Candidate List Solution#
Ogle et al. (2016) mined the NASA/IPAC Extragalactic Database (NED) to identify a new type of galaxy: Superluminous Spiral Galaxies.
Table 1 lists the positions of these Super Spirals. Based on those positions, let’s create multiwavelength cutouts for each super spiral to see what is unique about this new class of objects.
1. Import the Python modules we’ll be using#
# Suppress unimportant warnings.
import warnings
warnings.filterwarnings("ignore", module="astropy.io.votable.*")
warnings.filterwarnings("ignore", module="pyvo.utils.xml.*")
warnings.filterwarnings('ignore', '.*RADECSYS=*', append=True)
import matplotlib.pyplot as plt
import numpy as np
# For downloading files
from astropy.utils.data import download_file
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.nddata import Cutout2D
import astropy.visualization as vis
from astropy.wcs import WCS
from astroquery.ipac.ned import Ned
import pyvo as vo
The next cell prepares the notebook to display our visualizations.
%matplotlib inline
2. Search NED for objects in this paper#
Insert a Code Cell below by clicking on the “Insert” Menu and choosing “Insert Cell Below”. Then consult QuickReference.md to figure out how to use astroquery to search NED for all objects in a paper, based on the refcode of the paper. Inspect the resulting astropy table.
objects_in_paper = Ned.query_refcode('2016ApJ...817..109O')
objects_in_paper.show_in_notebook()
WARNING: AstropyDeprecationWarning: show_in_notebook() is deprecated as of 6.1 and to create
interactive tables it is recommended to use dedicated tools like:
- https://github.com/bloomberg/ipydatagrid
- https://docs.bokeh.org/en/latest/docs/user_guide/interaction/widgets.html#datatable
- https://dash.plotly.com/datatable [warnings]
idx | No. | Object Name | RA | DEC | Type | Velocity | Redshift | Redshift Flag | Magnitude and Filter | Separation | References | Notes | Photometry Points | Positions | Redshift Points | Diameter Points | Associations |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
degrees | degrees | km / s | arcmin | ||||||||||||||
0 | 1 | WISEA J001550.14-100242.3 | 3.95892 | -10.04511 | G | 52766.0 | 0.17601 | SLS | 17.5g | -- | 15 | 0 | 63 | 8 | 9 | 10 | 0 |
1 | 2 | WISEA J003807.80-010936.7 | 9.53254 | -1.16022 | G | 62442.0 | 0.208284 | SLS | 18.0g | -- | 16 | 0 | 72 | 12 | 11 | 10 | 0 |
2 | 3 | WISEA J040422.92-054134.8 | 61.09553 | -5.693 | G | 75113.0 | 0.250549 | SLS | 18.6g | -- | 7 | 0 | 61 | 10 | 9 | 8 | 0 |
3 | 4 | WISEA J073806.16+282359.6 | 114.52568 | 28.3999 | G | 69225.0 | 0.230909 | SLS | 18.0g | -- | 10 | 0 | 66 | 7 | 7 | 10 | 0 |
4 | 5 | 2MASX J07550424+1353261 | 118.76779 | 13.89069 | G | 66746.0 | 0.22264 | SLS | 18.5g | -- | 10 | 0 | 38 | 7 | 9 | 6 | 0 |
5 | 6 | WISEA J082655.11+181147.7 | 126.72963 | 18.19659 | G | 79580.0 | 0.265449 | SLS | 18.4g | -- | 8 | 0 | 46 | 7 | 8 | 6 | 0 |
6 | 7 | 2MASX J08542169+0449308 | 133.59031 | 4.82505 | G | 47005.0 | 0.156793 | SLS | 16.7g | -- | 18 | 0 | 58 | 8 | 9 | 10 | 0 |
7 | 8 | WHL J090944.8+222607 | 137.43669 | 22.43538 | GClstr | 88683.0 | 0.295813 | PUN | -- | 7 | 0 | 0 | 4 | 6 | 0 | 0 | |
8 | 9 | 2MASX J09094480+2226078 | 137.43671 | 22.43535 | G | 85557.0 | 0.285388 | SLS | 19.0g | -- | 9 | 0 | 30 | 5 | 8 | 6 | 0 |
9 | 10 | 2MASX J09260805+2405242 | 141.53355 | 24.09009 | G | 66672.0 | 0.222393 | SLS | 17.8g | -- | 16 | 0 | 32 | 7 | 13 | 6 | 0 |
10 | 11 | WHL J092608.1+240524 | 141.53357 | 24.09003 | GClstr | 66284.0 | 0.2211 | SUN | -- | 8 | 0 | 0 | 6 | 8 | 0 | 0 | |
