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Yifan Wang
RDStackingProject
Commits
ae6a0d9d
Commit
ae6a0d9d
authored
Nov 20, 2020
by
frcojimenez
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code_new/NR_dynesty_t0_loop.ipynb
+153
-89
153 additions, 89 deletions
code_new/NR_dynesty_t0_loop.ipynb
code_new/NR_dynesty_t0_loop.py
+17
-174
17 additions, 174 deletions
code_new/NR_dynesty_t0_loop.py
with
170 additions
and
263 deletions
code_new/NR_dynesty_t0_loop.ipynb
+
153
−
89
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ae6a0d9d
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code_new/NR_dynesty_t0_loop.py
+
17
−
174
View file @
ae6a0d9d
#!/usr/bin/env python
# coding: utf-8
# In[2]:
#Import relevant modules, import data and all that
#Import relevant modules, import data and all that
import
numpy
as
np
import
numpy
as
np
from
scipy
import
interpolate
from
scipy
import
interpolate
...
@@ -48,10 +42,6 @@ except SystemExit:
...
@@ -48,10 +42,6 @@ except SystemExit:
parser
.
sections
()
parser
.
sections
()
pass
pass
# In[3]:
# path
# path
rootpath
=
parser
.
get
(
'
nr-paths
'
,
'
rootpath
'
)
rootpath
=
parser
.
get
(
'
nr-paths
'
,
'
rootpath
'
)
...
@@ -65,18 +55,10 @@ output_folder = parser.get('output-folder','output-folder')
...
@@ -65,18 +55,10 @@ output_folder = parser.get('output-folder','output-folder')
overwrite
=
parser
.
get
(
'
setup
'
,
'
overwrite
'
)
overwrite
=
parser
.
get
(
'
setup
'
,
'
overwrite
'
)
downfactor
=
np
.
int
(
parser
.
get
(
'
setup
'
,
'
plot_down_factor
'
))
downfactor
=
np
.
int
(
parser
.
get
(
'
setup
'
,
'
plot_down_factor
'
))
# In[4]:
if
not
os
.
path
.
exists
(
output_folder
):
if
not
os
.
path
.
exists
(
output_folder
):
os
.
mkdir
(
output_folder
)
os
.
mkdir
(
output_folder
)
print
(
"
Directory
"
,
output_folder
,
"
Created
"
)
print
(
"
Directory
"
,
output_folder
,
"
Created
"
)
# In[5]:
# time config
# time config
tshift
=
parser
.
get
(
'
time-setup
'
,
'
tshift
'
)
tshift
=
parser
.
get
(
'
time-setup
'
,
'
tshift
'
)
...
@@ -88,10 +70,6 @@ tend = np.float(tend)
...
@@ -88,10 +70,6 @@ tend = np.float(tend)
t_align
=
parser
.
get
(
'
time-setup
'
,
'
t_align
'
)
t_align
=
parser
.
get
(
'
time-setup
'
,
'
t_align
'
)
t_align
=
np
.
float
(
t_align
)
t_align
=
np
.
float
(
t_align
)
# In[6]:
# n-tones & nlive
# n-tones & nlive
nmax
=
parser
.
get
(
'
n-tones
'
,
'
nmax
'
)
nmax
=
parser
.
get
(
'
n-tones
'
,
'
nmax
'
)
...
@@ -100,10 +78,6 @@ nmax = np.int(nmax)
...
@@ -100,10 +78,6 @@ nmax = np.int(nmax)
npoints
=
parser
.
get
(
'
n-live-points
'
,
'
npoints
'
)
npoints
=
parser
.
get
(
'
n-live-points
'
,
'
npoints
'
)
npoints
=
np
.
int
(
npoints
)
npoints
=
np
.
int
(
npoints
)
# In[7]:
# model
# model
model
=
parser
.
get
(
'
rd-model
'
,
'
model
'
)
model
=
parser
.
get
(
'
rd-model
'
,
'
model
'
)
error_str
=
eval
(
parser
.
get
(
'
rd-model
'
,
'
error_str
'
))
error_str
=
eval
(
parser
.
get
(
'
rd-model
'
,
'
error_str
'
))
...
