Skip to content
GitLab
Menu
Projects
Groups
Snippets
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
Menu
Open sidebar
ift
NIFTy
Commits
5e0f300b
Commit
5e0f300b
authored
Jan 28, 2018
by
Philipp Arras
Browse files
More tests
parent
a63ef1fc
Pipeline
#24128
failed with stage
in 3 minutes and 58 seconds
Changes
3
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
test/test_energies/test_
nonlinearWiener
.py
→
test/test_energies/test_
map
.py
View file @
5e0f300b
...
...
@@ -24,17 +24,10 @@ from test.common import expand
from
numpy.testing
import
assert_allclose
_harmonic_spaces
=
[
ift
.
RGSpace
(
7
,
distances
=
0.2
,
harmonic
=
True
),
ift
.
RGSpace
((
12
,
46
),
distances
=
(
0.2
,
0.3
),
harmonic
=
True
),
ift
.
LMSpace
(
17
)]
# TODO Add also other space types
_position_spaces
=
[
ift
.
RGSpace
(
19
,
distances
=
0.7
),
ift
.
RGSpace
((
1
,
2
,
3
,
6
),
distances
=
(
0.2
,
0.25
,
0.34
,
.
8
)),
ift
.
HPSpace
(
17
),
ift
.
GLSpace
(
8
,
13
)]
class
Energy_Tests
(
unittest
.
TestCase
):
class
Map_Energy_Tests
(
unittest
.
TestCase
):
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
ift
.
library
.
Exponential
,
ift
.
library
.
Linear
]))
...
...
@@ -76,99 +69,47 @@ class Energy_Tests(unittest.TestCase):
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
ift
.
library
.
Exponential
,
ift
.
library
.
Linear
]))
def
testNonlinearPower
(
self
,
space
,
nonlinearity
):
f
=
nonlinearity
()
dim
=
len
(
space
.
shape
)
fft
=
ift
.
FFTOperator
(
space
)
hspace
=
fft
.
target
[
0
]
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
hspace
,
logarithmic
=
True
)
pspace
=
ift
.
PowerSpace
(
hspace
,
binbounds
=
binbounds
)
P
=
ift
.
PowerProjectionOperator
(
domain
=
hspace
,
power_space
=
pspace
)
xi
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
def
pspec
(
k
):
return
1
/
(
1
+
k
**
2
)
**
dim
tau0
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
tau0
))
n
=
ift
.
Field
.
from_random
(
domain
=
space
,
random_type
=
'normal'
)
s
=
fft
.
inverse_times
(
xi
*
A
)
diag
=
ift
.
Field
.
ones
(
space
)
*
10
R
=
ift
.
DiagonalOperator
(
diag
)
diag
=
ift
.
Field
.
ones
(
space
)
N
=
ift
.
DiagonalOperator
(
diag
)
d
=
R
(
f
(
s
))
+
n
direction
=
ift
.
Field
.
from_random
(
'normal'
,
pspace
)
direction
/=
np
.
sqrt
(
direction
.
var
())
eps
=
1e-10
tau1
=
tau0
+
eps
*
direction
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
inverter
=
ift
.
ConjugateGradient
(
IC
)
S
=
ift
.
create_power_operator
(
hspace
,
power_spectrum
=
lambda
k
:
1.
)
D
=
ift
.
library
.
NonlinearWienerFilterEnergy
(
position
=
xi
,
d
=
d
,
Instrument
=
R
,
nonlinearity
=
f
,
FFT
=
fft
,
power
=
A
,
N
=
N
,
S
=
S
,
inverter
=
inverter
).
curvature
energy0
=
ift
.
library
.
NonlinearPowerEnergy
(
position
=
tau0
,
d
=
d
,
m
=
xi
,
D
=
D
,
Instrument
=
R
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
N
=
N
,
inverter
=
inverter
)
energy1
=
ift
.
library
.
NonlinearPowerEnergy
(
position
=
tau1
,
d
=
d
,
m
=
xi
,
D
=
D
,
Instrument
=
R
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
N
=
N
,
inverter
=
inverter
)
a
=
(
energy1
.
value
-
energy0
.
value
)
/
eps
b
=
energy0
.
gradient
.
vdot
(
direction
)
tol
=
1e-2
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
ift
.
library
.
Exponential
,
ift
.
library
.
