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Gregory Ashton
PyFstat
Commits
1cb3e05d
Commit
1cb3e05d
authored
Aug 24, 2017
by
Gregory Ashton
Browse files
Update to Nstar description
parent
800659c1
Changes
2
Hide whitespace changes
Inline
Side-by-side
pyfstat/mcmc_based_searches.py
View file @
1cb3e05d
...
...
@@ -1872,9 +1872,9 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
d
=
pickle
.
load
(
f
)
return
d
def
write_setup_input_file
(
self
,
run_setup_input_file
,
R
,
Nsegs0
,
def
write_setup_input_file
(
self
,
run_setup_input_file
,
NstarMax
,
Nsegs0
,
nsegs_vals
,
Nstar_vals
,
theta_prior
):
d
=
dict
(
R
=
R
,
Nsegs0
=
Nsegs0
,
nsegs_vals
=
nsegs_vals
,
d
=
dict
(
NstarMax
=
NstarMax
,
Nsegs0
=
Nsegs0
,
nsegs_vals
=
nsegs_vals
,
theta_prior
=
theta_prior
,
Nstar_vals
=
Nstar_vals
)
with
open
(
run_setup_input_file
,
'w+'
)
as
f
:
pickle
.
dump
(
d
,
f
)
...
...
@@ -1886,19 +1886,19 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
return
True
else
:
logging
.
info
(
'Old setup does not match one of
R
, Nsegs0 or prior'
)
'Old setup does not match one of
NstarMax
, Nsegs0 or prior'
)
except
KeyError
as
e
:
logging
.
info
(
'Error found when comparing with old setup: {}'
.
format
(
e
))
return
False
def
init_run_setup
(
self
,
run_setup
=
None
,
R
=
1
0
,
Nsegs0
=
None
,
log_table
=
True
,
gen_tex_table
=
True
):
def
init_run_setup
(
self
,
run_setup
=
None
,
NstarMax
=
100
0
,
Nsegs0
=
None
,
log_table
=
True
,
gen_tex_table
=
True
):
if
run_setup
is
None
and
Nsegs0
is
None
:
raise
ValueError
(
'You must either specify the run_setup, or Nsegs0
from which
'
'the optimal run_setup
given R
can be estimated'
)
'You must either specify the run_setup, or Nsegs0
and NStarMax
'
'
from which
the optimal run_setup can be estimated'
)
if
run_setup
is
None
:
logging
.
info
(
'No run_setup provided'
)
...
...
@@ -1909,11 +1909,12 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
logging
.
info
(
'Checking old setup input file {}'
.
format
(
run_setup_input_file
))
old_setup
=
self
.
read_setup_input_file
(
run_setup_input_file
)
if
self
.
check_old_run_setup
(
old_setup
,
R
=
R
,
if
self
.
check_old_run_setup
(
old_setup
,
NstarMax
=
NstarMax
,
Nsegs0
=
Nsegs0
,
theta_prior
=
self
.
theta_prior
):
logging
.
info
(
'Using old setup with R={}, Nsegs0={}'
.
format
(
R
,
Nsegs0
))
logging
.
info
(
'Using old setup with NstarMax={}, Nsegs0={}'
.
format
(
NstarMax
,
Nsegs0
))
nsegs_vals
=
old_setup
[
'nsegs_vals'
]
Nstar_vals
=
old_setup
[
'Nstar_vals'
]
generate_setup
=
False
...
...
@@ -1924,11 +1925,11 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
if
generate_setup
:
nsegs_vals
,
Nstar_vals
=
get_optimal_setup
(
R
,
Nsegs0
,
self
.
tref
,
self
.
minStartTime
,
NstarMax
,
Nsegs0
,
self
.
tref
,
self
.
minStartTime
,
self
.
maxStartTime
,
self
.
theta_prior
,
self
.
search
.
detector_names
,
self
.
earth_ephem
,
self
.
sun_ephem
)
self
.
write_setup_input_file
(
run_setup_input_file
,
R
,
Nsegs0
,
self
.
write_setup_input_file
(
run_setup_input_file
,
NstarMax
,
Nsegs0
,
nsegs_vals
,
Nstar_vals
,
self
.
theta_prior
)
...
...