11 | 12 | WISEA J093347.77+211436.7 | 143.44906 | 21.24354 | G | 51620.0 | 0.172186 | SLS | 16.9g | -- | 49 | 0 | 51 | 21 | 19 | 6 | 1 |
12 | 13 | WISEA J093622.14+390628.9 | 144.09227 | 39.10805 | G | 84821.0 | 0.282931 | SLS | 18.5g | -- | 6 | 0 | 66 | 7 | 5 | 10 | 0 |
13 | 14 | MSPM 05544 | 146.2235 | 22.8851 | GClstr | 26738.0 | 0.08919 | SUN | -- | 3 | 0 | 0 | 1 | 2 | 0 | 0 | |
14 | 15 | CGCG 122-067 | 146.22362 | 22.88533 | G | 26686.0 | 0.089016 | SLS | 15.3g | -- | 34 | 0 | 49 | 13 | 16 | 8 | 0 |
15 | 16 | WISEA J094700.08+254045.8 | 146.75036 | 25.6794 | G | 32690.0 | 0.109043 | SLS | 15.8g | -- | 22 | 0 | 46 | 8 | 10 | 6 | 0 |
16 | 17 | SDSS J095727.02+083501.7 | 149.36259 | 8.58385 | G | 76903.0 | 0.256521 | SLS | 18.3g | -- | 8 | 0 | 39 | 5 | 5 | 8 | 0 |
17 | 18 | WISEA J100356.88+382902.1 | 150.98703 | 38.48393 | G | 77525.0 | 0.258596 | SLS | 17.8g | -- | 8 | 0 | 57 | 7 | 7 | 8 | 0 |
18 | 19 | WISEA J100416.04+295844.1 | 151.06686 | 29.97893 | G | 89471.0 | 0.298443 | SLS | 18.7g | -- | 7 | 0 | 66 | 7 | 7 | 10 | 0 |
19 | 20 | WISEA J100956.35+261132.0 | 152.48482 | 26.19222 | G | 72216.0 | 0.240886 | SLS | 18.1g | -- | 13 | 0 | 46 | 7 | 6 | 6 | 0 |
20 | 21 | GMBCG J152.52936+32.89139 | 152.52936 | 32.89139 | GClstr | 95634.0 | 0.319 | PUN | -- | 4 | 0 | 0 | 2 | 3 | 0 | 0 | |
21 | 22 | WISEA J101007.05+325329.0 | 152.52939 | 32.89142 | G | 86909.0 | 0.289896 | SLS | 18.7g | -- | 10 | 0 | 66 | 7 | 7 | 10 | 0 |
22 | 23 | 2MASS J10160396+3037481 | 154.01652 | 30.63005 | G | 75520.0 | 0.251906 | SLS | 18.7g | -- | 6 | 0 | 49 | 7 | 6 | 8 | 0 |
23 | 24 | WISEA J102154.85+072415.5 | 155.47855 | 7.40433 | G | 87123.0 | 0.290611 | SLS | 18.4g | -- | 6 | 0 | 55 | 7 | 5 | 8 | 0 |
24 | 25 | WISEA J103015.74-010607.0 | 157.56562 | -1.10196 | G | 84625.0 | 0.282277 | SLS | 18.3g | -- | 11 | 0 | 62 | 9 | 8 | 10 | 0 |
25 | 26 | 2MASX J10304263+0418219 | 157.67755 | 4.306 | G | 48244.0 | 0.160924 | SLS | 16.8g | -- | 24 | 0 | 59 | 8 | 9 | 10 | 0 |
26 | 27 | 2MASX J10405643-0103584 | 160.23509 | -1.06629 | G | 75021.0 | 0.250242 | SLS | 18.2g | -- | 17 | 0 | 51 | 9 | 12 | 10 | 0 |
27 | 28 | SDSS CE J160.241898-01.069106 | 160.23509 | -1.06631 | GClstr | 75548.0 | 0.252 | SUN | -- | 8 | 0 | 0 | 5 | 8 | 0 | 0 | |
28 | 29 | WISEA J104724.97+230917.4 | 161.85407 | 23.15485 | G | 54729.0 | 0.182556 | SLS | 18.46 | -- | 19 | 0 | 46 | 8 | 8 | 6 | 0 |
29 | 30 | WISEA J111917.41+141946.5 | 169.82258 | 14.3296 | G | 43101.0 | 0.143771 | SLS | 16.6g | -- | 20 | 0 | 66 | 8 | 9 | 10 | 0 |
30 | 31 | 2MASS J11292875+0255498 | 172.36979 | 2.93052 | G | 71830.0 | 0.239599 | SLS | 17.5g | -- | 9 | 0 | 49 | 7 | 7 | 8 | 0 |
31 | 32 | WISEA J113800.86+521303.8 | 174.50359 | 52.21773 | G | 88717.0 | 0.295927 | SLS | 19.0g | -- | 9 | 0 | 57 | 7 | 6 | 8 | 0 |
32 | 33 | WISEA J114100.04+384807.4 | 175.2502 | 38.80206 | G | 80255.0 | 0.267701 | SLS | 18.5g | -- | 7 | 0 | 66 | 8 | 8 | 10 | 0 |
33 | 34 | WISEA J115052.96+460448.1 | 177.72067 | 46.08004 | G | 86778.0 | 0.289461 | SLS | 18.5g | -- | 10 | 0 | 53 | 9 | 9 | 8 | 0 |
34 | 35 | WISEA J115356.21+492355.4 | 178.48421 | 49.39875 | G | 49984.0 | 0.166728 | SLS | 17.1g | -- | 19 | 0 | 67 | 9 | 10 | 10 | 0 |
35 | 36 | 2MASX J11593546+1257080 | 179.8977 | 12.95193 | G | 79003.0 | 0.263525 | SLS | 18.3g | -- | 7 | 0 | 54 | 6 | 6 | 10 | 0 |
36 | 37 | WISEA J120053.92+480007.8 | 180.22468 | 48.00217 | G | 83466.0 | 0.278413 | SLS | 18.4g | -- | 8 | 0 | 70 | 7 | 8 | 10 | 0 |
37 | 38 | GMBCG J180.22479+48.00211 | 180.22479 | 48.00211 | GClstr | 74948.0 | 0.25 | PUN | -- | 6 | 0 | 0 | 3 | 5 | 0 | 0 | |
