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ift
NIFTy
Commits
bbeba660
Commit
bbeba660
authored
Dec 09, 2019
by
Philipp Arras
Browse files
Remove temporary files used during development
parent
ee029099
Pipeline
#65159
passed with stages
in 12 minutes and 50 seconds
Changes
3
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
demos/find_amplitude_parameters.py
deleted
100644 → 0
View file @
ee029099
# 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) 2013-2019 Max-Planck-Society
# Author: Philipp Arras
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
import
numpy
as
np
import
nifty6
as
ift
import
matplotlib.pyplot
as
plt
def
_default_pspace
(
dom
):
return
ift
.
PowerSpace
(
dom
.
get_default_codomain
())
if
__name__
==
'__main__'
:
np
.
random
.
seed
(
42
)
fa
=
ift
.
CorrelatedFieldMaker
.
make
(
10
,
0.1
,
''
)
n_samps
=
20
slope_means
=
[
-
2
,
-
3
]
fa
.
add_fluctuations
(
ift
.
RGSpace
(
128
,
0.1
),
10
,
2
,
1
,
1e-6
,
2
,
1e-6
,
slope_means
[
0
],
0.2
,
'spatial'
)
# fa.add_fluctuations(_default_pspace(ift.RGSpace((128, 64))), 10, 2, 1,
# 1e-6, 2, 1e-6, slope_means[0], 0.2, 'spatial')
fa
.
add_fluctuations
(
ift
.
RGSpace
(
32
),
3
,
5
,
1
,
1e-6
,
2
,
1e-6
,
slope_means
[
1
],
1
,
'freq'
)
correlated_field
=
fa
.
finalize
()
amplitudes
=
fa
.
normalized_amplitudes
plt
.
style
.
use
(
'seaborn-notebook'
)
tgt
=
correlated_field
.
target
if
len
(
tgt
.
shape
)
==
1
:
fig
,
axes
=
plt
.
subplots
(
nrows
=
1
,
ncols
=
2
)
fig
.
set_size_inches
(
20
,
10
)
else
:
fig
,
axes
=
plt
.
subplots
(
nrows
=
3
,
ncols
=
3
)
fig
.
set_size_inches
(
20
,
16
)
axs
=
(
ax
for
ax
in
axes
.
ravel
())
for
ii
,
aa
in
enumerate
(
amplitudes
):
ax
=
next
(
axs
)
pspec
=
aa
**
2
ax
.
set_xscale
(
'log'
)
ax
.
set_yscale
(
'log'
)
for
_
in
range
(
n_samps
):
fld
=
pspec
(
ift
.
from_random
(
'normal'
,
pspec
.
domain
))
klengths
=
fld
.
domain
[
0
].
k_lengths
ycoord
=
fld
.
val_rw
()
ycoord
[
0
]
=
ycoord
[
1
]
ax
.
plot
(
klengths
,
ycoord
,
alpha
=
1
)
ymin
,
ymax
=
ax
.
get_ylim
()
color
=
plt
.
rcParams
[
'axes.prop_cycle'
].
by_key
()[
'color'
][
0
]
lbl
=
'Mean slope (k^{})'
.
format
(
2
*
slope_means
[
ii
])
for
fac
in
np
.
linspace
(
np
.
log
(
ymin
),
np
.
log
(
ymax
**
2
/
ymin
)):
xs
=
np
.
linspace
(
np
.
amin
(
klengths
[
1
:]),
np
.
amax
(
klengths
[
1
:]))
ys
=
xs
**
(
2
*
slope_means
[
ii
])
*
np
.
exp
(
fac
)
xs
=
np
.
insert
(
xs
,
0
,
0
)
ys
=
np
.
insert
(
ys
,
0
,
ys
[
0
])
ax
.
plot
(
xs
,
ys
,
zorder
=
1
,
color
=
color
,
linewidth
=
0.3
,
label
=
lbl
)
lbl
=
None
ax
.
set_ylim
([
ymin
,
ymax
])
ax
.
set_xlim
([
None
,
np
.
amax
(
klengths
)])
ax
.
legend
()
if
len
(
tgt
.
shape
)
==
2
:
foo
=
[]
for
ax
in
axs
:
pos
=
ift
.
from_random
(
'normal'
,
correlated_field
.
domain
)
fld
=
correlated_field
(
pos
).
val
foo
.
append
((
ax
,
fld
))
mi
,
ma
=
np
.
inf
,
-
np
.
inf
for
_
,
fld
in
foo
:
mi
=
min
([
mi
,
np
.
amin
(
fld
)])
ma
=
max
([
ma
,
np
.
amax
(
fld
)])
nxdx
,
nydy
=
tgt
.
shape
if
len
(
tgt
)
==
2
:
nxdx
*=
tgt
[
0
].
distances
[
0
]
nydy
*=
tgt
[
1
].
distances
[
0
]
else
:
nxdx
*=
tgt
[
0
].
distances
[
0
]
nydy
*=
tgt
[
0
].
distances
[
1
]
for
ax
,
fld
in
foo
:
im
=
ax
.
imshow
(
fld
.
