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ift
NIFTy
Commits
3d3e41dc
Commit
3d3e41dc
authored
Nov 10, 2019
by
Philipp Frank
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Plain Diff
utility functions for fluctuations
parent
e01ad3a5
Pipeline
#63362
passed with stages
in 5 minutes and 48 seconds
Changes
2
Pipelines
1
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Inline
Side-by-side
Showing
2 changed files
with
124 additions
and
58 deletions
+124
-58
demos/multi_amplitudes_consistency.py
demos/multi_amplitudes_consistency.py
+32
-44
nifty5/library/correlated_fields.py
nifty5/library/correlated_fields.py
+92
-14
No files found.
demos/multi_amplitudes_consistency.py
View file @
3d3e41dc
...
...
@@ -5,9 +5,11 @@ np.random.seed(42)
def
testAmplitudesConsistency
(
seed
,
sspace
):
offset_std
=
40
intergated_fluct_std0
=
10.
intergated_fluct_std1
=
2.
offset_std
=
30
intergated_fluct_std0
=
.
003
intergated_fluct_std1
=
0.1
nsam
=
1000
hspace
=
sspace
.
get_default_codomain
()
target0
=
ift
.
PowerSpace
(
hspace
)
...
...
@@ -23,51 +25,29 @@ def testAmplitudesConsistency(seed, sspace):
-
4
,
1.
,
'freq'
)
op
=
fa
.
finalize
(
offset_std
,
1E-8
,
''
)
flucts
=
[
intergated_fluct_std0
,
intergated_fluct_std1
]
tot_flm
,
totflsig
=
fa
.
effective_total_fluctuation
(
flucts
,
[
1E-8
,
1E-8
])
space
=
op
.
target
totaltoalvol
=
1.
for
s
in
space
[:]:
totaltoalvol
*=
s
.
total_volume
nsam
=
1000
zm_std_mean
=
0.
fluct_space
=
0.
fluct_freq
=
0.
fluct_total
=
0.
for
i
in
range
(
nsam
):
x
=
ift
.
from_random
(
'normal'
,
op
.
domain
)
res
=
op
(
x
)
zm
=
res
.
integrate
()
/
totaltoalvol
zm2
=
res
.
mean
()
fl
=
((
res
-
zm
)
**
2
).
integrate
()
/
totaltoalvol
zm_std_mean
+=
zm
**
2
fluct_total
+=
fl
r
=
res
.
integrate
(
1
)
/
fsspace
.
total_volume
r0
=
r
.
integrate
()
/
sspace
.
total_volume
tm
=
((
r
-
r0
)
**
2
).
integrate
()
/
sspace
.
total_volume
fluct_space
+=
tm
fr
=
res
.
integrate
(
0
)
/
sspace
.
total_volume
fr0
=
fr
.
integrate
()
/
fsspace
.
total_volume
ftm
=
((
fr
-
fr0
)
**
2
).
integrate
()
/
fsspace
.
total_volume
fluct_freq
+=
ftm
fluct_total
=
np
.
sqrt
(
fluct_total
/
nsam
)
fluct_space
=
np
.
sqrt
(
fluct_space
/
nsam
)
fluct_freq
=
np
.
sqrt
(
fluct_freq
/
nsam
)
zm_std_mean
=
np
.
sqrt
(
zm_std_mean
/
nsam
)
samples
=
[
ift
.
from_random
(
'normal'
,
op
.
domain
)
for
_
in
range
(
nsam
)]
tot_flm
,
_
=
fa
.
stats
(
fa
.
total_fluctuation
,
samples
)
offset_std
,
_
=
fa
.
stats
(
fa
.
amplitude_total_offset
,
samples
)
intergated_fluct_std0
,
_
=
fa
.
stats
(
fa
.
average_fluctuation
(
0
),
samples
)
intergated_fluct_std1
,
_
=
fa
.
stats
(
fa
.
average_fluctuation
(
1
),
samples
)
slice_fluct_std0
,
_
=
fa
.
stats
(
fa
.
slice_fluctuation
(
0
),
samples
)
slice_fluct_std1
,
_
=
fa
.
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
)
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
)
print
(
"Expected offset Std: "
+
str
(
offset_std
))
print
(
"Estimated offset Std: "
+
str
(
zm_std_mean
))
...
...
@@ -79,6 +59,14 @@ def testAmplitudesConsistency(seed, sspace):
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
))
...
...
nifty5/library/correlated_fields.py
View file @
3d3e41dc
...
...
@@ -32,6 +32,7 @@ from ..operators.operator import Operator
from
..operators.simple_linear_operators
import
VdotOperator
,
ducktape
from
..operators.value_inserter
import
ValueInserter
from
..sugar
import
from_global_data
,
full
,
makeDomain
from
..probing
import
StatCalculator
def
_lognormal_moments
(
mean
,
sig
):
...
...
@@ -207,12 +208,14 @@ class _Amplitude(Operator):
adder
=
Adder
(
from_global_data
(
target
,
mask
))
op
=
adder
@
((
expander
@
fluctuations
)
*
normal_ampl
)
self
.
apply
=
op
.
apply
self
.
fluctuation_amplitude
=
fluctuations
self
.
_domain
,
self
.
_target
=
op
.
domain
,
op
.
target
class
CorrelatedFieldMaker
:
def
__init__
(
self
):
self
.
_a
=
[]
self
.
