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Yifan Wang
RDStackingProject
Commits
e4815c0e
Commit
e4815c0e
authored
4 years ago
by
frcojimenez
Browse files
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added config -c option and 1-sigma band plots
parent
60415167
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code_new/NR_dynesty_t0_loop.ipynb
+200
-137
200 additions, 137 deletions
code_new/NR_dynesty_t0_loop.ipynb
code_new/NR_dynesty_t0_loop.py
+145
-61
145 additions, 61 deletions
code_new/NR_dynesty_t0_loop.py
code_new/run.sh
+7
-5
7 additions, 5 deletions
code_new/run.sh
with
352 additions
and
203 deletions
code_new/NR_dynesty_t0_loop.ipynb
+
200
−
137
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e4815c0e
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code_new/NR_dynesty_t0_loop.py
+
145
−
61
View file @
e4815c0e
#!/usr/bin/env python
# coding: utf-8
# In[
99
]:
# In[
63
]:
#Import relevant modules, import data and all that
...
...
@@ -30,6 +30,10 @@ import dynesty
from
dynesty
import
plotting
as
dyplot
import
os
import
csv
import
argparse
import
scipy.optimize
as
optimization
from
scipy.optimize
import
minimize
#Remember to change the following global variables
#rootpath: root path to nr data
...
...
@@ -38,16 +42,25 @@ import csv
#tshift: time shift after the strain peak
#vary_fund: whether you vary the fundamental frequency. Works in the model_dv function.
try
:
parser
=
argparse
.
ArgumentParser
(
description
=
"
Simple argument parser
"
)
parser
.
add_argument
(
"
-c
"
,
action
=
"
store
"
,
dest
=
"
config_file
"
)
result
=
parser
.
parse_args
()
config_file
=
result
.
config_file
parser
=
ConfigParser
()
parser
.
read
(
config_file
)
parser
.
sections
()
except
SystemExit
:
parser
=
ConfigParser
()
parser
.
read
(
'
config
_q10n
.ini
'
)
parser
.
read
(
'
config.ini
'
)
parser
.
sections
()
pass
# In[
100
]:
# In[
36
]:
# path
rootpath
=
parser
.
get
(
'
nr-paths
'
,
'
rootpath
'
)
simulation_path_1
=
parser
.
get
(
'
nr-paths
'
,
'
simulation_path_1
'
)
...
...
@@ -57,17 +70,18 @@ simulation_number = parser.get('nr-paths','simulation_number')
simulation_number
=
np
.
int
(
simulation_number
)
output_folder
=
parser
.
get
(
'
output-folder
'
,
'
output-folder
'
)
overwrite
=
parser
.
get
(
'
overwrite
'
,
'
overwrite
'
)
# In[
101
]:
# In[
37
]:
if
not
os
.
path
.
exists
(
output_folder
):
os
.
mkdir
(
dirName
)
os
.
mkdir
(
output_folder
)
print
(
"
Directory
"
,
output_folder
,
"
Created
"
)
# In[
102
]:
# In[
38
]:
# time config
...
...
@@ -82,7 +96,7 @@ t_align=parser.get('time-setup','t_align')
t_align
=
np
.
float
(
t_align
)
# In[
10
3]:
# In[3
9
]:
# n-tones & nlive
...
...
@@ -94,7 +108,7 @@ npoints=parser.get('n-live-points','npoints')
npoints
=
np
.
int
(
npoints
)
# In[
10
4]:
# In[4
0
]:
# model
...
...
@@ -106,8 +120,26 @@ elif model == 'w-q':
elif
model
==
'
w-tau-fixed
'
:
tau_var_str
=
'
q
'
print
(
'
model:
'
,
model
)
print
(
'
nmax
'
,
nmax
)
# In[41]:
tshift
# In[105]:
# In[42]:
output_folder_1
=
output_folder
+
'
/
'
+
model
+
'
-nmax
'
+
str
(
nmax
)
if
not
os
.
path
.
exists
(
output_folder_1
):
os
.
mkdir
(
output_folder_1
)
print
(
"
Directory
"
,
output_folder_1
,
"
Created
"
)
# In[43]:
# loading priors
...