38 | 39 | WISEA J121644.33+122450.5 | 184.18471 | 12.41404 | G | 77030.0 | 0.256944 | SLS | 18.3g | -- | 5 | 0 | 55 | 7 | 5 | 8 | 0 |
39 | 40 | WISEA J122100.50+482729.1 | 185.25209 | 48.4581 | G | 89836.0 | 0.299659 | SLS | 18.7g | -- | 8 | 0 | 61 | 8 | 5 | 8 | 0 |
40 | 41 | WISEA J123215.19+102119.1 | 188.06332 | 10.35531 | G | 49729.0 | 0.165877 | SLS | 17.2g | -- | 19 | 0 | 66 | 9 | 9 | 10 | 0 |
41 | 42 | WISEA J123431.08+515629.7 | 188.62952 | 51.94159 | G | 88714.0 | 0.295917 | SLS | 18.1g | -- | 33 | 0 | 64 | 12 | 16 | 10 | 0 |
42 | 43 | WISEA J123746.63+481227.5 | 189.44429 | 48.20764 | G | 81679.0 | 0.272452 | SLS | 18.2g | -- | 8 | 0 | 65 | 8 | 7 | 10 | 0 |
43 | 44 | WISEA J131039.32+223502.8 | 197.66385 | 22.58413 | G | 69320.0 | 0.231225 | SLS | 18.1g | -- | 12 | 0 | 48 | 8 | 7 | 6 | 1 |
44 | 45 | WISEA J132757.52+334529.3 | 201.98959 | 33.75825 | G | 74623.0 | 0.248916 | SLS | 17.7g | -- | 16 | 0 | 88 | 12 | 7 | 10 | 0 |
45 | 46 | WISEA J134228.33+115734.5 | 205.61806 | 11.9596 | G | 83561.0 | 0.27873 | SLS | 19.0g | -- | 8 | 0 | 66 | 7 | 6 | 10 | 0 |
46 | 47 | WISEA J134355.49+244048.1 | 205.98122 | 24.68004 | G | 41147.0 | 0.13725 | SLS | 17.81 | -- | 24 | 0 | 46 | 13 | 13 | 6 | 0 |
47 | 48 | SDSSCGB 16827 | 206.99833 | 32.44039 | GGroup | -- | -- | 18.44 | -- | 3 | 0 | 0 | 1 | 0 | 0 | 0 | |
48 | 49 | 2MASX J13475962+3227100 | 206.99837 | 32.45284 | G | 66870.0 | 0.223055 | SLS | 18.1g | -- | 11 | 0 | 54 | 6 | 6 | 10 | 0 |
49 | 50 | WISEA J135546.08+025456.0 | 208.94203 | 2.91557 | G | 71602.0 | 0.238837 | SLS | 18.7g | -- | 11 | 0 | 51 | 7 | 7 | 8 | 0 |
50 | 51 | WISEA J140138.37+263527.6 | 210.40992 | 26.59101 | G | 85128.0 | 0.283958 | SLS | 18.7g | -- | 8 | 0 | 30 | 5 | 6 | 4 | 0 |
51 | 52 | 2MASS J14175497+2704341 | 214.47905 | 27.07617 | G | 47226.0 | 0.15753 | SLS | 16.9g | -- | 17 | 0 | 25 | 6 | 9 | 4 | 0 |
52 | 53 | WISEA J143447.88+020228.8 | 218.6995 | 2.04136 | G | 83828.0 | 0.279619 | SLS | 18.6g | -- | 10 | 0 | 62 | 10 | 8 | 8 | 0 |
53 | 54 | WISEA J144728.35+590831.9 | 221.86816 | 59.1422 | G | 73602.0 | 0.245509 | SLS | 18.3g | -- | 15 | 0 | 70 | 11 | 10 | 10 | 0 |
54 | 55 | WISEA J153619.01+452247.7 | 234.07923 | 45.37992 | G | 70804.0 | 0.236177 | SLS | 18.1g | -- | 9 | 0 | 57 | 7 | 5 | 8 | 0 |
55 | 56 | WISEA J154307.78+193751.7 | 235.78245 | 19.63105 | G | 68774.0 | 0.229405 | SLS | 17.0g | -- | 44 | 1 | 50 | 22 | 21 | 6 | 0 |
56 | 57 | WISEA J154950.90+234444.0 | 237.46211 | 23.74557 | G | 78570.0 | 0.262082 | SLS | 18.5g | -- | 7 | 0 | 31 | 5 | 6 | 4 | 0 |
57 | 58 | GMBCG J240.41924+27.30444 | 240.41924 | 27.30444 | GClstr | 57860.0 | 0.193 | PUN | -- | 4 | 0 | 0 | 2 | 3 | 0 | 0 | |
58 | 59 | 2MASX J16014061+2718161 | 240.41925 | 27.3044 | G | 49285.0 | 0.164398 | SLS | 16.7g | -- | 22 | 0 | 54 | 7 | 11 | 10 | 1 |
59 | 60 | WISEA J163945.96+460905.8 | 249.9415 | 46.15163 | G | 74087.0 | 0.247128 | SLS | 18.3g | -- | 13 | 0 | 70 | 8 | 7 | 10 | 0 |
60 | 61 | WISEA J173406.17+602919.1 | 263.52572 | 60.48865 | G | 82730.0 | 0.275956 | SLS | 18.5g | -- | 8 | 0 | 66 | 7 | 6 | 10 | 0 |
61 | 62 | SDSSCGB 59704 | 263.53292 | 60.48197 | GGroup | 82743.0 | 0.276 | SUN | 17.20 | -- | 3 | 0 | 0 | 1 | 2 | 0 | 0 |
3. Filter the NED results#
The results from NED will include galaxies, but also other kinds of objects (e.g. galaxy clusters, galaxy groups). Print the ‘Type’ column to see the full range of classifications and filter the results so that we only keep the galaxies in the list.