Linear
]))
def
testNoise
(
self
,
space
,
nonlinearity
):
f
=
nonlinearity
()
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)]))
def
testLinearMap
(
self
,
space
):
dim
=
len
(
space
.
shape
)
fft
=
ift
.
FFTOperator
(
space
)
h
space
=
f
ft
.
target
[
0
]
hspace
=
space
.
get_default_codomain
(
)
h
t
=
i
ft
.
HarmonicTransformOperator
(
hspace
,
target
=
space
)
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
hspace
,
logarithmic
=
False
)
pspace
=
ift
.
PowerSpace
(
hspace
,
binbounds
=
binbounds
)
P
=
ift
.
PowerProjectionOperator
(
domain
=
hspace
,
power_space
=
pspace
)
xi
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
xi
0
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
def
pspec
(
k
):
return
1
/
(
1
+
k
**
2
)
**
dim
tau
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
tau
))
pspec
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
pspec
))
n
=
ift
.
Field
.
from_random
(
domain
=
space
,
random_type
=
'normal'
)
s
=
fft
.
inverse_times
(
xi
*
A
)
s
0
=
xi
0
*
A
diag
=
ift
.
Field
.
ones
(
space
)
*
10
R
=
ift
.
DiagonalOperator
(
diag
)
Instrument
=
ift
.
DiagonalOperator
(
diag
)
R
=
Instrument
*
ht
diag
=
ift
.
Field
.
ones
(
space
)
eta0
=
ift
.
log
(
diag
)
N
=
ift
.
DiagonalOperator
(
diag
)
d
=
R
(
f
(
s
))
+
n
alpha
=
ift
.
Field
(
d
.
domain
,
val
=
2.
)
q
=
ift
.
Field
(
d
.
domain
,
val
=
1e-5
)
d
=
R
(
s0
)
+
n
direction
=
ift
.
Field
.
from_random
(
'normal'
,
d
.
domain
)
direction
=
ift
.
Field
.
from_random
(
'normal'
,
hspace
)
direction
/=
np
.
sqrt
(
direction
.
var
())
eps
=
1e-10
eta
1
=
eta
0
+
eps
*
direction
s
1
=
s
0
+
eps
*
direction
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
inverter
=
ift
.
ConjugateGradient
(
IC
)
S
=
ift
.
create_power_operator
(
hspace
,
power_spectrum
=
lambda
k
:
1.
)
D
=
ift
.
library
.
NonlinearWienerFilterEnergy
(
position
=
xi
,
d
=
d
,
Instrument
=
R
,
nonlinearity
=
f
,
FFT
=
fft
,
power
=
A
,
N
=
N
,
S
=
S
,
inverter
=
inverter
).
curvature
energy0
=
ift
.
library
.
NoiseEnergy
(
position
=
eta0
,
d
=
d
,
m
=
xi
,
D
=
D
,
t
=
tau
,
Instrument
=
R
,
alpha
=
alpha
,
q
=
q
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
samples
=
3
)
energy1
=
ift
.
library
.
NoiseEnergy
(
position
=
eta1
,
d
=
d
,
m
=
xi
,
D
=
D
,
t
=
tau
,
Instrument
=
R
,
alpha
=
alpha
,
q
=
q
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
samples
=
3
)
energy0
=
ift
.
library
.
WienerFilterEnergy
(
position
=
s0
,
d
=
d
,
R
=
R
,
N
=
N
,
S
=
S
,
inverter
=
inverter
)
energy1
=
ift
.
library
.
WienerFilterEnergy
(
position
=
s1
,
d
=
d
,
R
=
R
,
N
=
N
,
S
=
S
,
inverter
=
inverter
)
a
=
(
energy1
.
value
-
energy0
.
value
)
/
eps
b
=
energy0
.
gradient
.
vdot
(
direction
)
tol
=
1e-
2
tol
=
1e-
3
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
test/test_energies/test_noise.py
0 → 100644
View file @
5e0f300b
# 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) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
import
unittest
import
nifty4
as
ift
import
numpy
as
np
from
itertools
import
product
from
test.common
import
expand
from
numpy.testing
import
assert_allclose
# TODO Add also other space types
class
Noise_Energy_Tests
(
unittest
.
TestCase
):
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
ift
.
library
.
Exponential
,
ift
.
library
.
Linear
]))
def
testNoise
(
self
,
space
,
nonlinearity
):
f
=
nonlinearity
()
dim
=
len
(
space
.
shape
)
fft
=
ift
.