@@ -119,18 +93,17 @@ print('nmax:',nmax)
...
@@ -119,18 +93,17 @@ print('nmax:',nmax)
print
(
'
tshift:
'
,
tshift
)
print
(
'
tshift:
'
,
tshift
)
print
(
'
error:
'
,
error_str
)
print
(
'
error:
'
,
error_str
)
# In[8]:
output_folder_1
=
output_folder
+
'
/
'
+
model
+
'
-nmax
'
+
str
(
nmax
)
output_folder_1
=
output_folder
+
'
/
'
+
model
+
'
-nmax
'
+
str
(
nmax
)
if
not
os
.
path
.
exists
(
output_folder_1
):
if
not
os
.
path
.
exists
(
output_folder_1
):
os
.
mkdir
(
output_folder_1
)
os
.
mkdir
(
output_folder_1
)
print
(
"
Directory
"
,
output_folder_1
,
"
Created
"
)
print
(
"
Directory
"
,
output_folder_1
,
"
Created
"
)
corner_plot
=
output_folder_1
+
'
/Dynesty_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax=
'
+
str
(
nmax
)
+
'
_tshift=
'
+
str
(
tshift
)
+
'
_
'
+
str
(
npoints
)
+
'
corner_plot.png
'
diagnosis_plot
=
output_folder_1
+
'
/Dynesty_diagnosis
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax=
'
+
str
(
nmax
)
+
'
_tshift=
'
+
str
(
tshift
)
+
'
_
'
+
str
(
npoints
)
+
'
.png
'
fit_plot
=
output_folder_1
+
'
/Fit_results_
'
+
str
(
simulation_number
)
+
'
tshift_
'
+
str
(
tshift
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.png
'
# In[9]:
sumary_data
=
output_folder_1
+
'
/summary
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
best_data
=
output_folder_1
+
'
/best_values_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
# loading priors
# loading priors
w_mins
=
np
.
empty
(
nmax
+
1
)
w_mins
=
np
.
empty
(
nmax
+
1
)
...
@@ -177,10 +150,6 @@ if model == 'w-tau-fixed':
...
@@ -177,10 +150,6 @@ if model == 'w-tau-fixed':
priors_max
=
np
.
concatenate
((
a_maxs
,
ph_maxs
))
priors_max
=
np
.
concatenate
((
a_maxs
,
ph_maxs
))
prior_dim
=
len
(
priors_min
)
prior_dim
=
len
(
priors_min
)
# In[10]:
vary_fund
=
True
vary_fund
=
True
#sampler parameters
#sampler parameters
...
@@ -225,10 +194,6 @@ def tauRD_to_t_Phys(tau,M):
...
@@ -225,10 +194,6 @@ def tauRD_to_t_Phys(tau,M):
c
=
2.99792458
*
10
**
8
;
G
=
6.67259
*
10
**
(
-
11
);
MS
=
1.9885
*
10
**
30
;
c
=
2.99792458
*
10
**
8
;
G
=
6.67259
*
10
**
(
-
11
);
MS
=
1.9885
*
10
**
30
;
return
((
M
*
MS
*
G
)
/
c
**
3
)
*
tau
return
((
M
*
MS
*
G
)
/
c
**
3
)
*
tau
# In[11]:
#This loads the 22 mode data
#This loads the 22 mode data
gw
=
{}
gw
=
{}
gw
[
simulation_number
]
=
h5py
.
File
(
simulation_path_1
,
'
r
'
)
gw
[
simulation_number
]
=
h5py
.
File
(
simulation_path_1
,
'
r
'
)
...
