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ift
NIFTy
Commits
14926005
Commit
14926005
authored
Nov 13, 2019
by
Philipp Arras
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Add prior statistics output summary
parent
ab3fc143
Changes
2
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2 changed files
with
82 additions
and
50 deletions
+82
-50
demos/getting_started_mf.py
demos/getting_started_mf.py
+0
-5
nifty5/library/correlated_fields.py
nifty5/library/correlated_fields.py
+82
-45
No files found.
demos/getting_started_mf.py
View file @
14926005
...
...
@@ -84,11 +84,6 @@ if __name__ == '__main__':
-
1.5
,
.
5
,
'amp2'
)
correlated_field
=
cfmaker
.
finalize
(
1e-3
,
1e-6
,
''
)
sams
=
[
ift
.
from_random
(
'normal'
,
correlated_field
.
domain
)
for
_
in
range
(
20
)]
print
(
"Prior expected total fluctuations: "
+
str
(
cfmaker
.
stats
(
cfmaker
.
total_fluctuation
,
sams
)[
0
]))
A1
=
cfmaker
.
amplitudes
[
0
]
A2
=
cfmaker
.
amplitudes
[
1
]
...
...
nifty5/library/correlated_fields.py
View file @
14926005
...
...
@@ -73,6 +73,20 @@ def _log_vol(power_space):
return
logk_lengths
[
1
:]
-
logk_lengths
[:
-
1
]
def
_total_fluctuation_realized
(
samples
):
res
=
0.
for
s
in
samples
:
res
=
res
+
(
s
-
s
.
mean
())
**
2
return
np
.
sqrt
((
res
/
len
(
samples
)).
mean
())
def
_stats
(
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
()
class
_LognormalMomentMatching
(
Operator
):
def
__init__
(
self
,
mean
,
sig
,
key
):
key
=
str
(
key
)
...
...
@@ -329,7 +343,8 @@ class CorrelatedFieldMaker:
offset_amplitude_mean
,
offset_amplitude_stddev
,
prefix
=
''
,
offset
=
None
):
offset
=
None
,
prior_info
=
100
):
"""
offset vs zeromode: volume factor
"""
...
...
@@ -343,7 +358,41 @@ class CorrelatedFieldMaker:
azm
=
_LognormalMomentMatching
(
offset_amplitude_mean
,
offset_amplitude_stddev
,
prefix
+
'zeromode'
)
return
self
.
finalize_from_op
(
azm
,
prefix
)
op
=
self
.
finalize_from_op
(
azm
,
prefix
)
if
prior_info
>
0
:
from
..sugar
import
from_random
samps
=
[
from_random
(
'normal'
,
op
.
domain
)
for
_
in
range
(
prior_info
)
]
self
.
statistics_summary
(
samps
)
return
op
def
statistics_summary
(
self
,
samples
):
lst
=
[(
'Offset amplitude'
,
self
.
amplitude_total_offset
),
(
'Total fluctuation amplitude'
,
self
.
total_fluctuation
)]
namps
=
len
(
self
.
amplitudes
)
if
namps
>
1
:
for
ii
in
range
(
namps
):
lst
.
append
((
'Slice fluctuation (space {})'
.
format
(
ii
),
self
.
slice_fluctuation
(
ii
)))
lst
.
append
((
'Average fluctuation (space {})'
.
format
(
ii
),
self
.
average_fluctuation
(
ii
)))
for
kk
,
op
in
lst
:
mean
,
stddev
=
_stats
(
op
,
samples
)
print
(
'{}: {:.02E} ± {:.02E}'
.
format
(
kk
,
mean
,
stddev
))
def
moment_slice_to_average
(
self
,
fluctuations_slice_mean
,
nsamples
=
1000
):
fluctuations_slice_mean
=
float
(
fluctuations_slice_mean
)
assert
fluctuations_slice_mean
>
0
scm
=
1.
for
a
in
self
.
_a
:
m
,
std
=
a
.
fluctuation_amplitude
.
mean
,
a
.
fluctuation_amplitude
.
std
mu
,
sig
=
_lognormal_moments
(
m
,
std
)
flm
=
np
.
exp
(
mu
+
sig
*
np
.
random
.
normal
(
size
=
nsamples
))
scm
*=
flm
**
2
+
1.
return
fluctuations_slice_mean
/
np
.
mean
(
np
.
sqrt
(
scm
))
@
property
def
amplitudes
(
self
):
...
