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Alexander Kutkin
apipeline
Commits
3a98e336
Commit
3a98e336
authored
3 years ago
by
Alexander Kutkin
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add clustering script
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Clustering based on presence of artifacts around sources
Created on Tue Dec 10 20:48:31 2019
@author: kutkin
"""
import
os
import
sys
import
astropy.io.fits
as
fits
from
astropy.wcs
import
WCS
from
astropy.coordinates
import
SkyCoord
,
angles
from
astropy.stats
import
median_absolute_deviation
as
mad
import
astropy.units
as
u
import
numpy
as
np
import
matplotlib.pyplot
as
plt
import
pandas
as
pd
from
matplotlib.patches
import
Circle
,
Rectangle
,
Ellipse
# import copy
import
logging
logging
.
basicConfig
(
level
=
logging
.
DEBUG
)
def
ra2deg
(
ra
):
s
=
np
.
array
(
ra
.
split
(
'
:
'
),
dtype
=
float
)
if
s
[
0
]
<
0.0
:
sign
=
-
1.0
else
:
sign
=
1.0
return
sign
*
(
abs
(
s
[
0
])
+
s
[
1
]
/
60.0
+
s
[
2
]
/
3600.
)
*
15
def
dec2deg
(
dec
):
s
=
np
.
array
(
dec
.
split
(
'
.
'
),
dtype
=
float
)
sign
=
np
.
sign
(
s
[
0
])
if
len
(
s
)
==
4
:
return
sign
*
(
abs
(
s
[
0
])
+
s
[
1
]
/
60.0
+
s
[
2
]
/
3600.
+
s
[
3
]
*
10
**
(
-
len
(
str
(
s
[
3
])))
/
3600.
)
elif
len
(
s
)
==
3
:
return
sign
*
(
abs
(
s
[
0
])
+
s
[
1
]
/
60.0
+
s
[
2
]
/
3600.
)
def
sep_radec
(
ra
,
dec
,
ra0
,
dec0
):
c0
=
SkyCoord
(
ra0
,
dec0
,
unit
=
u
.
deg
)
c
=
SkyCoord
(
ra
,
dec
,
unit
=
u
.
deg
)
return
c0
.
separation
(
c
).
arcsec
def
radec
(
ra
,
dec
):
"""
return SkyCoord object from ra, dec
"""
ra
=
ra2deg
(
ra
)
dec
=
dec2deg
(
dec
)
# print ra, dec
return
SkyCoord
(
ra
,
dec
,
unit
=
(
'
deg, deg
'
))
class
Cluster
():
"""
A cluster object
"""
def
__init__
(
self
,
name
,
center
,
radius
):
"""
INPUT:
name -- cluster1, ...
center -- SkyCoord object with RA, Dec
radius -- radius (SkyCoord Angle)
"""
self
.
name
,
self
.
center
,
self
.
radius
=
name
,
center
,
radius
# logging.debug(self.radius)
# def separation(self, other):
# """
# Separation between Cluster center and ra, dec in arcmin
# """
# return self.center.separation(other.center)
def
offset
(
self
,
radec
):
"""
offset between cluster center and SkyCoord object (Angle)
"""
return
self
.
center
.
separation
(
radec
)
def
intersects
(
self
,
other
):
"""
Does it intersect with the other
"""
sep
=
self
.
center
.
separation
(
other
.
center
)
rsum
=
self
.
radius
+
other
.
radius
# print res, rsum
if
sep
<=
rsum
:
return
True
else
:
return
False
# TODO merging of the clusters
def
merge
(
self
,
other
,
overwrite
=
True
):
"""
merge the Cluster with the other one
"""
sep
=
self
.
center
.
separation
(
other
.
center
)
rsum
=
self
.
radius
+
other
.
radius
if
overwrite
:
new_name
=
self
.
name
else
:
new_name
=
'
{}_{}
'
.
format
(
self
.
name
,
other
.
name
)
new_center
=
SkyCoord
((
self
.
center
.
ra
*
self
.
radius
/
rsum
+
other
.
center
.
ra
*
other
.
radius
/
rsum
),
(
self
.
center
.
dec
*
self
.
radius
/
rsum
+
other
.
center
.
dec
*
other
.
radius
/
rsum
))
new_radius
=
max
((
sep
,
self
.
radius
,
other
.
radius
))
if
sep
>
rsum
:
logging
.
warning
(
'
Merging the non-intersecting clusters
'
)
return
Cluster
(
new_name
,
new_center
,
new_radius
)
def
overplot
(
self
,
ax
):
c
=
self
.
center
txt
=
self
.
name
.
lstrip
(
'
cluster
'
)
circle
=
plt
.
Circle
((
c
.
ra
.
value
,
c
.
dec
.
value
),
self
.
radius
.
deg
,
facecolor
=
'
none
'
,
edgecolor
=
'
r
'
,
transform
=
ax
.
get_transform
(
'
world
'
))
ax
.
text
(
c
.
ra
.
value
,
c
.
dec
.
value
,
'
{}
'
.
format
(
txt
),
ha
=
'
center
'
,
va
=
'
center
'
,
fontdict
=
{
'
weight
'
:
'
bold
'
,
'
size
'
:
13
},
transform
=
ax
.
get_transform
(
'
world
'
))
ax
.
add_artist
(
circle
)
def
cluster_sources
(
df
,
cluster
):
radius
=
cluster
.
radius
df
[
'
ra
'
]
=
df
.
Ra
.
apply
(
ra2deg
)
df
[
'
dec
'
]
=
df
.
Dec
.
apply
(
dec2deg
)
df
[
'
sep
'
]
=
sep_radec
(
df
.
ra
,
df
.
dec
,
cluster
.
center
.
ra
,
cluster
.
center
.
dec
)
/
60.0
# df['Patch']
return
df
.
query
(
'
sep<@radius
'
)
def
cluster_snr
(
df
,
cluster
,
wcs
,
resid_data
,
pix_arcmin_scale
):
"""
SNR of the model sources within a cluster
"""
radius
=
cluster
.
radius
.
arcmin
a
=
cluster_sources
(
df
,
cluster
)
# signal_max = a.I.max()
signal_sum
=
a
.
I
.
sum
()
px
,
py
=
np
.
round
(
wcs
.
all_world2pix
(
cluster
.
center
.
ra
,
cluster
.
center
.
dec
,
0
)).
astype
(
int
)
# print(px, py)
# sys.exit()
y
,
x
=
np
.
mgrid
[
0
:
resid_data
.
shape
[
0
],
0
:
resid_data
.
shape
[
1
]]
radius_pix
=
radius
/
pix_arcmin_scale
mask
=
np
.
where
((
y
-
py
)
**
2
+
(
x
-
px
)
**
2
<=
radius_pix
**
2
)
noise
=
np
.
std
(
resid_data
[
mask
])
return
signal_sum
,
signal_sum
/
noise
def
write_df
(
df
,
clusters
,
output
=
None
):
with
open
(
output
,
'
w
'
)
as
out
:
out
.
write
(
"
Format = Name,Patch,Type,Ra,Dec,I,Q,U,V,SpectralIndex,LogarithmicSI,ReferenceFrequency=
'
1399603271.48438
'
,MajorAxis,MinorAxis,Orientation
\n
"
)
if
not
clusters
:
logging
.
error
(
'
No clusters
'
)
return
-
1
for
cluster
in
clusters
:
df
[
'
sep
'
]
=
sep_radec
(
df
.
ra
,
df
.
dec
,
cluster
.
center
.
ra
,
cluster
.
center
.
dec
)
clust
=
df
.
query
(
'
sep <= @cluster.radius.arcsec
'
)
clust
.
loc
[
clust
.
index
,
'
Patch
'
]
=
cluster
.
name
df
.
loc
[
clust
.
index
,
'
Patch
'
]
=
cluster
.
name
clusternum
=
cluster
.
name
.
lstrip
(
'
cluster
'
)
with
open
(
output
,
'
a
'
)
as
out
:
out
.
write
(
'
, {}, POINT, , , , , , , , , , , ,
\n
'
.
format
(
cluster
.
name
))
clust
.
to_csv
(
out
,
index
=
False
,
header
=
False
,
columns
=
df
.
columns
[:
-
3
])
clust
=
df
.
query
(
'
Patch ==
"
cluster0
"'
)
clusternum
=
int
(
clusternum
)
+
1
restname
=
'
cluster
'
+
str
(
clusternum
)
clust
.
loc
[
clust
.
index
,
'
Patch
'
]
=
restname
df
.
loc
[
clust
.
index
,
'
Patch
'
]
=
restname
with
open
(
output
,
'
a
'
)
as
out
:
out
.
write
(
'
, {}, POINT, , , , , , , , , , , ,
\n
'
.
format
(
restname
))
clust
.
to_csv
(
out
,
index
=
False
,
header
=
False
,
columns
=
df
.
columns
[:
-
3
])
return
0
def
radial_profile
(
ra
,
dec
,
resid_img
):
window
=
180
step
=
10
# sampling=200
initial_radius
=
15
final_radius
=
window
rads
=
np
.
arange
(
initial_radius
,
final_radius
,
step
)
with
fits
.
open
(
resid_img
)
as
f
:
wcs
=
WCS
(
f
[
0
].
header
).
celestial
pix_arcmin_scale
=
f
[
0
].
header
[
'
CDELT2
'
]
*
60
resid_data
=
f
[
0
].
data
[
0
,
0
,...]
c
=
radec
(
ra
,
dec
)
px
,
py
=
wcs
.
all_world2pix
(
c
.
ra
,
c
.
dec
,
0
)
px
,
py
=
int
(
round
(
py
)),
int
(
round
(
px
))
res
=
np
.
zeros_like
(
rads
,
dtype
=
float
)
for
ind
,
rad
in
enumerate
(
rads
):
sampling
=
int
(
1000
*
rad
/
final_radius
)
for
angle
in
np
.
linspace
(
0
,
2
*
np
.
pi
,
sampling
):
x
=
int
(
rad
*
np
.
cos
(
angle
))
y
=
int
(
rad
*
np
.
sin
(
angle
))
d
=
resid_data
[
y
+
py
:
y
+
py
+
step
,
x
+
px
:
x
+
px
+
step
]
res
[
ind
]
+=
np
.
nanmean
(
abs
(
d
))
/
sampling
return
rads
,
res
def
sector_max
(
ra
,
dec
,
resid_img
,
ax
,
nsectors
=
6
):
r0
=
10
r1
=
200
# pixels
with
fits
.
open
(
resid_img
)
as
f
:
wcs
=
WCS
(
f
[
0
].
header
).
celestial
img_size
=
f
[
0
].
header
[
'
NAXIS1
'
]
pix_arcmin_scale
=
f
[
0
].
header
[
'
CDELT2
'
]
*
60
resid_data
=
f
[
0
].
data
[
0
,
0
,...]
mad_std
=
mad
(
resid_data
)
c
=
radec
(
ra
,
dec
)
px
,
py
=
wcs
.
all_world2pix
(
c
.
ra
,
c
.
dec
,
0
)
px
,
py
=
int
(
round
(
px
)),
int
(
round
(
py
))
y
,
x
=
np
.
mgrid
[
0
:
img_size
,
0
:
img_size
]
x
=
x
-
px
y
=
y
-
py
radcond
=
np
.
logical_and
(
np
.
hypot
(
x
,
y
)
>
r0
,
np
.
hypot
(
x
,
y
)
<
r1
)
sectors
=
np
.
linspace
(
-
np
.
pi
,
np
.
pi
,
nsectors
)
result
=
[]
# fig = plt.figure(figsize=[10,10])
# ax = fig.add_subplot(1,1,1, projection=wcs.celestial)
# vmin, vmax = np.percentile(resid_data, 5), np.percentile(resid_data, 95)
# ax.imshow(resid_data, vmin=vmin, vmax=vmax, origin='lower')#cmap='gray', vmin=2e-5, vmax=0.1)#, norm=LogNorm())
for
i
in
range
(
nsectors
-
1
):
ang1
,
ang2
=
sectors
[
i
],
sectors
[
i
+
1
]
# angcond = np.logical_and(x*np.sin(ang1)/np.cos(ang1) < y, y <= x*np.sin(ang2)/np.cos(ang2))
coef
=
np
.
arctan2
(
y
,
x
)
angcond
=
np
.
logical_and
(
ang1
<
coef
,
coef
<
ang2
)
cond
=
np
.
logical_and
(
radcond
,
angcond
)
res
=
np
.
nanmax
(
resid_data
[
cond
])
/
mad_std
result
.
append
(
res
)
cond2
=
np
.
logical_and
(
cond
,
resid_data
==
max
(
resid_data
[
cond
]))
yy
,
xx
=
np
.
argwhere
(
cond2
)[
0
]
ra
,
dec
=
wcs
.
all_pix2world
(
xx
,
yy
,
0
)
# print yy, xx
if
res
>
10
:
ax
.
plot
(
ra
,
dec
,
'
.r
'
,
transform
=
ax
.
get_transform
(
'
world
'
))
# tmp = np.argwhere(cond)
# for p in tmp:
# ax.plot(p[1], p[0], '.k')
# print ang1, ang2, len(cond[cond])
return
np
.
array
(
result
)
def
ellipses_coh
(
img
,
x0
=
None
,
y0
=
None
,
dr
=
None
,
amin
=
20
,
amax
=
100
):
"""
Find an ellipse ring with the highest absolute mean pixels value.
Return: max of abs of mean of the pixels within various ellipses,
minor_axis/major_axis, major_axis, number of pixels within the ellipse
"""
y
,
x
=
np
.
mgrid
[
0
:
img
.
shape
[
0
],
0
:
img
.
shape
[
1
]]
x0
=
x0
or
int
(
img
.
shape
[
1
]
/
2
)
y0
=
y0
or
int
(
img
.
shape
[
0
]
/
2
)
y
,
x
=
y
-
y0
,
x
-
x0
eccs
=
np
.
linspace
(
0.6
,
1.0
,
10
)
arange
=
range
(
amin
,
amax
)
# drmin = 1.0
# drmax = 40.0
# OldRange = float(amax - amin)
# NewRange = float(drmax - drmin)
res
=
np
.
zeros
((
len
(
eccs
),
len
(
arange
)))
for
i
,
ecc
in
enumerate
(
eccs
):
for
j
,
a
in
enumerate
(
arange
):
# dr = (((a - amin) * NewRange) / OldRange) + drmin
b
=
a
*
ecc
cond
=
np
.
logical_and
(
x
**
2
*
(
a
+
dr
)
**
2
+
y
**
2
*
(
b
+
dr
)
**
2
<
(
a
+
dr
)
**
2
*
(
b
+
dr
)
**
2
,
x
**
2
*
a
**
2
+
y
**
2
*
b
**
2
>=
a
**
2
*
b
**
2
)
if
len
(
cond
[
cond
])
>=
50
:
# res[i, j] = abs(sum(img[cond]))/len(img[cond])
res
[
i
,
j
]
=
abs
(
np
.
nanmean
(
img
[
cond
]))
else
:
logging
.
warning
(
'
{:d} pixels for dr={:.1f} a={:0.1f}, e={:.1f}
'
.
format
(
len
(
cond
[
cond
]),
dr
,
a
,
ecc
))
res
[
i
,
j
]
=
0.0
imax
,
jmax
=
np
.
argwhere
(
res
==
res
.
max
())[
0
]
eccmax
=
eccs
[
imax
]
amax
=
arange
[
jmax
]
bmax
=
amax
*
eccmax
cond
=
np
.
logical_and
(
x
**
2
*
(
amax
+
dr
)
**
2
+
y
**
2
*
(
bmax
+
dr
)
**
2
<
(
amax
+
dr
)
**
2
*
(
bmax
+
dr
)
**
2
,
x
**
2
*
amax
**
2
+
y
**
2
*
bmax
**
2
>=
amax
**
2
*
bmax
**
2
)
# logging.debug('Ellipse size: {:d} pixels'.format(len(img[cond])))
return
res
.
max
(),
eccmax
,
amax
,
len
(
img
[
cond
])
def
manual_clustering
(
fig
,
ax
,
wcs
,
pix_arcmin_scale
,
startnum
=
1
):
def
get_cluster
(
cen
,
rad
,
name
):
xc
,
yc
=
cen
x
,
y
=
rad
radius_pix
=
np
.
hypot
(
x
-
xc
,
y
-
yc
)
radius
=
angles
.
Angle
(
radius_pix
*
pix_arcmin_scale
,
unit
=
'
arcmin
'
)
ra
,
dec
=
wcs
.
all_pix2world
(
xc
,
yc
,
0
)
center
=
SkyCoord
(
ra
,
dec
,
unit
=
'
deg
'
)
logging
.
info
(
"
Cluster {} at {} of {} radius
"
.
format
(
name
,
center
,
radius
))
return
Cluster
(
name
,
center
,
radius
)
do
=
True
i
=
startnum
clusters
=
[]
while
do
:
logging
.
info
(
'
Select center and radius for the cluster.
\
Then press left button to continue or middle to skip.
\
Right button -- to cancel the last selection.
'
)
inp
=
fig
.
ginput
(
3
,
timeout
=-
1
)
cen
,
rad
=
inp
[:
2
]
if
len
(
inp
)
==
2
:
do
=
False
cluster
=
get_cluster
(
cen
,
rad
,
'
cluster{}
'
.
format
(
i
))
cluster
.
overplot
(
ax
)
clusters
.
append
(
cluster
)
i
+=
1
return
clusters
def
auto_clustering
(
fig
,
ax
,
df
,
wcs
,
resid_data
,
pix_arcmin_scale
,
nbright
,
cluster_radius
,
cluster_overlap
,
boxsize
=
250
,
nclusters
=
5
):
a
=
df
.
sort_values
(
'
I
'
)[::
-
1
][:
nbright
][[
'
Ra
'
,
'
Dec
'
,
'
I
'
]]
clusters
=
[]
#
csnrs
=
[]
cfluxes
=
[]
cmeasures
=
[]
# the main value to clusterize by
cellipse_params
=
[]
fmin
,
fmax
=
min
(
a
.
I
),
max
(
a
.
I
)
rms
=
mad
(
resid_data
)
resid_mean
=
np
.
mean
(
resid_data
)
if
nclusters
==
'
auto
'
:
logging
.
info
(
'
Number of clusters will be determined automatically
'
)
else
:
logging
.
info
(
'
Maximum number of clusters is {}
'
.
format
(
nclusters
))
logging
.
info
(
'
Getting measures for the potential clusters...
'
)
src_index
=
1
cluster_index
=
1
for
ra
,
dec
,
flux
in
a
.
values
:
c
=
radec
(
ra
,
dec
)
px
,
py
=
np
.
round
(
wcs
.
all_world2pix
(
c
.
ra
,
c
.
dec
,
0
)).
astype
(
int
)
# print(px, py)
# print(src_index, ra, dec, px, py, flux)
src_index
+=
1
# skip the edge sources
if
(
abs
(
px
-
resid_data
.
shape
[
1
])
<
boxsize
)
or
(
abs
(
py
-
resid_data
.
shape
[
0
])
<
boxsize
):
logging
.
debug
(
'
Skipping the edge source
'
)
continue
# Check if the component already in a cluster
if
clusters
and
any
([
c0
.
offset
(
c
).
arcmin
<=
cluster_radius
.
value
*
cluster_overlap
for
c0
in
clusters
]):
# logging.debug('Already in a cluster')
continue
small_resid
=
resid_data
[
py
-
boxsize
:
py
+
boxsize
,
px
-
boxsize
:
px
+
boxsize
]
ellipse_mean
,
ecc
,
amaj
,
numpix
=
ellipses_coh
(
small_resid
,
amin
=
20
,
amax
=
boxsize
-
1
,
dr
=
1.0
)
# print ellipse_mean, ecc, amaj, numpix
# if abs(ring_mean/resid_mean) > 1.7e5:
# if abs(ring_mean/small_resid_mean) > 1e4:
if
nclusters
==
'
auto
'
:
if
abs
(
ellipse_mean
/
rms
)
>
1.4
:
rect
=
plt
.
Rectangle
((
px
-
boxsize
,
py
-
boxsize
),
2
*
boxsize
,
2
*
boxsize
,
lw
=
2
,
color
=
'
k
'
,
fc
=
'
none
'
)
ellipse
=
Ellipse
(
xy
=
(
px
,
py
),
width
=
2
*
amaj
*
ecc
,
height
=
2
*
amaj
,
angle
=
0
,
lw
=
3
,
color
=
'
gray
'
,
fc
=
'
none
'
,
alpha
=
0.5
)
ax
.
add_artist
(
rect
)
ax
.
add_artist
(
ellipse
)
cluster_name
=
'
cluster{}
'
.
format
(
cluster_index
)
cluster
=
Cluster
(
cluster_name
,
c
,
cluster_radius
)
csnr
=
cluster_snr
(
df
,
cluster
,
wcs
,
resid_data
,
pix_arcmin_scale
)[
1
]
if
csnr
<
100
:
# skip clusters with low SNR
logging
.
debug
(
'
Skipping low SNR cluster at {}
'
.
format
(
cluster
.
center
))
continue
clusters
.
append
(
cluster
)
cluster
.
overplot
(
ax
)
print
(
cluster_name
,
ra
,
dec
,
csnr
,
boxsize
)
cluster_index
+=
1
else
:
cluster_name
=
'
cluster{}
'
.
format
(
src_index
)
cluster
=
Cluster
(
cluster_name
,
c
,
cluster_radius
)
cflux
,
csnr
=
cluster_snr
(
df
,
cluster
,
wcs
,
resid_data
,
pix_arcmin_scale
)
clusters
.
append
(
cluster
)
cfluxes
.
append
(
cflux
)
csnrs
.
append
(
csnr
)
cmeasures
.
append
(
abs
(
ellipse_mean
/
resid_mean
))
cellipse_params
.
append
([
amaj
,
ecc
,
numpix
])
if
nclusters
==
'
auto
'
:
return
clusters
else
:
indexes
=
np
.
argsort
(
cmeasures
)[::
-
1
][:
nclusters
]
final_clusters
=
[]
logging
.
info
(
'
Picking {} clusters
'
.
format
(
nclusters
))
for
i
in
indexes
:
cmeasure
=
cmeasures
[
i
]
cluster
=
clusters
[
i
]
amaj
,
ecc
,
npix
=
cellipse_params
[
i
]
csnr
=
csnrs
[
i
]
cflux
=
cfluxes
[
i
]
if
csnr
<
100
:
# skip clusters with low SNR
logging
.
debug
(
'
Skipping low SNR cluster at {}
'
.
format
(
cluster
.
center
))
continue
cluster
.
name
=
'
cluster{}
'
.
format
(
cluster_index
)
print
(
cluster
.
name
,
ra
,
dec
,
csnr
,
cmeasure
)
px
,
py
=
wcs
.
all_world2pix
(
cluster
.
center
.
ra
,
cluster
.
center
.
dec
,
0
)
px
,
py
=
int
(
px
),
int
(
py
)
rect
=
plt
.
Rectangle
((
px
-
boxsize
,
py
-
boxsize
),
2
*
boxsize
,
2
*
boxsize
,
lw
=
2
,
color
=
'
k
'
,
fc
=
'
none
'
)
ellipse
=
Ellipse
(
xy
=
(
px
,
py
),
width
=
2
*
amaj
*
ecc
,
height
=
2
*
amaj
,
angle
=
0
,
lw
=
3
,
color
=
'
gray
'
,
fc
=
'
none
'
,
alpha
=
0.5
)
ax
.
add_artist
(
rect
)
ax
.
add_artist
(
ellipse
)
final_clusters
.
append
(
cluster
)
cluster
.
overplot
(
ax
)
cluster_index
+=
1
return
final_clusters
def
main
(
img
,
resid
,
model
,
auto
=
True
,
add_manual
=
False
,
nclusters
=
5
,
boxsize
=
250
,
nbright
=
80
,
cluster_radius
=
5
,
cluster_overlap
=
1.6
):
path
=
os
.
path
.
split
(
os
.
path
.
abspath
(
img
))[
0
]
output
=
os
.
path
.
join
(
path
,
'
clustered.txt
'
)
df
=
pd
.
read_csv
(
model
,
skipinitialspace
=
True
)
df
[
'
ra
'
]
=
df
.
Ra
.
apply
(
ra2deg
)
df
[
'
dec
'
]
=
df
.
Dec
.
apply
(
dec2deg
)
df
.
insert
(
1
,
'
Patch
'
,
'
cluster0
'
)
df
.
insert
(
6
,
'
Q
'
,
0
)
df
.
insert
(
7
,
'
U
'
,
0
)
df
.
insert
(
8
,
'
V
'
,
0
)
image_data
=
fits
.
getdata
(
img
)[
0
,
0
,...]
resid_data
=
fits
.
getdata
(
resid
)[
0
,
0
,...]
with
fits
.
open
(
img
)
as
f
:
wcs
=
WCS
(
f
[
0
].
header
).
celestial
pix_arcmin_scale
=
f
[
0
].
header
[
'
CDELT2
'
]
*
60
# racen = f[0].header['CRVAL1']
# deccen = f[0].header['CRVAL2']
cluster_radius
=
angles
.
Angle
(
cluster_radius
,
unit
=
'
arcmin
'
)
fig
=
plt
.
figure
(
figsize
=
[
12
,
12
])
ax
=
fig
.
add_subplot
(
1
,
1
,
1
,
projection
=
wcs
.
celestial
)
vmin
,
vmax
=
np
.
percentile
(
image_data
,
5
),
np
.
percentile
(
image_data
,
95
)
ax
.
imshow
(
resid_data
,
vmin
=
vmin
,
vmax
=
vmax
,
origin
=
'
lower
'
)
#cmap='gray', vmin=2e-5, vmax=0.1)#, norm=LogNorm())
if
auto
:
clusters
=
auto_clustering
(
fig
,
ax
,
df
,
wcs
,
resid_data
,
pix_arcmin_scale
,
nbright
,
cluster_radius
,
cluster_overlap
,
boxsize
=
boxsize
,
nclusters
=
nclusters
)
if
add_manual
:
clusters_man
=
manual_clustering
(
fig
,
ax
,
wcs
,
pix_arcmin_scale
,
startnum
=
len
(
clusters
)
+
1
)
clusters
=
clusters
+
clusters_man
else
:
clusters
=
manual_clustering
(
fig
,
ax
,
wcs
,
pix_arcmin_scale
)
if
clusters
:
write_df
(
df
,
clusters
,
output
=
output
)
fig
.
tight_layout
()
fig
.
savefig
(
path
+
'
/clustering.png
'
)
### if __name__ == "__main__":
if
__name__
==
"
__main__
"
:
# base = '/home/kutkin/apertif/clustering/191209026/01/dical2-'
# base = '/home/kutkin/apertif/clustering/191209026/15/dical2-'
# base = '/home/kutkin/apertif/clustering/191010042/00/dical2-'
# base = '/home/kutkin/apertif/clustering/191006041_21/dical2-'
# base = '/home/kutkin/apertif/clustering/191010042_25/dical-'
# base = '/home/kutkin/apertif/clustering/191209026/10/dical2-'
base
=
'
/home/kutkin/apertif/clustering/191010041/23/dical2-
'
# base = '/home/kutkin/apertif/clustering/190915041/25/dical2-'
img
=
base
+
'
image.fits
'
resid
=
base
+
'
residual.fits
'
model
=
base
+
'
sources.txt
'
# img = sys.argv[1]
# resid = sys.argv[2]
# model = sys.argv[3]
main
(
img
,
resid
,
model
,
auto
=
True
,
nclusters
=
'
auto
'
)
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