@@ -257,10 +222,6 @@ times5 = gw5_sxs_bbh_0305[:,0]
...
@@ -257,10 +222,6 @@ times5 = gw5_sxs_bbh_0305[:,0]
tmax5
=
FindTmaximum
(
gw5_sxs_bbh_0305
)
tmax5
=
FindTmaximum
(
gw5_sxs_bbh_0305
)
times5
=
times5
-
tmax5
times5
=
times5
-
tmax5
# In[12]:
#Select the data from 0 onwards
#Select the data from 0 onwards
position
=
np
.
argmax
(
times
>=
(
t_align
))
position
=
np
.
argmax
(
times
>=
(
t_align
))
position5
=
np
.
argmax
(
times5
>=
(
t_align
))
position5
=
np
.
argmax
(
times5
>=
(
t_align
))
...
@@ -269,10 +230,6 @@ gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:-1]
...
@@ -269,10 +230,6 @@ gw_sxs_bbh_0305rd5=gw5_sxs_bbh_0305[position5+1:-1]
timesrd
=
gw_sxs_bbh_0305
[
position
:
-
1
][:,
0
][:
-
1
]
-
tmax
timesrd
=
gw_sxs_bbh_0305
[
position
:
-
1
][:,
0
][:
-
1
]
-
tmax
timesrd5
=
gw5_sxs_bbh_0305
[
position5
:
-
1
][:,
0
][:
-
1
]
-
tmax5
timesrd5
=
gw5_sxs_bbh_0305
[
position5
:
-
1
][:,
0
][:
-
1
]
-
tmax5
# In[13]:
#Test plot real part (data was picked in the last cell). Aligning in time
#Test plot real part (data was picked in the last cell). Aligning in time
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
plot
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
1
],
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev6$: real
'
)
plt
.
plot
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
1
],
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev6$: real
'
)
...
@@ -281,10 +238,6 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,1], "b", alpha=0.3, lw=3, label=r'$Lev5:
...
@@ -281,10 +238,6 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,1], "b", alpha=0.3, lw=3, label=r'$Lev5:
plt
.
plot
(
timesrd5
,
np
.
sqrt
(
gw_sxs_bbh_0305rd5
[:,
1
]
**
2
+
gw_sxs_bbh_0305rd5
[:,
2
]
**
2
),
"
b
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev5\,amp$
'
)
plt
.
plot
(
timesrd5
,
np
.
sqrt
(
gw_sxs_bbh_0305rd5
[:,
1
]
**
2
+
gw_sxs_bbh_0305rd5
[:,
2
]
**
2
),
"
b
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev5\,amp$
'
)
plt
.
legend
()
plt
.
legend
()
# In[14]:
#Test plot im part (data was picked in the last cell). Aligning in time
#Test plot im part (data was picked in the last cell). Aligning in time
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
plot
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
2
],
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev6: imag$
'
)
plt
.
plot
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
2
],
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev6: imag$
'
)
...
@@ -293,29 +246,17 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,2], "b", alpha=0.3, lw=3, label=r'$Lev5:
...
@@ -293,29 +246,17 @@ plt.plot(timesrd5, gw_sxs_bbh_0305rd5[:,2], "b", alpha=0.3, lw=3, label=r'$Lev5:
plt
.
plot
(
timesrd5
,
np
.
sqrt
(
gw_sxs_bbh_0305rd5
[:,
1
]
**
2
+
gw_sxs_bbh_0305rd5
[:,
2
]
**
2
),
"
b
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev5\,amp$
'
)
plt
.
plot
(
timesrd5
,
np
.
sqrt
(
gw_sxs_bbh_0305rd5
[:,
1
]
**
2
+
gw_sxs_bbh_0305rd5
[:,
2
]
**
2
),
"
b
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$Lev5\,amp$
'
)
plt
.
legend
()
plt
.