@@ -2002,9 +2003,9 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
else
:
return
run_setup
def
run
(
self
,
run_setup
=
None
,
proposal_scale_factor
=
2
,
R
=
10
,
Nsegs0
=
None
,
create_plots
=
True
,
log_table
=
True
,
gen_tex_table
=
True
,
fig
=
None
,
axes
=
None
,
return_fig
=
False
,
**
kwargs
):
def
run
(
self
,
run_setup
=
None
,
proposal_scale_factor
=
2
,
NstarMax
=
10
,
Nsegs0
=
None
,
create_plots
=
True
,
log_table
=
True
,
gen_tex_table
=
True
,
fig
=
None
,
axes
=
None
,
return_fig
=
False
,
**
kwargs
):
""" Run the follow-up with the given run_setup
Parameters
...
...
@@ -2029,7 +2030,7 @@ class MCMCFollowUpSearch(MCMCSemiCoherentSearch):
self
.
nsegs
=
1
self
.
_initiate_search_object
()
run_setup
=
self
.
init_run_setup
(
run_setup
,
R
=
R
,
Nsegs0
=
Nsegs0
,
log_table
=
log_table
,
run_setup
,
NstarMax
=
NstarMax
,
Nsegs0
=
Nsegs0
,
log_table
=
log_table
,
gen_tex_table
=
gen_tex_table
)
self
.
run_setup
=
run_setup
...
...
pyfstat/optimal_setup_functions.py
View file @
1cb3e05d
...
...
@@ -14,13 +14,13 @@ import pyfstat.helper_functions as helper_functions
def
get_optimal_setup
(
R
,
Nsegs0
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
NstarMax
,
Nsegs0
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
detector_names
,
earth_ephem
,
sun_ephem
):
""" Using an optimisation step, calculate the optimal setup ladder
Parameters
----------
R
: float
NstarMax
: float
Nsegs0 : int
The number of segments for the initial step of the ladder
minStartTime, maxStartTime : int
...
...
@@ -38,8 +38,8 @@ def get_optimal_setup(
"""
logging
.
info
(
'Calculating optimal setup for
R
={}, Nsegs0={}'
.
format
(
R
,
Nsegs0
))
logging
.
info
(
'Calculating optimal setup for
NstarMax
={}, Nsegs0={}'
.
format
(
NstarMax
,
Nsegs0
))
Nstar_0
=
get_Nstar_estimate
(
Nsegs0
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
...
...
@@ -54,7 +54,7 @@ def get_optimal_setup(
nsegs_i
=
Nsegs0
while
nsegs_i
>
1
:
nsegs_i
,
Nstar_i
=
_get_nsegs_ip1
(
nsegs_i
,
R
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
nsegs_i
,
NstarMax
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
detector_names
,
earth_ephem
,
sun_ephem
)
nsegs_vals
.
append
(
nsegs_i
)
Nstar_vals
.
append
(
Nstar_i
)
...
...
@@ -65,11 +65,11 @@ def get_optimal_setup(
return
nsegs_vals
,
Nstar_vals
def
_get_nsegs_ip1
(
nsegs_i
,
R
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
def
_get_nsegs_ip1
(
nsegs_i
,
NstarMax
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
detector_names
,
earth_ephem
,
sun_ephem
):
""" Calculate Nsegs_{i+1} given Nsegs_{i} """
log10
R
=
np
.
log10
(
R
)
log10
NstarMax
=
np
.
log10
(
NstarMax
)
log10Nstari
=
np
.
log10
(
get_Nstar_estimate
(
nsegs_i
,
tref
,
minStartTime
,
maxStartTime
,
prior
,
detector_names
,
earth_ephem
,
sun_ephem
))
...
...
@@ -89,9 +89,10 @@ def _get_nsegs_ip1(nsegs_i, R, tref, minStartTime, maxStartTime, prior,
return
1e6
else
:
log10Nstarip1
=
np
.
log10
(
Nstarip1
)
return
np
.
abs
(
log10Nstari
+
log10
R
-
log10Nstarip1
)
res
=
scipy
.
optimize
.
minimize
(
f
,
.
5
*
nsegs_i
,
method
=
'Powell'
,
tol
=
0.
1
,
return
np
.
abs
(
log10Nstari
+
log10
NstarMax
-
log10Nstarip1
)
res
=
scipy
.
optimize
.
minimize
(
f
,
.
4
*
nsegs_i
,
method
=
'Powell'
,
tol
=
1
,
options
=
{
'maxiter'
:
10
})
logging
.
info
(
'{} with {} evaluations'
.
format
(
res
[
'message'
],
res
[
'nfev'
]))
nsegs_ip1
=
int
(
res
.
x
)
if
nsegs_ip1
==
0
:
nsegs_ip1
=
1
...
...
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