...
@@ -156,7 +188,7 @@ if model == 'w-tau-fixed':
prior_dim
=
len
(
priors_min
)
# In[
106
]:
# In[
44
]:
vary_fund
=
True
...
...
@@ -195,8 +227,16 @@ def EasyMatchT(t,h1,h2,tmin,tmax):
return
res
def
wRD_to_f_Phys
(
f
,
M
):
c
=
2.99792458
*
10
**
8
;
G
=
6.67259
*
10
**
(
-
11
);
MS
=
1.9885
*
10
**
30
;
return
(
c
**
3
/
(
M
*
MS
*
G
*
2
*
np
.
pi
))
*
f
# In[107]:
def
tauRD_to_t_Phys
(
tau
,
M
):
c
=
2.99792458
*
10
**
8
;
G
=
6.67259
*
10
**
(
-
11
);
MS
=
1.9885
*
10
**
30
;
return
((
M
*
MS
*
G
)
/
c
**
3
)
*
tau
# In[45]:
#This loads the 22 mode data
...
...
@@ -228,7 +268,7 @@ tmax5=FindTmaximum(gw5_sxs_bbh_0305)
times5
=
times5
-
tmax5
# In[
108
]:
# In[
46
]:
#Select the data from 0 onwards
...
...
@@ -240,7 +280,7 @@ timesrd=gw_sxs_bbh_0305[position:-1][:,0][:-1]-tmax
timesrd5
=
gw5_sxs_bbh_0305
[
position5
:
-
1
][:,
0
][:
-
1
]
-
tmax5
# In[
109
]:
# In[
47
]:
#Test plot real part (data was picked in the last cell). Aligning in time
...
...
@@ -252,7 +292,7 @@ plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2
plt
.
legend
()
# In[
110
]:
# In[
48
]:
#Test plot im part (data was picked in the last cell). Aligning in time
...
...
@@ -264,7 +304,7 @@ plt.plot(timesrd5, np.sqrt(gw_sxs_bbh_0305rd5[:,1]**2+gw_sxs_bbh_0305rd5[:,2]**2
plt
.
legend
()
# In[
111
]:
# In[
49
]:
# Depending on nmax, you load nmax number of freqs. and damping times from the qnm package
...
...
@@ -273,7 +313,7 @@ w = (np.real(omegas))/mf
tau
=-
1
/
(
np
.
imag
(
omegas
))
*
mf
# In[
112
]:
# In[
50
]:
gwnew_re
=
interpolate
.
interp1d
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
1
],
kind
=
'
cubic
'
)
...
...
@@ -283,7 +323,7 @@ gwnew_re5 = interpolate.interp1d(timesrd5, gw_sxs_bbh_0305rd5[:,1], kind = 'cubi
gwnew_im5
=
interpolate
.
interp1d
(
timesrd5
,
gw_sxs_bbh_0305rd5
[:,
2
],
kind
=
'
cubic
'
)
# In[1
13
]:
# In[
5
1]:
if
timesrd5
[
-
1
]
>=
timesrd
[
-
1
]:
...
...
@@ -300,7 +340,7 @@ gwdatanew = gwdatanew_re - 1j*gwdatanew_im
gwdatanew5
=
gwdatanew_re5
-
1j
*
gwdatanew_im5
# In[
114
]:
# In[
52
]:
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
0
+
90
)
...
...
@@ -308,7 +348,7 @@ error=np.sqrt(2*mismatch)
print
(
mismatch
)
# In[
11
5]:
# In[5
3
]:
# Phase alignement
...
...