legend
()
# In[15]:
# Depending on nmax, you load nmax number of freqs. and damping times from the qnm package
# Depending on nmax, you load nmax number of freqs. and damping times from the qnm package
omegas
=
[
qnm
.
modes_cache
(
s
=-
2
,
l
=
2
,
m
=
2
,
n
=
i
)(
a
=
af
)[
0
]
for
i
in
range
(
0
,
dim
)]
omegas
=
[
qnm
.
modes_cache
(
s
=-
2
,
l
=
2
,
m
=
2
,
n
=
i
)(
a
=
af
)[
0
]
for
i
in
range
(
0
,
dim
)]
w
=
(
np
.
real
(
omegas
))
/
mf
w
=
(
np
.
real
(
omegas
))
/
mf
tau
=-
1
/
(
np
.
imag
(
omegas
))
*
mf
tau
=-
1
/
(
np
.
imag
(
omegas
))
*
mf
# In[16]:
gwnew_re
=
interpolate
.
interp1d
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
1
],
kind
=
'
cubic
'
)
gwnew_re
=
interpolate
.
interp1d
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
1
],
kind
=
'
cubic
'
)
gwnew_im
=
interpolate
.
interp1d
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
2
],
kind
=
'
cubic
'
)
gwnew_im
=
interpolate
.
interp1d
(
timesrd
,
gw_sxs_bbh_0305rd
[:,
2
],
kind
=
'
cubic
'
)
gwnew_re5
=
interpolate
.
interp1d
(
timesrd5
,
gw_sxs_bbh_0305rd5
[:,
1
],
kind
=
'
cubic
'
)
gwnew_re5
=
interpolate
.
interp1d
(
timesrd5
,
gw_sxs_bbh_0305rd5
[:,
1
],
kind
=
'
cubic
'
)
gwnew_im5
=
interpolate
.
interp1d
(
timesrd5
,
gw_sxs_bbh_0305rd5
[:,
2
],
kind
=
'
cubic
'
)
gwnew_im5
=
interpolate
.
interp1d
(
timesrd5
,
gw_sxs_bbh_0305rd5
[:,
2
],
kind
=
'
cubic
'
)
# In[17]:
if
timesrd5
[
-
1
]
>=
timesrd
[
-
1
]:
if
timesrd5
[
-
1
]
>=
timesrd
[
-
1
]:
timesrd_final
=
timesrd
timesrd_final
=
timesrd
else
:
else
:
...
@@ -329,18 +270,10 @@ gwdatanew_im5 = gwnew_im5(timesrd_final)
...
@@ -329,18 +270,10 @@ gwdatanew_im5 = gwnew_im5(timesrd_final)
gwdatanew
=
gwdatanew_re
-
1j
*
gwdatanew_im
gwdatanew
=
gwdatanew_re
-
1j
*
gwdatanew_im
gwdatanew5
=
gwdatanew_re5
-
1j
*
gwdatanew_im5
gwdatanew5
=
gwdatanew_re5
-
1j
*
gwdatanew_im5
# In[18]:
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
0
+
90
)
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
0
+
90
)
error
=
np
.
sqrt
(
2
*
mismatch
)
error
=
np
.
sqrt
(
2
*
mismatch
)
print
(
mismatch
)
print
(
mismatch
)
# In[19]:
# Phase alignement
# Phase alignement
phas
=
np
.
angle
(
gwdatanew
)
phas
=
np
.
angle
(
gwdatanew
)
phas
=
np
.
unwrap
(
phas
)
phas
=
np
.
unwrap
(
phas
)
...
@@ -349,10 +282,6 @@ phas5 = np.unwrap(phas5)
...
