=
ift
.
power_analyze
(
energy
.
FFT
.
inverse_times
(
energy
.
s
),
binbounds
=
energy
.
parameters
[
'power_spectrum'
].
domain
[
0
].
binbounds
)
return
power
if
__name__
==
'__main__'
:
pass
...
...
Write
Preview
Supports
Markdown
0%
Try again
or
attach a new file
.
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment