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ift
NIFTy
Commits
af8fa403
Commit
af8fa403
authored
Oct 04, 2018
by
Philipp Arras
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Split AmplitudeModel into several models
parent
fbe00860
Changes
1
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with
87 additions
and
35 deletions
+87
-35
nifty5/library/amplitude_model.py
nifty5/library/amplitude_model.py
+87
-35
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nifty5/library/amplitude_model.py
View file @
af8fa403
...
...
@@ -29,7 +29,7 @@ from ..sugar import makeOp, sqrt
def
_ceps_kernel
(
dof_space
,
k
,
a
,
k0
):
return
a
**
2
/
(
1
+
(
k
/
k0
)
**
2
)
**
2
return
a
**
2
/
(
1
+
(
k
/
k0
)
**
2
)
**
2
def
create_cepstrum_amplitude_field
(
domain
,
cepstrum
):
...
...
@@ -51,7 +51,7 @@ def create_cepstrum_amplitude_field(domain, cepstrum):
q_array
=
domain
.
get_k_array
()
# Fill cepstrum field (all non-zero modes)
no_zero_modes
=
(
slice
(
1
,
None
),)
*
dim
no_zero_modes
=
(
slice
(
1
,
None
),)
*
dim
ks
=
q_array
[(
slice
(
None
),)
+
no_zero_modes
]
cepstrum_field
=
np
.
zeros
(
shape
)
cepstrum_field
[
no_zero_modes
]
=
cepstrum
(
ks
)
...
...
@@ -69,6 +69,76 @@ def create_cepstrum_amplitude_field(domain, cepstrum):
return
Field
.
from_global_data
(
domain
,
cepstrum_field
)
class
CepstrumModel
(
Operator
):
'''
Parameters
----------
ceps_a, ceps_k0 : Smoothness parameters in ceps_kernel
eg. ceps_kernel(k) = (a/(1+(k/k0)**2))**2
a = ceps_a, k0 = ceps_k0
'''
def
__init__
(
self
,
logk_space
,
ceps_a
,
ceps_k
):
from
..operators.qht_operator
import
QHTOperator
from
..operators.symmetrizing_operator
import
SymmetrizingOperator
qht
=
QHTOperator
(
target
=
logk_space
)
dof_space
=
qht
.
domain
[
0
]
sym
=
SymmetrizingOperator
(
logk_space
)
kern
=
lambda
k
:
_ceps_kernel
(
dof_space
,
k
,
ceps_a
,
ceps_k
)
cepstrum
=
create_cepstrum_amplitude_field
(
dof_space
,
kern
)
self
.
_qht
=
qht
self
.
_ceps
=
makeOp
(
sqrt
(
cepstrum
))
self
.
_op
=
sym
(
qht
(
makeOp
(
sqrt
(
cepstrum
))))
self
.
_domain
,
self
.
_target
=
self
.
_op
.
domain
,
self
.
_op
.
target
def
apply
(
self
,
x
):
self
.
_check_input
(
x
)
return
self
.
_op
(
x
)
@
property
def
qht
(
self
):
return
self
.
_qht
@
property
def
ceps
(
self
):
return
self
.
_ceps
class
SlopeModel
(
Operator
):
'''
Parameters
----------
sm, sv : slope_mean = expected exponent of power law (e.g. -4),
slope_variance (default=1)
im, iv : y-intercept_mean, y-intercept_variance of power_slope
'''
def
__init__
(
self
,
logk_space
,
sm
,
sv
,
im
,
iv
):
from
..operators.slope_operator
import
SlopeOperator
phi_mean
=
np
.
array
([
sm
,
im
+
sm
*
logk_space
.
t_0
[
0
]])
phi_sig
=
np
.
array
([
sv
,
iv
])
self
.
_slope
=
SlopeOperator
(
logk_space
)
self
.
_slope
=
self
.
_slope
(
makeOp
(
Field
.
from_global_data
(
self
.
_slope
.
domain
,
phi_sig
)))
self
.
_norm_phi_mean
=
Field
.
from_global_data
(
self
.
_slope
.
domain
,
phi_mean
/
phi_sig
)
self
.
_domain
=
self
.
_slope
.
domain
self
.
_target
=
self
.
_slope
.
target
def
apply
(
self
,
x
):
self
.
_check_input
(
x
)
return
self
.
_slope
(
x
+
self
.
_norm_phi_mean
)
@
property
def
norm_phi_mean
(
self
):
return
self
.
_norm_phi_mean
class
AmplitudeModel
(
Operator
):
'''
Computes a smooth power spectrum.
...
...
@@ -88,49 +158,31 @@ class AmplitudeModel(Operator):
im, iv : y-intercept_mean, y-intercept_variance of power_slope
'''
def
__init__
(
self
,
s_space
,
Npixdof
,
ceps_a
,
ceps_k
,
sm
,
sv
,
im
,
iv
,
keys
=
[
'tau'
,
'phi'
]):
def
__init__
(
self
,
s_space
,
Npixdof
,
ceps_a
,
ceps_k
,
sm
,
sv
,
im
,
iv
,
keys
=
[
'tau'
,
'phi'
]):
from
..operators.exp_transform
import
ExpTransform
from
..operators.qht_operator
import
QHTOperator
from
..operators.slope_operator
import
SlopeOperator
from
..operators.symmetrizing_operator
import
SymmetrizingOperator
from
..operators.simple_linear_operators
import
FieldAdapter
from
..operators.scaling_operator
import
ScalingOperator
h_space
=
s_space
.
get_default_codomain
()
self
.
_exp_transform
=
ExpTransform
(
PowerSpace
(
h_space
),
Npixdof
)
logk_space
=
self
.
_exp_transform
.
domain
[
0
]
qht
=
QHTOperator
(
target
=
logk_space
)
dof_space
=
qht
.
domain
[
0
]
sym
=
SymmetrizingOperator
(
logk_space
)
phi_mean
=
np
.
array
([
sm
,
im
+
sm
*
logk_space
.
t_0
[
0
]])
phi_sig
=
np
.
array
([
sv
,
iv
])
et
=
ExpTransform
(
PowerSpace
(
h_space
),
Npixdof
)
logk_space
=
et
.
domain
[
0
]
self
.
_slope
=
SlopeOperator
(
logk_space
)
self
.
_slope
=
self
.
_slope
(
makeOp
(
Field
.
from_global_data
(
self
.
_slope
.
domain
,
phi_sig
)))
self
.
_norm_phi_mean
=
Field
.
from_global_data
(
self
.
_slope
.
domain
,
phi_mean
/
phi_sig
)
smooth
=
CepstrumModel
(
logk_space
,
ceps_a
,
ceps_k
)
linear
=
SlopeModel
(
logk_space
,
sm
,
sv
,
im
,
iv
)
self
.
_domain
=
MultiDomain
.
make
({
keys
[
0
]:
dof_space
,
keys
[
1
]:
self
.
_slope
.
domain
})
self
.
_target
=
self
.
_exp_transform
.
target
self
.
_qht
,
self
.
_ceps
=
smooth
.
qht
,
smooth
.
ceps
self
.
_norm_phi_mean
=
linear
.
norm_phi_mean
kern
=
lambda
k
:
_ceps_kernel
(
dof_space
,
k
,
ceps_a
,
ceps_k
)
cepstrum
=
create_cepstrum_amplitude_field
(
dof_space
,
kern
)
self
.
_smooth_op
=
sym
(
qht
(
makeOp
(
sqrt
(
cepstrum
))))
self
.
_keys
=
tuple
(
keys
)
fa_smooth
=
FieldAdapter
(
smooth
.
domain
,
keys
[
0
])
fa_linear
=
FieldAdapter
(
linear
.
domain
,
keys
[
1
])
self
.
_qht
=
qht
self
.
_ceps
=
makeOp
(
sqrt
(
cepstrum
))
fac
=
ScalingOperator
(
0.5
,
smooth
.
target
)
self
.
_op
=
et
((
fac
(
smooth
(
fa_smooth
)
+
linear
(
fa_linear
))).
exp
())
self
.
_domain
,
self
.
_target
=
self
.
_op
.
domain
,
self
.
_op
.
target
def
apply
(
self
,
x
):
self
.
_check_input
(
x
)
smooth_spec
=
self
.
_smooth_op
(
x
[
self
.
_keys
[
0
]])
phi
=
x
[
self
.
_keys
[
1
]]
+
self
.
_norm_phi_mean
linear_spec
=
self
.
_slope
(
phi
)
loglog_spec
=
smooth_spec
+
linear_spec
return
self
.
_exp_transform
((
0.5
*
loglog_spec
)).
exp
()
return
self
.
_op
(
x
)
@
property
def
qht
(
self
):
...
...
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