objects_in_paper['Type']
G |
G |
G |
G |
G |
G |
G |
GClstr |
G |
G |
GClstr |
G |
... |
G |
G |
G |
G |
G |
G |
GClstr |
G |
G |
G |
GGroup |
# Keep only the galaxies from the list
galaxies = objects_in_paper[np.array(objects_in_paper['Type']) == 'G']
galaxies.show_in_notebook()
WARNING: AstropyDeprecationWarning: show_in_notebook() is deprecated as of 6.1 and to create
interactive tables it is recommended to use dedicated tools like:
- https://github.com/bloomberg/ipydatagrid
- https://docs.bokeh.org/en/latest/docs/user_guide/interaction/widgets.html#datatable
- https://dash.plotly.com/datatable [warnings]
idx | No. | Object Name | RA | DEC | Type | Velocity | Redshift | Redshift Flag | Magnitude and Filter | Separation | References | Notes | Photometry Points | Positions | Redshift Points | Diameter Points | Associations |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
degrees | degrees | km / s | arcmin | ||||||||||||||
0 | 1 | WISEA J001550.14-100242.3 | 3.95892 | -10.04511 | G | 52766.0 | 0.17601 | SLS | 17.5g | -- | 15 | 0 | 63 | 8 | 9 | 10 | 0 |
1 | 2 | WISEA J003807.80-010936.7 | 9.53254 | -1.16022 | G | 62442.0 | 0.208284 | SLS | 18.0g | -- | 16 | 0 | 72 | 12 | 11 | 10 | 0 |
2 | 3 | WISEA J040422.92-054134.8 | 61.09553 | -5.693 | G | 75113.0 | 0.250549 | SLS | 18.6g | -- | 7 | 0 | 61 | 10 | 9 | 8 | 0 |
3 | 4 | WISEA J073806.16+282359.6 | 114.52568 | 28.3999 | G | 69225.0 | 0.230909 | SLS | 18.0g | -- | 10 | 0 | 66 | 7 | 7 | 10 | 0 |
4 | 5 | 2MASX J07550424+1353261 | 118.76779 | 13.89069 | G | 66746.0 | 0.22264 | SLS | 18.5g | -- | 10 | 0 | 38 | 7 | 9 | 6 | 0 |
5 | 6 | WISEA J082655.11+181147.7 | 126.72963 | 18.19659 | G | 79580.0 | 0.265449 | SLS | 18.4g | -- | 8 | 0 | 46 | 7 | 8 | 6 | 0 |
6 | 7 | 2MASX J08542169+0449308 | 133.59031 | 4.82505 | G | 47005.0 | 0.156793 | SLS | 16.7g | -- | 18 | 0 | 58 | 8 | 9 | 10 | 0 |
7 | 9 | 2MASX J09094480+2226078 | 137.43671 | 22.43535 | G | 85557.0 | 0.285388 | SLS | 19.0g | -- | 9 | 0 | 30 | 5 | 8 | 6 | 0 |
8 | 10 | 2MASX J09260805+2405242 | 141.53355 | 24.09009 | G | 66672.0 | 0.222393 | SLS | 17.8g | -- | 16 | 0 | 32 | 7 | 13 | 6 | 0 |
9 | 12 | WISEA J093347.77+211436.7 | 143.44906 | 21.24354 | G | 51620.0 | 0.172186 | SLS | 16.9g | -- | 49 | 0 | 51 | 21 | 19 | 6 | 1 |
10 | 13 | WISEA J093622.14+390628.9 | 144.09227 | 39.10805 | G | 84821.0 | 0.282931 | SLS | 18.5g | -- | 6 | 0 | 66 | 7 | 5 | 10 | 0 |
11 | 15 | CGCG 122-067 | 146.22362 | 22.88533 | G | 26686.0 | 0.089016 | SLS | 15.3g | -- | 34 | 0 | 49 | 13 | 16 | 8 | 0 |
12 | 16 | WISEA J094700.08+254045.8 | 146.75036 | 25.6794 | G | 32690.0 | 0.109043 | SLS | 15.8g | -- | 22 | 0 | 46 | 8 | 10 | 6 | 0 |
13 | 17 | SDSS J095727.02+083501.7 | 149.36259 | 8.58385 | G | 76903.0 | 0.256521 | SLS | 18.3g | -- | 8 | 0 | 39 | 5 | 5 | 8 | 0 |
14 | 18 | WISEA J100356.88+382902.1 | 150.98703 | 38.48393 | G | 77525.0 | 0.258596 | SLS | 17.8g | -- | 8 | 0 | 57 | 7 | 7 | 8 | 0 |
15 | 19 | WISEA J100416.04+295844.1 | 151.06686 | 29.97893 | G | 89471.0 | 0.298443 | SLS | 18.7g | -- | 7 | 0 | 66 | 7 | 7 | 10 | 0 |
16 | 20 | WISEA J100956.35+261132.0 | 152.48482 | 26.19222 | G | 72216.0 | 0.240886 | SLS | 18.1g | -- | 13 | 0 | 46 | 7 | 6 | 6 | 0 |
17 | 22 | WISEA J101007.05+325329.0 | 152.52939 | 32.89142 | G | 86909.0 | 0.289896 | SLS | 18.7g | -- | 10 | 0 | 66 | 7 | 7 | 10 | 0 |
18 | 23 | 2MASS J10160396+3037481 | 154.01652 | 30.63005 | G | 75520.0 | 0.251906 | SLS | 18.7g | -- | 6 | 0 | 49 | 7 | 6 | 8 | 0 |
19 | 24 | WISEA J102154.85+072415.5 | 155.47855 | 7.40433 | G | 87123.0 | 0.290611 | SLS | 18.4g | -- | 6 | 0 | 55 | 7 | 5 | 8 | 0 |
20 | 25 | WISEA J103015.74-010607.0 | 157.56562 | -1.10196 | G | 84625.0 | 0.282277 | SLS | 18.3g | -- | 11 | 0 | 62 | 9 | 8 | 10 | 0 |
21 | 26 | 2MASX J10304263+0418219 | 157.67755 | 4.306 | G | 48244.0 | 0.160924 | SLS | 16.8g | -- | 24 | 0 | 59 | 8 | 9 | 10 | 0 |
22 | 27 | 2MASX J10405643-0103584 | 160.23509 | -1.06629 | G | 75021.0 | 0.250242 | SLS | 18.2g | -- | 17 | 0 | 51 | 9 | 12 | 10 | 0 |
23 | 29 | WISEA J104724.97+230917.4 | 161.85407 | 23.15485 | G | 54729.0 | 0.182556 | SLS | 18.46 | -- | 19 | 0 | 46 | 8 | 8 | 6 | 0 |
24 | 30 | WISEA J111917.41+141946.5 | 169.82258 | 14.3296 | G | 43101.0 | 0.143771 | SLS | 16.6g | -- | 20 | 0 | 66 | 8 | 9 | 10 | 0 |