FFTOperator
(
space
)
hspace
=
fft
.
target
[
0
]
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
hspace
,
logarithmic
=
False
)
pspace
=
ift
.
PowerSpace
(
hspace
,
binbounds
=
binbounds
)
P
=
ift
.
PowerProjectionOperator
(
domain
=
hspace
,
power_space
=
pspace
)
xi
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
def
pspec
(
k
):
return
1
/
(
1
+
k
**
2
)
**
dim
tau
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
tau
))
n
=
ift
.
Field
.
from_random
(
domain
=
space
,
random_type
=
'normal'
)
s
=
fft
.
inverse_times
(
xi
*
A
)
diag
=
ift
.
Field
.
ones
(
space
)
*
10
R
=
ift
.
DiagonalOperator
(
diag
)
diag
=
ift
.
Field
.
ones
(
space
)
eta0
=
ift
.
log
(
diag
)
N
=
ift
.
DiagonalOperator
(
diag
)
d
=
R
(
f
(
s
))
+
n
alpha
=
ift
.
Field
(
d
.
domain
,
val
=
2.
)
q
=
ift
.
Field
(
d
.
domain
,
val
=
1e-5
)
direction
=
ift
.
Field
.
from_random
(
'normal'
,
d
.
domain
)
direction
/=
np
.
sqrt
(
direction
.
var
())
eps
=
1e-10
eta1
=
eta0
+
eps
*
direction
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
inverter
=
ift
.
ConjugateGradient
(
IC
)
S
=
ift
.
create_power_operator
(
hspace
,
power_spectrum
=
lambda
k
:
1.
)
D
=
ift
.
library
.
NonlinearWienerFilterEnergy
(
position
=
xi
,
d
=
d
,
Instrument
=
R
,
nonlinearity
=
f
,
FFT
=
fft
,
power
=
A
,
N
=
N
,
S
=
S
,
inverter
=
inverter
).
curvature
energy0
=
ift
.
library
.
NoiseEnergy
(
position
=
eta0
,
d
=
d
,
m
=
xi
,
D
=
D
,
t
=
tau
,
Instrument
=
R
,
alpha
=
alpha
,
q
=
q
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
samples
=
3
)
energy1
=
ift
.
library
.
NoiseEnergy
(
position
=
eta1
,
d
=
d
,
m
=
xi
,
D
=
D
,
t
=
tau
,
Instrument
=
R
,
alpha
=
alpha
,
q
=
q
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
samples
=
3
)
a
=
(
energy1
.
value
-
energy0
.
value
)
/
eps
b
=
energy0
.
gradient
.
vdot
(
direction
)
tol
=
1e-2
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
test/test_energies/test_power.py
0 → 100644
View file @
5e0f300b
# 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) 2013-2017 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.
import
unittest
import
nifty4
as
ift
import
numpy
as
np
from
itertools
import
product
from
test.common
import
expand
from
numpy.testing
import
assert_allclose
# TODO Add also other space types
class
Power_Energy_Tests
(
unittest
.
TestCase
):
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)]))
def
testLinearPower
(
self
,
space
):
dim
=
len
(
space
.
shape
)
hspace
=
space
.
get_default_codomain
()
ht
=
ift
.
HarmonicTransformOperator
(
hspace
,
space
)
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
hspace
,
logarithmic
=
True
)
pspace
=
ift
.
PowerSpace
(
hspace
,
binbounds
=
binbounds
)
P
=
ift
.
PowerProjectionOperator
(
domain
=
hspace
,
power_space
=
pspace
)
xi
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
def
pspec
(
k
):
return
1
/
(
1
+
k
**
2
)
**
dim
tau0
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
tau0
))
n
=
ift
.
Field
.
from_random
(
domain
=
space
,
random_type
=
'normal'
)
s
=
xi
*
A
diag
=
ift
.
Field
.
ones
(
space
)
*
10
Instrument
=
ift
.
DiagonalOperator
(
diag
)
R
=
Instrument
*
ht
diag
=
ift
.
Field
.
ones
(
space
)
N
=
ift
.
DiagonalOperator
(
diag
)
d
=
R
(
s
)
+
n
direction
=
ift
.
Field
.
from_random
(
'normal'
,
pspace
)
direction
/=
np
.
sqrt
(
direction
.
var
())
eps
=
1e-10
tau1
=
tau0
+
eps
*
direction
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
inverter
=
ift
.
