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
14926005
Commit
14926005
authored
Nov 13, 2019
by
Philipp Arras
Browse files
Add prior statistics output summary
parent
ab3fc143
Changes
2
Hide whitespace changes
Inline
Side-by-side
demos/getting_started_mf.py
View file @
14926005
...
@@ -84,11 +84,6 @@ if __name__ == '__main__':
...
@@ -84,11 +84,6 @@ if __name__ == '__main__':
-
1.5
,
.
5
,
-
1.5
,
.
5
,
'amp2'
)
'amp2'
)
correlated_field
=
cfmaker
.
finalize
(
1e-3
,
1e-6
,
''
)
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
]
A1
=
cfmaker
.
amplitudes
[
0
]
A2
=
cfmaker
.
amplitudes
[
1
]
A2
=
cfmaker
.
amplitudes
[
1
]
...
...
nifty5/library/correlated_fields.py
View file @
14926005
...
@@ -73,6 +73,20 @@ def _log_vol(power_space):
...
@@ -73,6 +73,20 @@ def _log_vol(power_space):
return
logk_lengths
[
1
:]
-
logk_lengths
[:
-
1
]
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
):
class
_LognormalMomentMatching
(
Operator
):
def
__init__
(
self
,
mean
,
sig
,
key
):
def
__init__
(
self
,
mean
,
sig
,
key
):
key
=
str
(
key
)
key
=
str
(
key
)
...
@@ -329,7 +343,8 @@ class CorrelatedFieldMaker:
...
@@ -329,7 +343,8 @@ class CorrelatedFieldMaker:
offset_amplitude_mean
,
offset_amplitude_mean
,
offset_amplitude_stddev
,
offset_amplitude_stddev
,
prefix
=
''
,
prefix
=
''
,
offset
=
None
):
offset
=
None
,
prior_info
=
100
):
"""
"""
offset vs zeromode: volume factor
offset vs zeromode: volume factor
"""
"""
...
@@ -343,7 +358,41 @@ class CorrelatedFieldMaker:
...
@@ -343,7 +358,41 @@ class CorrelatedFieldMaker:
azm
=
_LognormalMomentMatching
(
offset_amplitude_mean
,
azm
=
_LognormalMomentMatching
(
offset_amplitude_mean
,
offset_amplitude_stddev
,
offset_amplitude_stddev
,
prefix
+
'zeromode'
)
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
@
property
def
amplitudes
(
self
):
def
amplitudes
(
self
):
...
@@ -355,6 +404,7 @@ class CorrelatedFieldMaker:
...
@@ -355,6 +404,7 @@ class CorrelatedFieldMaker:
@
property
@
property
def
total_fluctuation
(
self
):
def
total_fluctuation
(
self
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
raise
NotImplementedError
if
len
(
self
.
_a
)
==
1
:
if
len
(
self
.
_a
)
==
1
:
...
@@ -366,6 +416,7 @@ class CorrelatedFieldMaker:
...
@@ -366,6 +416,7 @@ class CorrelatedFieldMaker:
return
(
Adder
(
full
(
q
.
target
,
-
1.
))
@
q
).
sqrt
()
return
(
Adder
(
full
(
q
.
target
,
-
1.
))
@
q
).
sqrt
()
def
slice_fluctuation
(
self
,
space
):
def
slice_fluctuation
(
self
,
space
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
raise
NotImplementedError
assert
space
<
len
(
self
.
_a
)
assert
space
<
len
(
self
.
_a
)
...
@@ -381,6 +432,7 @@ class CorrelatedFieldMaker:
...
@@ -381,6 +432,7 @@ class CorrelatedFieldMaker:
return
q
.
sqrt
()
return
q
.
sqrt
()
def
average_fluctuation
(
self
,
space
):
def
average_fluctuation
(
self
,
space
):
"""Returns operator which acts on prior or posterior samples"""
if
len
(
self
.
_a
)
==
0
:
if
len
(
self
.
_a
)
==
0
:
raise
NotImplementedError
raise
NotImplementedError
assert
space
<
len
(
self
.
_a
)
assert
space
<
len
(
self
.
_a
)
...
@@ -388,61 +440,46 @@ class CorrelatedFieldMaker:
...
@@ -388,61 +440,46 @@ class CorrelatedFieldMaker:
return
self
.
_a
[
0
].
fluctuation_amplitude
return
self
.
_a
[
0
].
fluctuation_amplitude
return
self
.
_a
[
space
].
fluctuation_amplitude
return
self
.
_a
[
space
].
fluctuation_amplitude
def
average_fluctuation_realized
(
self
,
samples
,
space
):
@
staticmethod
ldom
=
len
(
samples
[
0
].
domain
)
def
offset_amplitude_realized
(
samples
):
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.
res
=
0.
for
s
in
samples
:
for
s
in
samples
:
r
=
s
.
mean
(
spaces
)
res
+=
s
.
mean
()
**
2
res
=
res
+
(
r
-
r
.
mean
())
**
2
return
np
.
sqrt
(
res
/
len
(
samples
))
res
=
res
/
len
(
samples
)
return
np
.
sqrt
(
res
.
mean
())
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
)
ldom
=
len
(
samples
[
0
].
domain
)
assert
space
<
ldom
assert
space
<
ldom
if
ldom
==
1
:
if
ldom
==
1
:
return
self
.
total_fluctuation_realized
(
samples
)
return
_
total_fluctuation_realized
(
samples
)
res1
,
res2
=
0.
,
0.
res1
,
res2
=
0.
,
0.
for
s
in
samples
:
for
s
in
samples
:
res1
+=
s
**
2
res1
+=
s
**
2
res2
+=
s
.
mean
(
space
)
**
2
res2
+=
s
.
mean
(
space
)
**
2
return
np
.
sqrt
((
res1
-
res2
).
mean
()
/
len
(
samples
))
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
@
staticmethod
def
offset_amplitude_realized
(
samples
):
def
average_fluctuation_realized
(
samples
,
space
):
res
=
0.
"""Computes average fluctuations from collection of field (defined in signal
for
s
in
samples
:
space) realizations."""
res
+=
s
.
mean
()
**
2
ldom
=
len
(
samples
[
0
].
domain
)
return
np
.
sqrt
(
res
/
len
(
samples
))
assert
space
<
ldom
if
ldom
==
1
:
@
staticmethod
return
_total_fluctuation_realized
(
samples
)
def
total_fluctuation_realized
(
samples
):
spaces
=
()
for
i
in
range
(
ldom
):
if
i
!=
space
:
spaces
+=
(
i
,)
res
=
0.
res
=
0.
for
s
in
samples
:
for
s
in
samples
:
res
=
res
+
(
s
-
s
.
mean
())
**
2
r
=
s
.
mean
(
spaces
)
return
np
.
sqrt
((
res
/
len
(
samples
)).
mean
())
res
=
res
+
(
r
-
r
.
mean
())
**
2
res
=
res
/
len
(
samples
)
@
staticmethod
return
np
.
sqrt
(
res
.
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
()
Write
Preview
Supports
Markdown
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