T
,
extent
=
[
0
,
nxdx
,
0
,
nydy
],
aspect
=
'auto'
,
origin
=
'lower'
,
vmin
=
mi
,
vmax
=
ma
)
fig
.
colorbar
(
im
,
ax
=
axes
.
ravel
().
tolist
())
elif
len
(
tgt
.
shape
)
==
1
:
ax
=
next
(
axs
)
flds
=
[]
for
_
in
range
(
n_samps
):
pos
=
ift
.
from_random
(
'normal'
,
correlated_field
.
domain
)
ax
.
plot
(
correlated_field
(
pos
).
val
)
plt
.
savefig
(
'correlated_fields.png'
)
plt
.
close
()
demos/multi_amplitudes_consistency.py
deleted
100644 → 0
View file @
ee029099
import
nifty6
as
ift
import
numpy
as
np
def
testAmplitudesConsistency
(
seed
,
sspace
):
def
stats
(
op
,
samples
):
sc
=
ift
.
StatCalculator
()
for
s
in
samples
:
sc
.
add
(
op
(
s
.
extract
(
op
.
domain
)))
return
sc
.
mean
.
val
,
sc
.
var
.
sqrt
().
val
np
.
random
.
seed
(
seed
)
offset_std
=
.
1
intergated_fluct_std0
=
.
003
intergated_fluct_std1
=
0.1
nsam
=
1000
fsspace
=
ift
.
RGSpace
((
12
,),
(
0.4
,))
fa
=
ift
.
CorrelatedFieldMaker
.
make
(
offset_std
,
1E-8
,
''
)
fa
.
add_fluctuations
(
sspace
,
intergated_fluct_std0
,
1E-8
,
1.1
,
2.
,
2.1
,
.
5
,
-
2
,
1.
,
'spatial'
)
fa
.
add_fluctuations
(
fsspace
,
intergated_fluct_std1
,
1E-8
,
3.1
,
1.
,
.
5
,
.
1
,
-
4
,
1.
,
'freq'
)
op
=
fa
.
finalize
()
samples
=
[
ift
.
from_random
(
'normal'
,
op
.
domain
)
for
_
in
range
(
nsam
)]
tot_flm
,
_
=
stats
(
fa
.
total_fluctuation
,
samples
)
offset_std
,
_
=
stats
(
fa
.
amplitude_total_offset
,
samples
)
intergated_fluct_std0
,
_
=
stats
(
fa
.
average_fluctuation
(
0
),
samples
)
intergated_fluct_std1
,
_
=
stats
(
fa
.
average_fluctuation
(
1
),
samples
)
slice_fluct_std0
,
_
=
stats
(
fa
.
slice_fluctuation
(
0
),
samples
)
slice_fluct_std1
,
_
=
stats
(
fa
.
slice_fluctuation
(
1
),
samples
)
sams
=
[
op
(
s
)
for
s
in
samples
]
fluct_total
=
fa
.
total_fluctuation_realized
(
sams
)
fluct_space
=
fa
.
average_fluctuation_realized
(
sams
,
0
)
fluct_freq
=
fa
.
average_fluctuation_realized
(
sams
,
1
)
zm_std_mean
=
fa
.
offset_amplitude_realized
(
sams
)
sl_fluct_space
=
fa
.
slice_fluctuation_realized
(
sams
,
0
)
sl_fluct_freq
=
fa
.
slice_fluctuation_realized
(
sams
,
1
)
print
(
"Expected offset Std: "
+
str
(
offset_std
))
print
(
"Estimated offset Std: "
+
str
(
zm_std_mean
))
print
(
"Expected integrated fluct. space Std: "
+
str
(
intergated_fluct_std0
))
print
(
"Estimated integrated fluct. space Std: "
+
str
(
fluct_space
))
print
(
"Expected integrated fluct. frequency Std: "
+
str
(
intergated_fluct_std1
))
print
(
"Estimated integrated fluct. frequency Std: "
+
str
(
fluct_freq
))
print
(
"Expected slice fluct. space Std: "
+
str
(
slice_fluct_std0
))
print
(
"Estimated slice fluct. space Std: "
+
str
(
sl_fluct_space
))
print
(
"Expected slice fluct. frequency Std: "
+
str
(
slice_fluct_std1
))
print
(
"Estimated slice fluct. frequency Std: "
+
str
(
sl_fluct_freq
))
print
(
"Expected total fluct. Std: "
+
str
(
tot_flm
))
print
(
"Estimated total fluct. Std: "
+
str
(
fluct_total
))
np
.
