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ift
NIFTy
Commits
cdb27fee
Commit
cdb27fee
authored
Jan 14, 2019
by
Philipp Arras
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Docs and add multifrequency model to tests
parent
9d00734c
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2
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2 changed files
with
75 additions
and
38 deletions
+75
-38
nifty5/library/correlated_fields.py
nifty5/library/correlated_fields.py
+69
-38
test/test_model_gradients.py
test/test_model_gradients.py
+6
-0
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nifty5/library/correlated_fields.py
View file @
cdb27fee
...
...
@@ -15,64 +15,95 @@
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik.
from
functools
import
reduce
from
..domain_tuple
import
DomainTuple
from
..operators.contraction_operator
import
ContractionOperator
from
..operators.distributors
import
PowerDistributor
from
..operators.harmonic_operators
import
HarmonicTransformOperator
from
..operators.simple_linear_operators
import
ducktape
from
..operators.scaling_operator
import
ScalingOperator
def
CorrelatedField
(
s_space
,
amplitude_operator
,
name
=
'xi'
):
'''
Function for construction of correlated fields
def
CorrelatedField
(
target
,
amplitude_operator
,
name
=
'xi'
):
'''Constructs operator which turns white Gaussian excitation fields into a
correlated field.
This function returns an operator which implements:
ht @ (vol * A * xi),
where `ht` is a harmonic transform operator, `A` is the sqare root of the
prior covariance an `xi` is the excitation field.
Parameters
----------
s_space : Domain
Field domain
target : Domain, DomainTuple or tuple of Domain
Target of the operator. Is not allowed to be a DomainTuple with more
than one space.
amplitude_operator: Operator
operator for correlation structure
name : string
MultiField component name
:class:`MultiField` key for xi-field.
Returns
-------
Correlated field : Operator
'''
h_space
=
s_space
.
get_default_codomain
()
ht
=
HarmonicTransformOperator
(
h_space
,
s_space
)
tgt
=
DomainTuple
.
make
(
target
)
if
len
(
tgt
)
>
1
:
raise
ValueError
h_space
=
tgt
[
0
].
get_default_codomain
()
ht
=
HarmonicTransformOperator
(
h_space
,
tgt
[
0
])
p_space
=
amplitude_operator
.
target
[
0
]
power_distributor
=
PowerDistributor
(
h_space
,
p_space
)
A
=
power_distributor
(
amplitude_operator
)
vol
=
h_space
.
scalar_dvol
vol
=
ScalingOperator
(
vol
**
(
-
0.5
),
h_space
)
return
ht
(
vol
(
A
)
*
ducktape
(
h_space
,
None
,
name
))
vol
=
h_space
.
scalar_dvol
**-
0.5
return
ht
(
vol
*
A
*
ducktape
(
h_space
,
None
,
name
))
def
MfCorrelatedField
(
s_space_spatial
,
s_space_energy
,
amplitude_operator_spatial
,
amplitude_operator_energy
,
name
=
"xi"
):
'''
Method for construction of correlated multi-frequency fields
def
MfCorrelatedField
(
target
,
amplitudes
,
name
=
'xi'
):
'''Constructs operator which turns white Gaussian excitation fields into a
correlated field defined on a DomainTuple with two entries and two separate
correlation structures.
This operator may be used as model for multi-frequency reconstructions
with a correlation structure in both spatial and energy direction.
Parameters
----------
target : Domain, DomainTuple or tuple of Domain
Target of the operator. Is not allowed to be a DomainTuple with more
than one space.
amplitudes: iterable of Operator
List of two amplitude operators.
name : string
:class:`MultiField` key for xi-field.
Returns
-------
Correlated field : Operator
'''
h_space_spatial
=
s_space_spatial
.
get_default_codomain
()
h_space_energy
=
s_space_energy
.
get_default_codomain
()
h_space
=
DomainTuple
.
make
((
h_space_spatial
,
h_space_energy
))
ht1
=
HarmonicTransformOperator
(
h_space
,
target
=
s_space_spatial
,
space
=
0
)
ht2
=
HarmonicTransformOperator
(
ht1
.
target
,
space
=
1
)
ht
=
ht2
(
ht1
)
tgt
=
DomainTuple
.
make
(
target
)
if
len
(
tgt
)
!=
2
:
raise
ValueError
if
len
(
amplitudes
)
!=
2
:
raise
ValueError
p_space_spatial
=
amplitude_operator_spatial
.
target
[
0
]
p_space_energy
=
amplitude_operator_energy
.
target
[
0
]
hsp
=
DomainTuple
.
make
([
tt
.
get_default_codomain
()
for
tt
in
tgt
])
ht1
=
HarmonicTransformOperator
(
hsp
,
target
=
tgt
[
0
],
space
=
0
)
ht2
=
HarmonicTransformOperator
(
ht1
.
target
,
space
=
1
)
ht
=
ht2
@
ht1
pd_spatial
=
PowerDistributor
(
h_space
,
p_space_spatial
,
0
)
pd_energy
=
PowerDistributor
(
pd_spatial
.
domain
,
p_space_energy
,
1
)
pd
=
pd_spatial
(
pd_energy
)
psp
=
[
aa
.
target
[
0
]
for
aa
in
amplitudes
]
pd0
=
PowerDistributor
(
hsp
,
psp
[
0
],
0
)
pd1
=
PowerDistributor
(
pd0
.
domain
,
psp
[
1
],
1
)
pd
=
pd0
@
pd1
dom_distr_spatial
=
ContractionOperator
(
pd
.
domain
,
1
).
adjoint
dom_distr_energy
=
ContractionOperator
(
pd
.
domain
,
0
).
adjoint
dd0
=
ContractionOperator
(
pd
.
domain
,
1
).
adjoint
dd1
=
ContractionOperator
(
pd
.
domain
,
0
).
adjoint
d
=
[
dd0
,
dd1
]
a
_spatial
=
dom_distr_spatial
(
amplitude_operator_spatial
)
a
_energy
=
dom_distr_energy
(
amplitude_operator_energy
)
a
=
a_spatial
*
a_energy
A
=
pd
(
a
)
return
ht
(
A
*
ducktape
(
h_space
,
None
,
name
))
a
=
[
dd
@
amplitudes
[
ii
]
for
ii
,
dd
in
enumerate
(
d
)]
a
=
reduce
(
lambda
x
,
y
:
x
*
y
,
a
)
A
=
pd
@
a
vol
=
reduce
(
lambda
x
,
y
:
x
*
y
,
[
sp
.
scalar_dvol
**-
0.5
for
sp
in
hsp
]
)
return
ht
(
vol
*
A
*
ducktape
(
hsp
,
None
,
name
))
test/test_model_gradients.py
View file @
cdb27fee
...
...
@@ -103,6 +103,12 @@ def testModelLibrary(space, seed):
pos
=
S
.
draw_sample
()
ift
.
extra
.
check_value_gradient_consistency
(
model2
,
pos
,
ntries
=
20
)
domtup
=
ift
.
DomainTuple
.
make
((
space
,
space
))
model3
=
ift
.
MfCorrelatedField
(
domtup
,
[
model
,
model
])
S
=
ift
.
ScalingOperator
(
1.
,
model3
.
domain
)
pos
=
S
.
draw_sample
()
ift
.
extra
.
check_value_gradient_consistency
(
model3
,
pos
,
ntries
=
20
)
def
testPointModel
(
space
,
seed
):
S
=
ift
.
ScalingOperator
(
1.
,
space
)
...
...
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