ConjugateGradient
(
IC
)
S
=
ift
.
create_power_operator
(
hspace
,
power_spectrum
=
lambda
k
:
1.
)
D
=
ift
.
library
.
WienerFilterEnergy
(
position
=
s
,
d
=
d
,
R
=
R
,
N
=
N
,
S
=
S
,
inverter
=
inverter
).
curvature
energy0
=
ift
.
library
.
CriticalPowerEnergy
(
position
=
tau0
,
m
=
xi
,
D
=
D
,
inverter
=
inverter
)
energy1
=
ift
.
library
.
CriticalPowerEnergy
(
position
=
tau1
,
m
=
xi
,
D
=
D
,
inverter
=
inverter
)
a
=
(
energy1
.
value
-
energy0
.
value
)
/
eps
b
=
energy0
.
gradient
.
vdot
(
direction
)
tol
=
1e-10
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
@
expand
(
product
([
ift
.
RGSpace
(
64
,
distances
=
.
789
),
ift
.
RGSpace
([
32
,
32
],
distances
=
.
789
)],
[
ift
.
library
.
Exponential
,
ift
.
library
.
Linear
]))
def
testNonlinearPower
(
self
,
space
,
nonlinearity
):
f
=
nonlinearity
()
dim
=
len
(
space
.
shape
)
fft
=
ift
.
FFTOperator
(
space
)
hspace
=
fft
.
target
[
0
]
binbounds
=
ift
.
PowerSpace
.
useful_binbounds
(
hspace
,
logarithmic
=
True
)
pspace
=
ift
.
PowerSpace
(
hspace
,
binbounds
=
binbounds
)
P
=
ift
.
PowerProjectionOperator
(
domain
=
hspace
,
power_space
=
pspace
)
xi
=
ift
.
Field
.
from_random
(
domain
=
hspace
,
random_type
=
'normal'
)
def
pspec
(
k
):
return
1
/
(
1
+
k
**
2
)
**
dim
tau0
=
ift
.
PS_field
(
pspace
,
pspec
)
A
=
P
.
adjoint_times
(
ift
.
sqrt
(
tau0
))
n
=
ift
.
Field
.
from_random
(
domain
=
space
,
random_type
=
'normal'
)
s
=
fft
.
inverse_times
(
xi
*
A
)
diag
=
ift
.
Field
.
ones
(
space
)
*
10
R
=
ift
.
DiagonalOperator
(
diag
)
diag
=
ift
.
Field
.
ones
(
space
)
N
=
ift
.
DiagonalOperator
(
diag
)
d
=
R
(
f
(
s
))
+
n
direction
=
ift
.
Field
.
from_random
(
'normal'
,
pspace
)
direction
/=
np
.
sqrt
(
direction
.
var
())
eps
=
1e-10
tau1
=
tau0
+
eps
*
direction
IC
=
ift
.
GradientNormController
(
name
=
'IC'
,
verbose
=
False
,
iteration_limit
=
100
,
tol_abs_gradnorm
=
1e-5
)
inverter
=
ift
.
ConjugateGradient
(
IC
)
S
=
ift
.
create_power_operator
(
hspace
,
power_spectrum
=
lambda
k
:
1.
)
D
=
ift
.
library
.
NonlinearWienerFilterEnergy
(
position
=
xi
,
d
=
d
,
Instrument
=
R
,
nonlinearity
=
f
,
FFT
=
fft
,
power
=
A
,
N
=
N
,
S
=
S
,
inverter
=
inverter
).
curvature
energy0
=
ift
.
library
.
NonlinearPowerEnergy
(
position
=
tau0
,
d
=
d
,
m
=
xi
,
D
=
D
,
Instrument
=
R
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
N
=
N
,
inverter
=
inverter
)
energy1
=
ift
.
library
.
NonlinearPowerEnergy
(
position
=
tau1
,
d
=
d
,
m
=
xi
,
D
=
D
,
Instrument
=
R
,
Projection
=
P
,
nonlinearity
=
f
,
FFT
=
fft
,
N
=
N
,
inverter
=
inverter
)
a
=
(
energy1
.
value
-
energy0
.
value
)
/
eps
b
=
energy0
.
gradient
.
vdot
(
direction
)
tol
=
1e-2
assert_allclose
(
a
,
b
,
rtol
=
tol
,
atol
=
tol
)
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
.
Attach a 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