25 | 31 | 2MASS J11292875+0255498 | 172.36979 | 2.93052 | G | 71830.0 | 0.239599 | SLS | 17.5g | -- | 9 | 0 | 49 | 7 | 7 | 8 | 0 |
26 | 32 | WISEA J113800.86+521303.8 | 174.50359 | 52.21773 | G | 88717.0 | 0.295927 | SLS | 19.0g | -- | 9 | 0 | 57 | 7 | 6 | 8 | 0 |
27 | 33 | WISEA J114100.04+384807.4 | 175.2502 | 38.80206 | G | 80255.0 | 0.267701 | SLS | 18.5g | -- | 7 | 0 | 66 | 8 | 8 | 10 | 0 |
28 | 34 | WISEA J115052.96+460448.1 | 177.72067 | 46.08004 | G | 86778.0 | 0.289461 | SLS | 18.5g | -- | 10 | 0 | 53 | 9 | 9 | 8 | 0 |
29 | 35 | WISEA J115356.21+492355.4 | 178.48421 | 49.39875 | G | 49984.0 | 0.166728 | SLS | 17.1g | -- | 19 | 0 | 67 | 9 | 10 | 10 | 0 |
30 | 36 | 2MASX J11593546+1257080 | 179.8977 | 12.95193 | G | 79003.0 | 0.263525 | SLS | 18.3g | -- | 7 | 0 | 54 | 6 | 6 | 10 | 0 |
31 | 37 | WISEA J120053.92+480007.8 | 180.22468 | 48.00217 | G | 83466.0 | 0.278413 | SLS | 18.4g | -- | 8 | 0 | 70 | 7 | 8 | 10 | 0 |
32 | 39 | WISEA J121644.33+122450.5 | 184.18471 | 12.41404 | G | 77030.0 | 0.256944 | SLS | 18.3g | -- | 5 | 0 | 55 | 7 | 5 | 8 | 0 |
33 | 40 | WISEA J122100.50+482729.1 | 185.25209 | 48.4581 | G | 89836.0 | 0.299659 | SLS | 18.7g | -- | 8 | 0 | 61 | 8 | 5 | 8 | 0 |
34 | 41 | WISEA J123215.19+102119.1 | 188.06332 | 10.35531 | G | 49729.0 | 0.165877 | SLS | 17.2g | -- | 19 | 0 | 66 | 9 | 9 | 10 | 0 |
35 | 42 | WISEA J123431.08+515629.7 | 188.62952 | 51.94159 | G | 88714.0 | 0.295917 | SLS | 18.1g | -- | 33 | 0 | 64 | 12 | 16 | 10 | 0 |
36 | 43 | WISEA J123746.63+481227.5 | 189.44429 | 48.20764 | G | 81679.0 | 0.272452 | SLS | 18.2g | -- | 8 | 0 | 65 | 8 | 7 | 10 | 0 |
37 | 44 | WISEA J131039.32+223502.8 | 197.66385 | 22.58413 | G | 69320.0 | 0.231225 | SLS | 18.1g | -- | 12 | 0 | 48 | 8 | 7 | 6 | 1 |
38 | 45 | WISEA J132757.52+334529.3 | 201.98959 | 33.75825 | G | 74623.0 | 0.248916 | SLS | 17.7g | -- | 16 | 0 | 88 | 12 | 7 | 10 | 0 |
39 | 46 | WISEA J134228.33+115734.5 | 205.61806 | 11.9596 | G | 83561.0 | 0.27873 | SLS | 19.0g | -- | 8 | 0 | 66 | 7 | 6 | 10 | 0 |
40 | 47 | WISEA J134355.49+244048.1 | 205.98122 | 24.68004 | G | 41147.0 | 0.13725 | SLS | 17.81 | -- | 24 | 0 | 46 | 13 | 13 | 6 | 0 |
41 | 49 | 2MASX J13475962+3227100 | 206.99837 | 32.45284 | G | 66870.0 | 0.223055 | SLS | 18.1g | -- | 11 | 0 | 54 | 6 | 6 | 10 | 0 |
42 | 50 | WISEA J135546.08+025456.0 | 208.94203 | 2.91557 | G | 71602.0 | 0.238837 | SLS | 18.7g | -- | 11 | 0 | 51 | 7 | 7 | 8 | 0 |
43 | 51 | WISEA J140138.37+263527.6 | 210.40992 | 26.59101 | G | 85128.0 | 0.283958 | SLS | 18.7g | -- | 8 | 0 | 30 | 5 | 6 | 4 | 0 |
44 | 52 | 2MASS J14175497+2704341 | 214.47905 | 27.07617 | G | 47226.0 | 0.15753 | SLS | 16.9g | -- | 17 | 0 | 25 | 6 | 9 | 4 | 0 |
45 | 53 | WISEA J143447.88+020228.8 | 218.6995 | 2.04136 | G | 83828.0 | 0.279619 | SLS | 18.6g | -- | 10 | 0 | 62 | 10 | 8 | 8 | 0 |
46 | 54 | WISEA J144728.35+590831.9 | 221.86816 | 59.1422 | G | 73602.0 | 0.245509 | SLS | 18.3g | -- | 15 | 0 | 70 | 11 | 10 | 10 | 0 |
47 | 55 | WISEA J153619.01+452247.7 | 234.07923 | 45.37992 | G | 70804.0 | 0.236177 | SLS | 18.1g | -- | 9 | 0 | 57 | 7 | 5 | 8 | 0 |
48 | 56 | WISEA J154307.78+193751.7 | 235.78245 | 19.63105 | G | 68774.0 | 0.229405 | SLS | 17.0g | -- | 44 | 1 | 50 | 22 | 21 | 6 | 0 |
49 | 57 | WISEA J154950.90+234444.0 | 237.46211 | 23.74557 | G | 78570.0 | 0.262082 | SLS | 18.5g | -- | 7 | 0 | 31 | 5 | 6 | 4 | 0 |
50 | 59 | 2MASX J16014061+2718161 | 240.41925 | 27.3044 | G | 49285.0 | 0.164398 | SLS | 16.7g | -- | 22 | 0 | 54 | 7 | 11 | 10 | 1 |
51 | 60 | WISEA J163945.96+460905.8 | 249.9415 | 46.15163 | G | 74087.0 | 0.247128 | SLS | 18.3g | -- | 13 | 0 | 70 | 8 | 7 | 10 | 0 |
52 | 61 | WISEA J173406.17+602919.1 | 263.52572 | 60.48865 | G | 82730.0 | 0.275956 | SLS | 18.5g | -- | 8 | 0 | 66 | 7 | 6 | 10 | 0 |
6. Choose the AllWISE image service that you are interested in#
allwise_image_service = allwise_image_services[0]
allwise_image_service.service
SIAService(baseurl : 'https://irsa.ipac.caltech.edu/ibe/sia/wise/allwise/p3am_cdd?', description : 'None')
7. Choose one of the galaxies in the NED list#
ra = galaxies['RA'][0]
dec = galaxies['DEC'][0]
pos = SkyCoord(ra, dec, unit = 'deg')
ra,dec
(np.float64(3.95892), np.float64(-10.04511))
8. Search for a list of AllWISE images that cover this galaxy#
How many images are returned? Which are you most interested in?
allwise_image_table = allwise_image_service.search(pos=pos, size=0)
allwise_image_table
<DALResultsTable length=4>
sia_title ... coadd_id
...
object ... object
---------------------- ... -------------
W2 Coadd 0046m107_ac51 ... 0046m107_ac51
W1 Coadd 0046m107_ac51 ... 0046m107_ac51
W3 Coadd 0046m107_ac51 ... 0046m107_ac51
W4 Coadd 0046m107_ac51 ... 0046m107_ac51
9. Use the .to_table() method to view the results as an Astropy table#
allwise_images = allwise_image_table.to_table()
allwise_images
sia_title | sia_url | cloud_access | sia_naxes | sia_fmt | sia_ra | sia_dec | sia_naxis | sia_crpix | sia_crval | sia_proj | sia_scale | sia_cd | sia_bp_id | sia_bp_ref | sia_bp_hi | sia_bp_lo | sia_bp_unit | magzp | magzpunc | unc_url | cov_url | coadd_id |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
deg | deg | pix | deg | deg / pix | deg / pix | |||||||||||||||||
object | object | object | int32 | object | float64 | float64 | int32[2] | float64[2] | float64[2] | object | float64[2] | float64[4] | object | float64 | float64 | float64 | object | float64 | float64 | object | object | object |
W2 Coadd 0046m107_ac51 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-int-3.fits | {"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-int-3.fits", "region": "us-west-2"}} | 2 | image/fits | 4.61538 | -10.601111 | 4095 .. 4095 | 2048.0 .. 2048.0 | 4.61538 .. -10.601111 | SIN | -0.0003819444391411 .. 0.0003819444391411 | -0.0003819444391411 .. 0.0003819444391411 | W2 | 4.6e-06 | 5.19e-06 | 4.02e-06 | m | 19.5 | 0.007 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-unc-3.fits.gz | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-cov-3.fits.gz | 0046m107_ac51 |
W1 Coadd 0046m107_ac51 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-int-3.fits | {"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-int-3.fits", "region": "us-west-2"}} | 2 | image/fits | 4.61538 | -10.601111 | 4095 .. 4095 | 2048.0 .. 2048.0 | 4.61538 .. -10.601111 | SIN | -0.0003819444391411 .. 0.0003819444391411 | -0.0003819444391411 .. 0.0003819444391411 | W1 | 3.35e-06 | 3.78e-06 | 3.13e-06 | m | 20.5 | 0.006 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-unc-3.fits.gz | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-cov-3.fits.gz | 0046m107_ac51 |
W3 Coadd 0046m107_ac51 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-int-3.fits | {"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-int-3.fits", "region": "us-west-2"}} | 2 | image/fits | 4.61538 | -10.601111 | 4095 .. 4095 | 2048.0 .. 2048.0 | 4.61538 .. -10.601111 | SIN | -0.0003819444391411 .. 0.0003819444391411 | -0.0003819444391411 .. 0.0003819444391411 | W3 | 1.156e-05 | 1.627e-05 | 7.6e-06 | m | 18.0 | 0.012 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-unc-3.fits.gz | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-cov-3.fits.gz | 0046m107_ac51 |
W4 Coadd 0046m107_ac51 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-int-3.fits | {"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-int-3.fits", "region": "us-west-2"}} | 2 | image/fits | 4.61538 | -10.601111 | 4095 .. 4095 | 2048.0 .. 2048.0 | 4.61538 .. -10.601111 | SIN | -0.0003819444391411 .. 0.0003819444391411 | -0.0003819444391411 .. 0.0003819444391411 | W4 | 2.209e-05 | 2.336e-05 | 1.984e-05 | m | 13.0 | 0.012 | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-unc-3.fits.gz | https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-cov-3.fits.gz | 0046m107_ac51 |
10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)#
Hints:
Loop over records and test on the
.bandpass_id
attribute of each recordPrint the
.title
and.bandpass_id
of the record you find, to verify it is the right one.
for allwise_image_record in allwise_image_table:
if 'W1' in allwise_image_record.bandpass_id:
break
print(allwise_image_record.title, allwise_image_record.bandpass_id)
W1 Coadd 0046m107_ac51 W1
11. Visualize this AllWISE image#
## If you only run this once, you can do it in memory in one line:
## This fetches the FITS as an astropy.io.fits object in memory
#allwise_w1_image = allwise_image_record.getdataobj()
## But if you might run this notebook repeatedly with limited bandwidth,
## download it once and cache it.
file_name = download_file(allwise_image_record.getdataurl(), cache=True)
allwise_w1_image = fits.open(file_name)
fig = plt.figure()
wcs = WCS(allwise_w1_image[0].header)
ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.imshow(allwise_w1_image[0].data, cmap='gray_r', origin='lower', vmax = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
<matplotlib.collections.PathCollection at 0x7fe52d2edf00>
12. Plot a cutout of the AllWISE image, centered on your position#
Try a 60 arcsecond cutout.