@@ -320,7 +360,7 @@ plt.plot(timesrd_final, phas, "r", alpha=0.3, lw=3, label=r'$phase$')
plt
.
plot
(
timesrd_final
,
phas5
,
"
blue
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
# In[
116
]:
# In[
54
]:
position
=
np
.
argmax
(
timesrd_final
>=
(
t_align
))
...
...
@@ -339,15 +379,15 @@ plt.plot(timesrd_final, phas, "r", alpha=0.3, lw=3, label=r'$phase$')
plt
.
plot
(
timesrd_final
,
phas5
,
"
blue
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
# In[
117
]:
# In[
55
]:
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
+
90
)
print
(
mismatch
)
error
=
np
.
sqrt
(
2
*
gwdatanew
*
gwdatanew
-
2
*
gwdatanew
*
gwdatanew5
)
error
=
np
.
sqrt
(
gwdatanew
*
gwdatanew
-
2
*
gwdatanew
*
gwdatanew5
+
gwdatanew5
*
gwdatanew5
)
# In[
118
]:
# In[
56
]:
#Test the new interpolated data
...
...
@@ -358,7 +398,7 @@ plt.plot(timesrd_final, error.real, "b", alpha=0.3, lw=2, label='error')
plt
.
legend
()
# In[
119
]:
# In[
57
]:
#Test the error data
...
...
@@ -369,7 +409,7 @@ plt.plot(timesrd_final, np.sqrt(error.imag**2+error.real**2), "r", alpha=0.3, lw
plt
.
legend
()
# In[
120
]:
# In[
58
]:
#Take the piece of waveform you want
...
...
@@ -381,7 +421,7 @@ gwdatanew_im_tsh = gwdatanew_im[position_in:position_end]
error_tsh
=
error
[
position_in
:
position_end
]
# In[
12
5]:
# In[5
9
]:
#Fitting
...
...
@@ -456,20 +496,34 @@ def log_probability(theta):
return
lp
+
log_likelihood
(
theta
)
# In[
12
6]:
# In[6
0
]:
dict
=
{
'
w-tau
'
:
model_dv_tau
,
'
w-q
'
:
model_dv_q
,
'
w-tau-fixed
'
:
model_dv
}
# In[128]:
# In[64]:
#I need to provid an initial guess for 4*(nmax+1) the parameters
np
.
random
.
seed
(
42
)
nll
=
lambda
*
args
:
-
log_likelihood
(
*
args
)
initial
=
np
.
ones
(
prior_dim
)
soln
=
minimize
(
nll
,
initial
)
#x0_ml, y0_ml, a0_ml, b0_ml = soln.x
print
(
"
Maximum likelihood estimates:
"
)
vars_ml
=
soln
.
x
print
(
vars_ml
)
f2
=
dynesty
.
NestedSampler
(
log_likelihood
,
prior_transform
,
prior_dim
,
nlive
=
npoints
)
# In[75]:
f2
=
dynesty
.
NestedSampler
(
log_likelihood
,
prior_transform
,
prior_dim
,
nlive
=
npoints
,
sample
=
'
rwalk
'
)
f2
.
run_nested
()
# In[1
40
]:
# In[
9
1]:
wstr
=
r
'
$\omega_
'
...
...
@@ -501,7 +555,7 @@ if model=='w-tau-fixed':
labels
=
np
.
concatenate
((
amp_lab
,
pha_lab
))
# In[
20
2]:
# In[
9
2]:
if
model
==
'
w-tau-fixed
'
:
...
...
@@ -524,7 +578,7 @@ else:
npamps
[
i
]
=
np
.
quantile
(
amps_aux
,
0.5
)
# In[
20
3]:
# In[
9
3]:
res
=
f2
.
results
...
...
@@ -533,43 +587,54 @@ res.summary()
samps
=
f2
.
results
.
samples
# In[
204
]:
# In[
79
]:
evidence
=
res
.
logz
[
-
1
]
evidence_error
=
res
.
logzerr
[
-
1
]
# In[
205
]:
# In[
80
]:
outvalues
=
[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence
_err
or
]
summary_titles
=
[
'
n
'
,
'
id
'
,
'
t_shift
'
,
'
dlogz
'
,
'
dlogz
_err
'
]
# In[
206
]:
# In[
81
]:
f
=
open
(
output_folder
+
'
/summary
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
,
'
w
'
)
f
=
output_folder
_1
+
'
/summary
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
with
f
:
writer
=
csv
.
writer
(
f
)
writer
.
writerows
(
map
(
lambda
x
:
[
x
],
outvalues
))
# In[82]:
if
os
.
path
.
exists
(
f
):
outvalues
=
np
.
array
([[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
else
:
outvalues
=
np
.
array
([
summary_titles
,[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
with
open
(
f
,
'
a
'
)
as
file
:
writer
=
csv
.
writer
(
file
)
if
(
outvalues
.
shape
)[
0
]
>
1
:
writer
.
writerows
(
outvalues
)
else
:
writer
.
writerow
(
outvalues
[
0
])
# In[
207
]:
# In[
83
]:
samps
=
f2
.
results
.
samples
samps_tr
=
np
.
transpose
(
samps
)
# In[
20
8]:
# In[8
4
]:
sigma_vars_m
=
np
.
empty
(
n
dim
)
sigma_vars_p
=
np
.
empty
(
n
dim
)
sigma_vars
=
np
.
empty
(
n
dim
)
sigma_vars_m
=
np
.
empty
(
prior_
dim
)
sigma_vars_p
=
np
.
empty
(
prior_
dim
)
sigma_vars
=
np
.
empty
(
prior_
dim
)
for
i
in
range
(
prior_dim
):
amps_aux
=
samps_tr
[
i
][
half_points
:
-
1
]
sigma_vars_m
[
i
]
=
np
.
quantile
(
amps_aux
,
0.1
)
...
...
@@ -577,38 +642,39 @@ for i in range(prior_dim):
sigma_vars_p
[
i
]
=
np
.
quantile
(
amps_aux
,
0.9
)
# In[
209
]:
# In[
85
]:
sigma_vars_all
=
[
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
]
# In[
210
]:
# In[
86
]:
sigma_vars_all
=
np
.
stack
([
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
],
axis
=
0
)
# In[
211
]:
# In[
87
]:
dfslist
=
[
pd
.
DataFrame
(
sigma_vars_all
[
i
].
reshape
((
-
1
,
prior_dim
)),
columns
=
labels
)
for
i
in
range
(
prior_dim
-
1
)]
df2
=
pd
.
concat
(
dfslist
,
keys
=
[
'
max val
'
,
'
lower bound
'
,
'
higher bound
'
])
df2
.
to_csv
(
output_folder
+
'
/best_values_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
,
index
=
False
)
key
=
[
'
max val
'
,
'
lower bound
'
,
'
higher bound
'
]
file
=
output_folder_1
+
'
/best_values_
'
+
str
(
simulation_number
)
+
'
tshift_
'
+
str
(
tshift
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
dfslist
=
[
pd
.
DataFrame
(
sigma_vars_all
[
i
].
reshape
((
-
1
,
prior_dim
)),
columns
=
labels
,
index
=
[
key
[
i
]])
for
i
in
range
(
3
)]
df2
=
pd
.
concat
(
dfslist
)
df2
.
to_csv
(
file
,
index
=
False
)
# In[
212
]:
# In[
88
]:
f
=
open
(
output_folder
+
'
/best_sigmas_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_
'
+
str
(
nmax
)
+
'
.csv
'
,
'
w
'
)
f
=
open
(
output_folder_1
+
'
/best_sigmas_
'
+
str
(
simulation_number
)
+
'
tshift_
'
+
str
(
tshift
)
+
'
_
'
+
model
+
'
_
'
+
str
(
nmax
)
+
'
.csv
'
,
'
w
'
)
with
f
:
writer
=
csv
.
writer
(
f
)
writer
.
writerows
(
map
(
lambda
x
:
[
x
],
df2
))
# In[
213
]:
# In[
89
]:
if
model
==
'
w-q
'
:
...
...
@@ -621,7 +687,7 @@ elif model == 'w-tau-fixed':
truths
=
npamps
# In[
216
]:
# In[
90
]:
fg
,
ax
=
dyplot
.
cornerplot
(
res
,
color
=
'
blue
'
,
...
...
@@ -633,8 +699,26 @@ fg, ax = dyplot.cornerplot(res, color='blue',
)
# In[177]:
# In[200]:
fg
.
savefig
(
output_folder_1
+
'
/Dynesty_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax=
'
+
str
(
nmax
)
+
'
_tshift=
'
+
str
(
tshift
)
+
'
_
'
+
str
(
npoints
)
+
'
_chainplot.png
'
,
format
=
'
png
'
,
bbox_inches
=
'
tight
'
)
# In[ ]:
fg
.
savefig
(
output_folder
+
'
/Dynesty_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax=
'
+
str
(
nmax
)
+
'
_tshift=
'
+
str
(
tshift
)
+
'
_
'
+
str
(
npoints
)
+
'
_chainplot.png
'
,
format
=
'
png
'
,
bbox_inches
=
'
tight
'
)
figband
=
plt
.
figure
(
figsize
=
(
12
,
9
))
plt
.
plot
(
timesrd_final_tsh
,
gwdatanew_re_tsh
,
"
green
"
,
alpha
=
0.9
,
lw
=
3
,
label
=
r
'
$res_{240}$
'
)
plt
.
plot
(
timesrd_final_tsh
,
dict
[
model
](
vars_ml
).
real
,
'
bo
'
,
alpha
=
0.9
,
lw
=
3
,
label
=
r
'
$fit$
'
)
samples_res
=
samps
[
-
20000
:
-
1
][
0
::
100
]
for
i
in
samples_res
:
plt
.
plot
(
tshift
,
dict
[
model
](
i
).
real
,
"
r-
"
,
alpha
=
0.01
,
lw
=
3
)
plt
.
title
(
r
'
Comparison of the MC fit data and the $1-\sigma$ error band
'
)
plt
.
legend
()
plt
.
xlabel
(
'
t
'
)
plt
.
ylabel
(
'
h
'
)
plt
.
show
()
This diff is collapsed.
Click to expand it.
code_new/run.sh
+
7
−
5
View file @
e4815c0e
#!/usr/bin/env bash
times
=(
0 2.5 5 7.5 10 12 15 18 20
)
times
=(
0 0.2 0.4 0.8 1.2 2 2.5 5 7.5 10 12 15 18 20
)
configfile
=
config.ini
pythonfile
=
/work/francisco.jimenez/sio/git/rdstackingproject/code_new/NR_dynesty_t0_loop.py
pythonscript
=
/work/francisco.jimenez/sio/pyenv/shims/python
awk
-v
a
=
"10"
'/^tshift/ && $3 != "supplied" { $3=a } { print }'
config.ini
>
tmp
&&
mv
tmp config.ini
awk
-v
a
=
"10"
'/^tshift/ && $3 != "supplied" { $3=a } { print }'
$configfile
>
tmp
&&
mv
tmp
$configfile
for
i
in
${
times
[@]
}
;
do
echo
"submit a simulation with this parameter:"
echo
"
$i
"
awk
-v
a
=
"
$i
"
'/^tshift/ && $3 != "supplied" { $3=a } { print }'
config
.ini
>
tmp
&&
mv
tmp config
.ini
/work/francisco.jimenez/sio/pyenv/shims/python /work/francisco.jimenez/sio/git/rdstackingproject/code_new/NR_dynesty_t0_loop.py
awk
-v
a
=
"
$i
"
'/^tshift/ && $3 != "supplied" { $3=a } { print }'
$
config
file
>
tmp
&&
mv
tmp
$
config
file
$pythonscript
$pythonfile
done
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