@@ -349,10 +282,6 @@ phas5 = np.unwrap(phas5)
plt
.
plot
(
timesrd_final
,
phas
,
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
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
'
$phase$
'
)
plt
.
plot
(
timesrd_final
,
phas5
,
"
blue
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
# In[20]:
position
=
np
.
argmax
(
timesrd_final
>=
(
t_align
))
position
=
np
.
argmax
(
timesrd_final
>=
(
t_align
))
dphase
=
phas5
[
position
]
-
phas
[
position
]
dphase
=
phas5
[
position
]
-
phas
[
position
]
print
(
dphase
)
print
(
dphase
)
...
@@ -368,10 +297,6 @@ phas5 = np.unwrap(phas5)
...
@@ -368,10 +297,6 @@ phas5 = np.unwrap(phas5)
plt
.
plot
(
timesrd_final
,
phas
,
"
r
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
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
'
$phase$
'
)
plt
.
plot
(
timesrd_final
,
phas5
,
"
blue
"
,
alpha
=
0.3
,
lw
=
3
,
label
=
r
'
$phase$
'
)
# In[21]:
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
+
90
)
mismatch
=
1
-
EasyMatchT
(
timesrd_final
,
gwdatanew
,
gwdatanew5
,
0
,
+
90
)
print
(
mismatch
)
print
(
mismatch
)
if
error_str
:
if
error_str
:
...
@@ -379,10 +304,6 @@ if error_str:
...
@@ -379,10 +304,6 @@ if error_str:
else
:
else
:
error
=
1
error
=
1
# In[22]:
#Test the new interpolated data
#Test the new interpolated data
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
plot
(
timesrd_final
,
gwdatanew
.
real
,
"
r
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
Lev6
'
)
plt
.
plot
(
timesrd_final
,
gwdatanew
.
real
,
"
r
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
Lev6
'
)
...
@@ -390,10 +311,6 @@ plt.plot(timesrd_final, gwdatanew5.real, "b", alpha=0.3, lw=2, label='Lev5')
...
@@ -390,10 +311,6 @@ plt.plot(timesrd_final, gwdatanew5.real, "b", alpha=0.3, lw=2, label='Lev5')
plt
.
plot
(
timesrd_final
,
error
.
real
,
"
b
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
error
'
)
plt
.
plot
(
timesrd_final
,
error
.
real
,
"
b
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
error
'
)
plt
.
legend
()
plt
.
legend
()
# In[23]:
#Test the error data
#Test the error data
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
figure
(
figsize
=
(
12
,
8
))
plt
.
plot
(
timesrd_final
,
error
.
real
,
"
b
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
error real
'
)
plt
.
plot
(
timesrd_final
,
error
.
real
,
"
b
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
error real
'
)
...
@@ -401,10 +318,6 @@ plt.plot(timesrd_final, error.imag, "r", alpha=0.3, lw=2, label='error imag')
...
@@ -401,10 +318,6 @@ plt.plot(timesrd_final, error.imag, "r", alpha=0.3, lw=2, label='error imag')
plt
.
plot
(
timesrd_final
,
np
.
sqrt
(
error
.
imag
**
2
+
error
.
real
**
2
),
"
r
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
all error
'
)
plt
.
plot
(
timesrd_final
,
np
.
sqrt
(
error
.
imag
**
2
+
error
.
real
**
2
),
"
r
"
,
alpha
=
0.3
,
lw
=
2
,
label
=
'
all error
'
)
plt
.
legend
()
plt
.
legend
()
# In[24]:
#Take the piece of waveform you want
#Take the piece of waveform you want
position_in
=
np
.
argmax
(
timesrd_final
>=
tshift
)
position_in
=
np
.
argmax
(
timesrd_final
>=
tshift
)
position_end
=
np
.
argmax
(
timesrd_final
>=
tend
)
position_end
=
np
.
argmax
(
timesrd_final
>=
tend
)
...
@@ -413,10 +326,6 @@ gwdatanew_re_tsh = gwdatanew_re[position_in:position_end]
...