size = 60
cutout = Cutout2D(allwise_w1_image[0].data, pos, (size, size), wcs=wcs)
wcs = cutout.wcs
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.imshow(cutout.data, cmap='gray_r', origin='lower', vmax = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
<matplotlib.collections.PathCollection at 0x7fe525039c60>
13. Try visualizing a cutout of a GALEX image that covers your position#
Repeat steps 4, 5, 6, 8 through 12 for GALEX.
galex_image_services = vo.regsearch(keywords=['galex'], servicetype='sia')
print(f'{len(galex_image_services)} result(s) found.')
galex_image_services.to_table()['ivoid', 'short_name', 'res_title']
3 result(s) found.
ivoid | short_name | res_title |
---|---|---|
object | object | object |
ivo://archive.stsci.edu/sia/galex | GALEX | Galaxy Evolution Explorer (GALEX) |
ivo://mast.stsci/siap/galex_atlas | GALEX_Atlas | GALEX Atlas of Nearby Galaxies |
ivo://nasa.heasarc/skyview/galex | GALEX | Galaxy Explorer All Sky Survey: Near UV |
galex_image_service = galex_image_services[0]
galex_image_table = galex_image_service.search(pos=pos, size=0.0, intersect='covers')
for i in range(len(galex_image_table)):
if (('image/fits' in galex_image_table[i].format) and
(galex_image_table['energy_bounds_center'][i]==2.35e-07) and
(galex_image_table[i]['productType'] == 'SCIENCE')):
break
galex_image_record = galex_image_table[i]
print(galex_image_record.title, galex_image_record.bandpass_id)
AIS_270_0005_sg14-nd-int.fits.gz NUV
## See above regarding two ways to do this:
#galex_nuv_image = fits.open(galex_image_record.getdataurl())
file_name = download_file(galex_image_record.getdataurl(), cache=True)
galex_nuv_image=fits.open(file_name)
image_data = galex_nuv_image[0].data
print('Min:', np.min(image_data))
print('Max:', np.max(image_data))
print('Mean:', np.mean(image_data))
print('Stdev:', np.std(image_data))
Min: 0.0
Max: 8.563285
Mean: 0.0014577592
Stdev: 0.013107345
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=WCS(galex_nuv_image[0].header))
ax.imshow(galex_nuv_image[0].data, cmap='gray_r', origin='lower', vmin=0.0, vmax=0.01)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
<matplotlib.collections.PathCollection at 0x7fe51de17ac0>
cutout = Cutout2D(galex_nuv_image[0].data, pos, size, wcs=WCS(galex_nuv_image[0].header))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=cutout.wcs)
ax.imshow(cutout.data, cmap='gray_r', origin='lower', vmin = 0.0, vmax = 0.01)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
<matplotlib.collections.PathCollection at 0x7fe51deb7c40>
14. Try visualizing a cutout of an SDSS image that covers your position#
Hints:
Search the registry using `keywords=[‘sloan’]
Find the service with a
short_name
of'SDSS SIAP'
After obtaining your search results, select r-band images using the
.title
attribute of the records that are returned, since.bandpass_id
is not populated.
sdss_image_services = vo.regsearch(keywords=['sloan'], servicetype='sia')
sdss_image_services.to_table()['ivoid', 'short_name', 'res_title', 'source_value']
ivoid | short_name | res_title | source_value |
---|---|---|---|
object | object | object | object |
ivo://mast.stsci/siap/al218 | VLA.AL218 | VLA-A Array AL218 Texas Survey Source Snapshots (AL218) | |
ivo://mast.stsci/siap/vla-first | VLA-FIRST | VLA Faint Images of the Radio Sky at Twenty Centimeters (FIRST) | |
ivo://nasa.heasarc/skyview/sdss | SDSS | Sloan Digital Sky Survey g-band | |
ivo://nasa.heasarc/skyview/sdssdr7 | SDSSDR7 | Sloan Digital Sky Survey g-band DR7 | |
ivo://nasa.heasarc/skyview/stripe82vla | Stripe82VLA | VLA Survey of SDSS Stripe 82 | |
ivo://org.gavo.dc/bgds/q/sia | bgds sia | Bochum Galactic Disk Survey (BGDS) images | 2015AN....336..590H |
ivo://sdss.jhu/services/siap-images | SDSS SIAP | Sloan Digital Sky Survey Images (Latest Release) | |
ivo://sdss.jhu/services/siapdr1-images | SDSSDR1 | Sloan Digital Sky Survey DR1 - Images | |
ivo://sdss.jhu/services/siapdr2-images | SDSSDR2 | Sloan Digital Sky Survey DR2 - Images | |
ivo://sdss.jhu/services/siapdr3-color | SDSSDR3-Color | Sloan Digital Sky Survey DR3 | |
ivo://sdss.jhu/services/siapdr3-g | SDSSDR3-G | Sloan Digital Sky Survey DR3 - Filter g | |
ivo://sdss.jhu/services/siapdr3-i | SDSSDR3-I | Sloan Digital Sky Survey DR3 - Filter i | |
ivo://sdss.jhu/services/siapdr3-r | SDSSDR3-R | Sloan Digital Sky Survey DR3 - Filter r | |
ivo://sdss.jhu/services/siapdr3-u | SDSSDR3-U | Sloan Digital Sky Survey DR3 - Filter u | |
ivo://sdss.jhu/services/siapdr3-z | SDSSDR3-Z | Sloan Digital Sky Survey DR3 - Filter z | |
ivo://sdss.jhu/services/siapdr4-color | SDSSDR4-Color | Sloan Digital Sky Survey DR4 | |
ivo://sdss.jhu/services/siapdr4-images | SDSSDR4 | Sloan Digital Sky Survey DR4 - Images | |
ivo://sdss.jhu/services/siapdr5-images | SDSSDR5 | Sloan Digital Sky Survey DR5 - Images | |
ivo://sdss.jhu/services/siapdr7-images | SDSSDR7 | Sloan Digital Sky Survey DR7 - Images | |
ivo://sdss.jhu/services/siapdr8-images | SDSSDR8 | Sloan Digital Sky Survey DR8 - Images | |
ivo://sdss.jhu/services/siapdr9-images | SDSSDR9 | Sloan Digital Sky Survey DR9 - Images |