_azm
=
None
def
add_fluctuations
(
self
,
target
,
...
...
@@ -256,6 +259,7 @@ class CorrelatedFieldMaker:
def
finalize_from_op
(
self
,
zeromode
,
prefix
=
''
):
assert
isinstance
(
zeromode
,
Operator
)
self
.
_azm
=
zeromode
hspace
=
makeDomain
([
dd
.
target
[
0
].
harmonic_partner
for
dd
in
self
.
_a
])
foo
=
np
.
ones
(
hspace
.
shape
)
zeroind
=
len
(
hspace
.
shape
)
*
(
0
,)
...
...
@@ -305,17 +309,91 @@ class CorrelatedFieldMaker:
def
amplitudes
(
self
):
return
self
.
_a
def
effective_total_fluctuation
(
self
,
fluctuations_means
,
fluctuations_stddevs
,
nsamples
=
100
):
namps
=
len
(
fluctuations_means
)
xis
=
np
.
random
.
normal
(
size
=
namps
*
nsamples
).
reshape
((
namps
,
nsamples
))
q
=
np
.
ones
(
nsamples
)
for
i
in
range
(
len
(
fluctuations_means
)):
m
,
sig
=
_lognormal_moments
(
fluctuations_means
[
i
],
fluctuations_stddevs
[
i
])
f
=
np
.
exp
(
m
+
sig
*
xis
[
i
])
q
*=
(
1.
+
f
**
2
)
q
=
np
.
sqrt
(
q
-
1.
)
return
np
.
mean
(
q
),
np
.
std
(
q
)
@
property
def
amplitude_total_offset
(
self
):
return
self
.
_azm
@
property
def
total_fluctuation
(
self
):
if
len
(
self
.
_a
)
==
0
:
raise
(
NotImplementedError
)
if
len
(
self
.
_a
)
==
1
:
return
self
.
_a
[
0
].
fluctuation_amplitude
q
=
1.
for
a
in
self
.
_a
:
fl
=
a
.
fluctuation_amplitude
q
=
q
*
(
Adder
(
full
(
fl
.
target
,
1.
))
@
fl
**
2
)
return
(
Adder
(
full
(
q
.
target
,
-
1.
))
@
q
).
sqrt
()
def
slice_fluctuation
(
self
,
space
):
if
len
(
self
.
_a
)
==
0
:
raise
(
NotImplementedError
)
assert
space
<
len
(
self
.
_a
)
if
len
(
self
.
_a
)
==
1
:
return
self
.
_a
[
0
].
fluctuation_amplitude
q
=
1.
for
j
in
range
(
len
(
self
.
_a
)):
fl
=
self
.
_a
[
j
].
fluctuation_amplitude
if
j
==
space
:
q
=
q
*
fl
**
2
else
:
q
=
q
*
(
Adder
(
full
(
fl
.
target
,
1.
))
@
fl
**
2
)
return
q
.
sqrt
()
def
average_fluctuation
(
self
,
space
):
if
len
(
self
.
_a
)
==
0
:
raise
(
NotImplementedError
)
assert
space
<
len
(
self
.
_a
)
if
len
(
self
.
_a
)
==
1
:
return
self
.
_a
[
0
].
fluctuation_amplitude
return
self
.
_a
[
space
].
fluctuation_amplitude
def
offset_amplitude_realized
(
self
,
samples
):
res
=
0.
for
s
in
samples
:
res
+=
s
.
mean
()
**
2
return
np
.
sqrt
(
res
/
len
(
samples
))
def
total_fluctuation_realized
(
self
,
samples
):
res
=
0.
for
s
in
samples
:
res
=
res
+
(
s
-
s
.
mean
())
**
2
res
=
res
/
len
(
samples
)
return
np
.
sqrt
(
res
.
mean
())
def
average_fluctuation_realized
(
self
,
samples
,
space
):
ldom
=
len
(
samples
[
0
].
domain
)
assert
space
<
ldom
if
ldom
==
1
:
return
self
.
total_fluctuation_realized
(
samples
)
spaces
=
()
for
i
in
range
(
ldom
):
if
i
!=
space
:
spaces
+=
(
i
,)
res
=
0.
for
s
in
samples
:
r
=
s
.
mean
(
spaces
)
res
=
res
+
(
r
-
r
.
mean
())
**
2
res
=
res
/
len
(
samples
)
return
np
.
sqrt
(
res
.
mean
())
def
slice_fluctuation_realized
(
self
,
samples
,
space
):
ldom
=
len
(
samples
[
0
].
domain
)
assert
space
<
ldom
if
ldom
==
1
:
return
self
.
total_fluctuation_realized
(
samples
)
res1
=
0.
res2
=
0.
for
s
in
samples
:
res1
=
res1
+
s
**
2
res2
=
res2
+
s
.
mean
(
space
)
**
2
res1
=
res1
/
len
(
samples
)
res2
=
res2
/
len
(
samples
)
res
=
res1
.
mean
()
-
res2
.
mean
()
return
np
.
sqrt
(
res
)
def
stats
(
self
,
op
,
samples
):
sc
=
StatCalculator
()
for
s
in
samples
:
sc
.
add
(
op
(
s
.
extract
(
op
.
domain
)))
return
sc
.
mean
.
to_global_data
(),
sc
.
var
.
sqrt
().
to_global_data
()
\ No newline at end of file
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