@@ -413,10 +326,6 @@ gwdatanew_re_tsh = gwdatanew_re[position_in:position_end]
gwdatanew_im_tsh
=
gwdatanew_im
[
position_in
:
position_end
]
gwdatanew_im_tsh
=
gwdatanew_im
[
position_in
:
position_end
]
error_tsh
=
error
[
position_in
:
position_end
]
error_tsh
=
error
[
position_in
:
position_end
]
# In[25]:
#Fitting
#Fitting
#RD model for nmax tones. Amplitudes are in (xn*Exp[i yn]) version. Used here.
#RD model for nmax tones. Amplitudes are in (xn*Exp[i yn]) version. Used here.
def
model_dv_q
(
theta
):
def
model_dv_q
(
theta
):
...
@@ -488,16 +397,8 @@ def log_probability(theta):
...
@@ -488,16 +397,8 @@ def log_probability(theta):
return
-
np
.
inf
return
-
np
.
inf
return
lp
+
log_likelihood
(
theta
)
return
lp
+
log_likelihood
(
theta
)
# In[26]:
dict
=
{
'
w-tau
'
:
model_dv_tau
,
'
w-q
'
:
model_dv_q
,
'
w-tau-fixed
'
:
model_dv
}
dict
=
{
'
w-tau
'
:
model_dv_tau
,
'
w-q
'
:
model_dv_q
,
'
w-tau-fixed
'
:
model_dv
}
# In[27]:
#I need to provid an initial guess for 4*(nmax+1) the parameters
#I need to provid an initial guess for 4*(nmax+1) the parameters
np
.
random
.
seed
(
42
)
np
.
random
.
seed
(
42
)
nll
=
lambda
*
args
:
-
log_likelihood
(
*
args
)
nll
=
lambda
*
args
:
-
log_likelihood
(
*
args
)
...
@@ -508,17 +409,9 @@ print("Maximum likelihood estimates:")
...
@@ -508,17 +409,9 @@ print("Maximum likelihood estimates:")
vars_ml
=
soln
.
x
vars_ml
=
soln
.
x
print
(
vars_ml
)
print
(
vars_ml
)
# In[28]:
f2
=
dynesty
.
NestedSampler
(
log_likelihood
,
prior_transform
,
prior_dim
,
nlive
=
npoints
,
sample
=
'
rwalk
'
)
f2
=
dynesty
.
NestedSampler
(
log_likelihood
,
prior_transform
,
prior_dim
,
nlive
=
npoints
,
sample
=
'
rwalk
'
)
f2
.
run_nested
()
f2
.
run_nested
()
# In[29]:
wstr
=
r
'
$\omega_
'
wstr
=
r
'
$\omega_
'
if
model
==
'
w-tau
'
:
if
model
==
'
w-tau
'
:
...
@@ -547,10 +440,6 @@ labels = np.concatenate((w_lab,tau_lab,amp_lab,pha_lab))
...
@@ -547,10 +440,6 @@ labels = np.concatenate((w_lab,tau_lab,amp_lab,pha_lab))
if
model
==
'
w-tau-fixed
'
:
if
model
==
'
w-tau-fixed
'
:
labels
=
np
.
concatenate
((
amp_lab
,
pha_lab
))
labels
=
np
.
concatenate
((
amp_lab
,
pha_lab
))
# In[30]:
if
model
==
'
w-tau-fixed
'
:
if
model
==
'
w-tau-fixed
'
:
rg
=
(
nmax
+
1
)
rg
=
(
nmax
+
1
)
else
:
else
:
...
@@ -570,56 +459,31 @@ else:
...