# Use list comprehension to check each service's short_name attribute.
# Given the above, we know the first match is the right one.
sdss_image_service = [s for s in sdss_image_services if 'SDSS SIAP' in s.short_name ][0]
sdss_image_service.short_name
'SDSS SIAP'
sdss_image_table = sdss_image_service.search(pos=pos, size=0.0)
len(sdss_image_table['Title'])
60
for sdss_rband_record in sdss_image_table:
if 'Sloan Digital Sky Survey - Filter r' in sdss_rband_record.title:
break
print(sdss_rband_record.title, sdss_rband_record.bandpass_id)
Sloan Digital Sky Survey - Filter r None
## See above regarding two ways to do this
# sdss_rband_image = fits.open(sdss_rband_record.getdataurl())
file_name = download_file(sdss_rband_record.getdataurl(), cache=True)
sdss_rband_image=fits.open(file_name)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=WCS(sdss_rband_image[0].header))
interval = vis.PercentileInterval(99.9)
vmin,vmax = interval.get_limits(sdss_rband_image[0].data)
norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
ax.imshow(sdss_rband_image[0].data, cmap = 'gray_r', norm = norm, origin = 'lower')
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]
<matplotlib.collections.PathCollection at 0x7fe516d73070>
cutout = Cutout2D(sdss_rband_image[0].data, pos, size, wcs=WCS(sdss_rband_image[0].header))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=cutout.wcs)
vmin,vmax = interval.get_limits(sdss_rband_image[0].data)
norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
ax.imshow(cutout.data, cmap = 'gray_r', norm = norm, origin = 'lower')
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]
<matplotlib.collections.PathCollection at 0x7fe516ba7310>
15. Try looping over all positions and plotting multiwavelength cutouts#
Warning: this cell takes a long time to run! We limit it to the first three galaxies only.
# Pick the first 3 galaxies.
galaxy_subset = galaxies[0:3]
# For each galaxy,
for galaxy in galaxy_subset:
# Establish the position.
ra = galaxy['RA']
dec = galaxy['DEC']
pos = SkyCoord(ra, dec, unit = 'deg')
# Set up the plot for this position.
fig = plt.figure(figsize=(20,6))
plt.suptitle('POSITION = ' + str(ra) + ', ' + str(dec), fontsize=16)
# GALEX
# Find the GALEX images that overlap the position.
galex_image_table = galex_image_service.search(pos=pos, size=0.25)
# Find the GALEX All-Sky Image Survey (AIS) Near-UV FITS coadd.
galex_image_record = None
for record in galex_image_table:
if (('image/fits' in record.format) and
(record['energy_bounds_center'] == 2.35e-07) and
(record['productType'] == 'SCIENCE')):
galex_image_record = record
break
if galex_image_record is not None:
# Create a cutout.
file_name = download_file(galex_image_record.getdataurl(), cache=True)
gimage = fits.open(file_name)
galex_cutout = Cutout2D(gimage[0].data, pos, size, wcs=WCS(gimage[0].header))
# Plot the cutout in the first position of a 1x3 (rowsxcols) grid.
ax = fig.add_subplot(1, 3, 1, projection=galex_cutout.wcs)
ax.set_title(galex_image_record.title)
ax.imshow(galex_cutout.data, cmap='gray_r', origin='lower', vmin = 0.0, vmax = 0.01)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
else:
# We didn't find a suitable image, so leave that subplot blank.
ax = fig.add_subplot(1, 3, 1, projection=galex_cutout.wcs)
ax.set_title('GALEX image not found')
# SDSS
# Find the SDSS images that overlap the position.
sdss_image_table = sdss_image_service.search(pos=pos, size=0)
# Find the first SDSS r-band image.
sdss_rband_record = None
for record in sdss_image_table:
if 'Sloan Digital Sky Survey - Filter r' in record.title:
sdss_rband_record = record
break
if sdss_rband_record is not None:
# Create a cutout.
file_name = download_file(sdss_rband_record.getdataurl(), cache=True)
sdss_rband_image=fits.open(file_name)
sdss_cutout = Cutout2D(sdss_rband_image[0].data, pos, size,
wcs=WCS(sdss_rband_image[0].header))
# Plot the cutout in the second position of a 1x3 grid.
vmin,vmax = interval.get_limits(sdss_cutout.data)
norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
ax = fig.add_subplot(1, 3, 2, projection=sdss_cutout.wcs)
ax.imshow(sdss_cutout.data, cmap = 'gray_r', norm = norm, origin = 'lower')
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
ax.set_title(sdss_rband_record.title)
else:
# We didn't find a suitable image, so leave that subplot blank.
ax = fig.add_subplot(1, 3, 2, projection=galex_cutout.wcs)
ax.set_title('SDSS rband image not found')
# AllWISE
# Find the AllWISE images that overlap the position.
allwise_image_table = allwise_image_service.search(pos=pos, size=0)
# Find the first AllWISE W1 channel image.
allwise_image_record = None
for record in allwise_image_table:
if 'W1' in record.bandpass_id:
allwise_image_record = record
break
if allwise_image_record is not None:
# Create a cutout.
file_name = download_file(allwise_image_record.getdataurl(), cache=True)
allwise_w1_image=fits.open(file_name)
allwise_cutout = Cutout2D(allwise_w1_image[0].data, pos, (size, size),
wcs=WCS(allwise_w1_image[0].header))
# Plot the cutout in the third position of a 1x3 grid.
ax = fig.add_subplot(1, 3, 3, projection=allwise_cutout.wcs)
ax.imshow(allwise_cutout.data, cmap='gray_r', origin='lower', vmax = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
ax.set_title(allwise_image_record.title)
else:
# We didn't find a suitable image, so leave that subplot blank.
ax = fig.add_subplot(1, 3, 3, projection=galex_cutout.wcs)
ax.set_title('AllWISE W1 image not found')
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54424.000000 from DATE-OBS'. [astropy.wcs.wcs]
WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 51465.000000 from DATE-OBS'. [astropy.wcs.wcs]