...
@@ -355,6 +404,7 @@ class CorrelatedFieldMaker:
@
property
def
total_fluctuation
(
self
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
if
len
(
self
.
_a
)
==
1
:
...
...
@@ -366,6 +416,7 @@ class CorrelatedFieldMaker:
return
(
Adder
(
full
(
q
.
target
,
-
1.
))
@
q
).
sqrt
()
def
slice_fluctuation
(
self
,
space
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
assert
space
<
len
(
self
.
_a
)
...
...
@@ -381,6 +432,7 @@ class CorrelatedFieldMaker:
return
q
.
sqrt
()
def
average_fluctuation
(
self
,
space
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
assert
space
<
len
(
self
.
_a
)
...
...
@@ -388,61 +440,46 @@ class CorrelatedFieldMaker:
return
self
.
_a
[
0
].
fluctuation_amplitude
return
self
.
_a
[
space
].
fluctuation_amplitude
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
,)
@
staticmethod
def
offset_amplitude_realized
(
samples
):
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
())
res
+=
s
.
mean
()
**
2
return
np
.
sqrt
(
res
/
len
(
samples
))
def
slice_fluctuation_realized
(
self
,
samples
,
space
):
@
staticmethod
def
total_fluctuation_realized
(
samples
):
return
_total_fluctuation_realized
(
samples
)
@
staticmethod
def
slice_fluctuation_realized
(
samples
,
space
):
"""Computes slice fluctuations from collection of field (defined in signal
space) realizations."""
ldom
=
len
(
samples
[
0
].
domain
)
assert
space
<
ldom
if
ldom
==
1
:
return
self
.
total_fluctuation_realized
(
samples
)
return
_
total_fluctuation_realized
(
samples
)
res1
,
res2
=
0.
,
0.
for
s
in
samples
:
res1
+=
s
**
2
res2
+=
s
.
mean
(
space
)
**
2
return
np
.
sqrt
((
res1
-
res2
).
mean
()
/
len
(
samples
))
def
moment_slice_to_average
(
self
,
fluctuations_slice_mean
,
nsamples
=
1000
):
fluctuations_slice_mean
=
float
(
fluctuations_slice_mean
)
assert
fluctuations_slice_mean
>
0
scm
=
1.
for
a
in
self
.
_a
:
m
,
std
=
a
.
fluctuation_amplitude
.
mean
,
a
.
fluctuation_amplitude
.
std
mu
,
sig
=
_lognormal_moments
(
m
,
std
)
flm
=
np
.
exp
(
mu
+
sig
*
np
.
random
.
normal
(
size
=
nsamples
))
scm
*=
flm
**
2
+
1.
return
fluctuations_slice_mean
/
np
.
mean
(
np
.
sqrt
(
scm
))
@
staticmethod
def
offset_amplitude_realized
(
samples
):
res
=
0.
for
s
in
samples
:
res
+=
s
.
mean
()
**
2
return
np
.
sqrt
(
res
/
len
(
samples
))
@
staticmethod
def
total_fluctuation_realized
(
samples
):
def
average_fluctuation_realized
(
samples
,
space
):
"""Computes average fluctuations from collection of field (defined in signal
space) realizations."""
ldom
=
len
(
samples
[
0
].
domain
)
assert
space
<
ldom
if
ldom
==
1
:
return
_total_fluctuation_realized
(
samples
)
spaces
=
()
for
i
in
range
(
ldom
):
if
i
!=
space
:
spaces
+=
(
i
,)
res
=
0.
for
s
in
samples
:
res
=
res
+
(
s
-
s
.
mean
())
**
2
return
np
.
sqrt
((
res
/
len
(
samples
)).
mean
())
@
staticmethod
def
stats
(
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
()
r
=
s
.
mean
(
spaces
)
res
=
res
+
(
r
-
r
.
mean
())
**
2
res
=
res
/
len
(
samples
)
return
np
.
sqrt
(
res
.
mean
())
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