testing
.
assert_allclose
(
offset_std
,
zm_std_mean
,
rtol
=
0.5
)
np
.
testing
.
assert_allclose
(
intergated_fluct_std0
,
fluct_space
,
rtol
=
0.5
)
np
.
testing
.
assert_allclose
(
intergated_fluct_std1
,
fluct_freq
,
rtol
=
0.5
)
np
.
testing
.
assert_allclose
(
tot_flm
,
fluct_total
,
rtol
=
0.5
)
np
.
testing
.
assert_allclose
(
slice_fluct_std0
,
sl_fluct_space
,
rtol
=
0.5
)
np
.
testing
.
assert_allclose
(
slice_fluct_std1
,
sl_fluct_freq
,
rtol
=
0.5
)
fa
=
ift
.
CorrelatedFieldMaker
.
make
(
offset_std
,
.
1
,
''
)
fa
.
add_fluctuations
(
fsspace
,
intergated_fluct_std1
,
1.
,
3.1
,
1.
,
.
5
,
.
1
,
-
4
,
1.
,
'freq'
)
m
=
3.
x
=
fa
.
moment_slice_to_average
(
m
)
fa
.
add_fluctuations
(
sspace
,
x
,
1.5
,
1.1
,
2.
,
2.1
,
.
5
,
-
2
,
1.
,
'spatial'
,
0
)
op
=
fa
.
finalize
()
em
,
estd
=
stats
(
fa
.
slice_fluctuation
(
0
),
samples
)
print
(
"Forced slice fluct. space Std: "
+
str
(
m
))
print
(
"Expected slice fluct. Std: "
+
str
(
em
))
np
.
testing
.
assert_allclose
(
m
,
em
,
rtol
=
0.5
)
assert
op
.
target
[
0
]
==
sspace
assert
op
.
target
[
1
]
==
fsspace
# Move to tests
# FIXME This test fails but it is not relevant for the final result
# assert_allclose(ampl(from_random('normal', ampl.domain)).val[0], vol) or 1??
# End move to tests
# move to tests
# assert_allclose(
# smooth(from_random('normal', smooth.domain)).val[0:2], 0)
# end move to tests
for
seed
in
[
1
,
42
]:
for
sp
in
[
ift
.
RGSpace
((
32
,
64
),
(
1.1
,
0.3
)),
ift
.
HPSpace
(
32
),
ift
.
GLSpace
(
32
)
]:
testAmplitudesConsistency
(
seed
,
sp
)
demos/newamplitudes.py
deleted
100644 → 0
View file @
ee029099
import
nifty6
as
ift
import
numpy
as
np
np
.
random
.
seed
(
42
)
sspace
=
ift
.
RGSpace
((
128
,))
fa
=
ift
.
CorrelatedFieldMaker
.
make
(
10
,
0.1
,
''
)
fa
.
add_fluctuations
(
sspace
,
10
,
2
,
1
,
1e-6
,
2
,
1e-6
,
-
2
,
1e-6
,
'spatial'
)
op
=
fa
.
finalize
()
A
=
fa
.
amplitude
cstpos
=
ift
.
from_random
(
'normal'
,
op
.
domain
)
p1
,
p2
=
[
ift
.
Plot
()
for
_
in
range
(
2
)]
lst1
=
[]
skys1
,
skys2
=
[],
[]
for
_
in
range
(
8
):
pos
=
ift
.
from_random
(
'normal'
,
op
.
domain
)
foo
=
ift
.
MultiField
.
union
([
cstpos
,
pos
.
extract
(
A
.
domain
)])
skys2
.
append
(
op
(
foo
))
sky
=
op
(
pos
)
skys1
.
append
(
sky
)
lst1
.
append
(
A
.
force
(
pos
))
for
pp
,
ll
in
[(
p1
,
skys1
),
(
p2
,
skys2
)]:
mi
,
ma
=
None
,
None
if
False
:
mi
,
ma
=
np
.
inf
,
-
np
.
inf
for
ss
in
ll
:
mi
=
min
([
mi
,
np
.
amin
(
ss
.
val
)])
ma
=
max
([
ma
,
np
.
amax
(
ss
.
val
)])
for
ss
in
ll
:
pp
.
add
(
ss
,
zmin
=
mi
,
zmax
=
ma
)
p1
.
add
(
lst1
)
p2
.
add
(
lst1
)
p1
.
output
(
name
=
'full.png'
)
p2
.
output
(
name
=
'xi_fixed.png'
)
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