@@ -570,56 +459,31 @@ else:
amps_aux
=
samps_tr
[
i
][
half_points
:
-
1
]
amps_aux
=
samps_tr
[
i
][
half_points
:
-
1
]
npamps
[
i
]
=
np
.
quantile
(
amps_aux
,
0.5
)
npamps
[
i
]
=
np
.
quantile
(
amps_aux
,
0.5
)
# In[31]:
res
=
f2
.
results
res
=
f2
.
results
res
.
samples_u
.
shape
res
.
samples_u
.
shape
res
.
summary
()
res
.
summary
()
samps
=
f2
.
results
.
samples
samps
=
f2
.
results
.
samples
# In[32]:
evidence
=
res
.
logz
[
-
1
]
evidence
=
res
.
logz
[
-
1
]
evidence_error
=
res
.
logzerr
[
-
1
]
evidence_error
=
res
.
logzerr
[
-
1
]
# In[33]:
summary_titles
=
[
'
n
'
,
'
id
'
,
'
t_shift
'
,
'
dlogz
'
,
'
dlogz_err
'
]
summary_titles
=
[
'
n
'
,
'
id
'
,
'
t_shift
'
,
'
dlogz
'
,
'
dlogz_err
'
]
f
=
output_folder_1
+
'
/summary
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
# In[35]:
if
os
.
path
.
exists
(
f
):
if
os
.
path
.
exists
(
sumary_data
):
outvalues
=
np
.
array
([[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
outvalues
=
np
.
array
([[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
else
:
else
:
outvalues
=
np
.
array
([
summary_titles
,[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
outvalues
=
np
.
array
([
summary_titles
,[
nmax
,
simulation_number
,
tshift
,
evidence
,
evidence_error
]])
with
open
(
f
,
'
a
'
)
as
file
:
with
open
(
sumary_data
,
'
a
'
)
as
file
:
writer
=
csv
.
writer
(
file
)
writer
=
csv
.
writer
(
file
)
if
(
outvalues
.
shape
)[
0
]
>
1
:
if
(
outvalues
.
shape
)[
0
]
>
1
:
writer
.
writerows
(
outvalues
)
writer
.
writerows
(
outvalues
)
else
:
else
:
writer
.
writerow
(
outvalues
[
0
])
writer
.
writerow
(
outvalues
[
0
])
# In[36]:
samps
=
f2
.
results
.
samples
samps
=
f2
.
results
.
samples
samps_tr
=
np
.
transpose
(
samps
)
samps_tr
=
np
.
transpose
(
samps
)
# In[37]:
sigma_vars_m
=
np
.
empty
(
prior_dim
)
sigma_vars_m
=
np
.
empty
(
prior_dim
)
sigma_vars_p
=
np
.
empty
(
prior_dim
)
sigma_vars_p
=
np
.
empty
(
prior_dim
)
sigma_vars
=
np
.
empty
(
prior_dim
)
sigma_vars
=
np
.
empty
(
prior_dim
)
...
@@ -629,29 +493,16 @@ for i in range(prior_dim):
...
@@ -629,29 +493,16 @@ for i in range(prior_dim):
sigma_vars
[
i
]
=
np
.
quantile
(
amps_aux
,
0.5
)
sigma_vars
[
i
]
=
np
.
quantile
(
amps_aux
,
0.5
)
sigma_vars_p
[
i
]
=
np
.
quantile
(
amps_aux
,
0.9
)
sigma_vars_p
[
i
]
=
np
.
quantile
(
amps_aux
,
0.9
)
# In[38]:
sigma_vars_all
=
[
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
]
sigma_vars_all
=
[
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
]
sigma_vars_all
=
np
.
stack
([
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
],
axis
=
0
)
sigma_vars_all
=
np
.
stack
([
sigma_vars
,
sigma_vars_m
,
sigma_vars_p
],
axis
=
0
)
# In[69]:
key
=
[
'
max val
'
,
'
lower bound
'
,
'
higher bound
'
]
key
=
[
'
max val
'
,
'
lower bound
'
,
'
higher bound
'
]
file
=
output_folder_1
+
'
/best_values_
'
+
str
(
simulation_number
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.csv
'
dfslist
=
[
pd
.
DataFrame
(
np
.
concatenate
(([
tshift
],
sigma_vars_all
[
1
])).
reshape
((
-
1
,
prior_dim
+
1
)),
columns
=
np
.
concatenate
(([
'
tshift
'
],
labels
)),
index
=
[
key
[
i
]])
for
i
in
range
(
3
)]
dfslist
=
[
pd
.
DataFrame
(
np
.
concatenate
(([
tshift
],
sigma_vars_all
[
1
])).
reshape
((
-
1
,
prior_dim
+
1
)),
columns
=
np
.
concatenate
(([
'
tshift
'
],
labels
)),
index
=
[
key
[
i
]])
for
i
in
range
(
3
)]
df2
=
pd
.
concat
(
dfslist
)
df2
=
pd
.
concat
(
dfslist
)
if
os
.
path
.
exists
(
file
):
if
os
.
path
.
exists
(
best_data
):
df2
.
to_csv
(
file
,
mode
=
'
a
'
,
header
=
False
,
index
=
True
)
df2
.
to_csv
(
best_data
,
mode
=
'
a
'
,
header
=
False
,
index
=
True
)
else
:
else
:
df2
.
to_csv
(
file
,
index
=
True
)
df2
.
to_csv
(
best_data
,
index
=
True
)
# In[42]:
if
model
==
'
w-q
'
:
if
model
==
'
w-q
'
:
tau_val
=
np
.
pi
*
w
*
tau
tau_val
=
np
.
pi
*
w
*
tau
...
@@ -662,10 +513,6 @@ elif model == 'w-tau':
...
@@ -662,10 +513,6 @@ elif model == 'w-tau':
elif
model
==
'
w-tau-fixed
'
:
elif
model
==
'
w-tau-fixed
'
:
truths
=
npamps
truths
=
npamps
# In[43]:
fg
,
ax
=
dyplot
.
cornerplot
(
res
,
color
=
'
blue
'
,
fg
,
ax
=
dyplot
.
cornerplot
(
res
,
color
=
'
blue
'
,
show_titles
=
True
,
show_titles
=
True
,
labels
=
labels
,
labels
=
labels
,
...
@@ -674,15 +521,15 @@ fg, ax = dyplot.cornerplot(res, color='blue',
...
@@ -674,15 +521,15 @@ fg, ax = dyplot.cornerplot(res, color='blue',
truth_color
=
'
red
'
,
truth_color
=
'
red
'
,
)
)
fg
.
savefig
(
corner_plot
,
format
=
'
png
'
,
bbox_inches
=
'
tight
'
)
# In[127]:
from
dynesty
import
plotting
as
dyplot
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[148]:
lnz_truth
=
ndim
*
-
np
.
log
(
2
*
10.
)
# analytic evidence solution
fig
,
axes
=
dyplot
.
runplot
(
res
,
lnz_truth
=
lnz_truth
)
fig
.
tight_layout
()
fig
.
savefig
(
diagnosis_plot
,
format
=
'
png
'
,
dpi
=
384
,
bbox_inches
=
'
tight
'
)
figband
=
plt
.
figure
(
figsize
=
(
12
,
9
))
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
,
gwdatanew_re_tsh
,
"
green
"
,
alpha
=
0.9
,
lw
=
3
,
label
=
r
'
$res_{240}$
'
)
...
@@ -698,10 +545,6 @@ plt.xlabel('t')
...
@@ -698,10 +545,6 @@ plt.xlabel('t')
plt
.
ylabel
(
'
h
'
)
plt
.
ylabel
(
'
h
'
)
plt
.
show
()
plt
.
show
()
# In[149]:
fit_plot
=
output_folder_1
+
'
/Fit_results_
'
+
str
(
simulation_number
)
+
'
tshift_
'
+
str
(
tshift
)
+
'
_
'
+
model
+
'
_nmax_
'
+
str
(
nmax
)
+
'
.png
'
figband
.
savefig
(
fit_plot
)
figband
.
savefig
(
fit_plot
)
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