CDS services in Jupyter notebooks#

Katarina A. Lutz¹, Manon Marchand¹

  1. Université de Strasbourg, CNRS, Observatoire Astronomique de Strasbourg, UMR 7550, F-67000, Strasbourg, France


Introduction#

Welcome to a Jupyter notebook demonstrating how to access some of the services provided by the Strasbourg astronomical Data Center (CDS) with Python.

In this tutorial, we will:

  1. gather information on a specific object using Simbad,

  2. visualise it through survey images using the Aladin Lite widget,

  3. find and download a catalogue from Vizier,

  4. overlay the sources of this catalog on the sky view in the Aladin Lite widget,

  5. cross-match the sources with a large catalogue using Xmatch.

# Astronomy tools
import astropy.units as u

# Access astronomical databases
from astroquery.simbad import Simbad
from astroquery.vizier import Vizier
from astroquery.xmatch import XMatch

# Sky visualization
from ipyaladin import Aladin

Step #1: Gather information on NGC4038#

Simbad

As a first step, we check what information is directly available from the Simbad python module. Let’s use the method query_object to gather information. Note that you can look at the methods available for the Simbad class with the help(Simbad) command:

Simbad.query_object("NGC4038")
Table length=1
main_idradeccoo_err_majcoo_err_mincoo_err_anglecoo_wavelengthcoo_bibcodematched_id
degdegmasmasdeg
objectfloat64float64float32float32int16str1objectobject
NGC 4038180.47084166666667-18.86758888888889------I2006AJ....131.1163SNGC 4038

We get basic information, like the position of NCG4038. This search function can be personalized by adding fields to the response. Let’s list the possible fields:

fields = Simbad.list_votable_fields()
fields
Table length=116
namedescriptiontype
objectobjectobject
mesDiameterCollection of stellar diameters.table
mesPMCollection of proper motions.table
mesISOInfrared Space Observatory (ISO) observing log.table
mesSpTCollection of spectral types.table
allfluxesall flux/magnitudes U,B,V,I,J,H,K,u_,g_,r_,i_,z_table
identIdentifiers of an astronomical objecttable
fluxMagnitude/Flux information about an astronomical objecttable
mesPLXCollection of trigonometric parallaxes.table
otypedefall names and definitions for the object typestable
mesDistanceCollection of distances (pc, kpc or Mpc) by several means.table
otypesList of all object types associated with an objecttable
mesVarCollection of stellar variability types and periods.table
mesXmmXMM observing log.table
.........
RMagnitude Rfilter name
IMagnitude Ifilter name
JMagnitude Jfilter name
HMagnitude Hfilter name
KMagnitude Kfilter name
uMagnitude SDSS ufilter name
gMagnitude SDSS gfilter name
rMagnitude SDSS rfilter name
iMagnitude SDSS ifilter name
zMagnitude SDSS zfilter name
GMagnitude Gaia Gfilter name
F150WJWST NIRCam F150Wfilter name
F200WJWST NIRCam F200Wfilter name
F444WJWST NIRCan F444Wfilter name

We get a list of the available fields we could add to the SIMBAD response.

simbad = Simbad()  # we create an instance that we can personalize
simbad.add_votable_fields("otype", "alltypes", "dim")
info_simbad = simbad.query_object("NGC4038")
info_simbad
Table length=1
main_idradeccoo_err_majcoo_err_mincoo_err_anglecoo_wavelengthcoo_bibcodeotypeotypesgaldim_majaxis_precgaldim_minaxis_precgaldim_bibcodegaldim_majaxisgaldim_minaxisgaldim_qualgaldim_anglegaldim_wavelengthmatched_id
degdegmasmasdegarcminarcmindeg
objectfloat64float64float32float32int16str1objectobjectobjectint16int16objectfloat32float32str1int16str1object
NGC 4038180.47084166666667-18.86758888888889------I2006AJ....131.1163SEmGAG?|EmG|G|GiP|HI|IG|PaG|Rad112007ApJS..173..185G5.23.1D80NGC 4038

We added the object type with the field otype. We now know the main object type of NGC4038: “Emission line Galaxy” EmG.

We can also read the object dimensions, that we added with the field dim. (major axis galdim_majaxis, minor axis galdim_minaxis, inclination angle galdim_angle…, and the related bibliographic reference galdim_bibcode where the dimension information was extracted).

We can also see the full list of object types attributed to NGC4038 in the past (ex: HI HI (21cm) Source, G Galaxy, GiP Galaxy in Pair, Rad Radio Source…). This comes from the fact that an object can have a lot of descriptions, a galaxy can very well also be a radio source. Sometimes, our understanding evolved with time and some of these former types won’t be compatible. The descriptions and relations between object types can be explored on Simbad’s webpages.

As we saw that NGC4038 is a galaxy in a pair, let’s search for the second galaxy in the pair. Let’s first identify the pair by looking into NGC4038’s parents:

simbad.reset_votable_fields()
simbad.add_votable_fields("otype")
siblings = simbad.query_hierarchy("NGC4038", hierarchy="parents")
siblings
Table length=4
main_idradeccoo_err_majcoo_err_mincoo_err_anglecoo_wavelengthcoo_bibcodeotype
degdegmasmasdeg
objectfloat64float64float32float32int16str1objectobject
NAME Antennae180.47154166666667-18.8772------I2006AJ....131.1163SIG
NAME NGC 4038 Group179.98833333333332-19.2725------O1993A&AS..100...47GGrG
[T2015] nest 100219179.8429-18.9931------O2015AJ....149..171TGrG
[TSK2008] 409179.85802908705062-19.40075160642234------O2013AJ....146...86TGrG

The Antennae Interacting Galaxies (of object type IG) ais the parent. Let’s now look at its children. We also add a filter on the type of objects with the criteria argument in which we select any type of galaxies (that’s the notation G..)

siblings = simbad.query_hierarchy(
    "Antennae", hierarchy="children", criteria="otype='G..'"
)
siblings
Table length=2
main_idradeccoo_err_majcoo_err_mincoo_err_anglecoo_wavelengthcoo_bibcodeotype
degdegmasmasdeg
objectfloat64float64float32float32int16str1objectobject
NGC 4038180.47084166666667-18.86758888888889------I2006AJ....131.1163SEmG
NGC 4039180.47295999999997-18.88619------O2020MNRAS.494.1784AGiP

You can learn more on the features of the Simbad python module here: https://astroquery.readthedocs.io/en/latest/simbad/simbad.html

and on the Simbad query criteria: http://simbad.cds.unistra.fr/simbad/sim-fsam

Step #2: Visualise the source on a sky map#

Aladin

We now open the Aladin Lite widget. We set it first to center on the Antennae galaxies, have a field of view of 0.5 \(^\circ\) and show color images from the Digitized Sky Survey (DSS). Typing aladin in the second line of code tells the notebook to display the widget.

aladin = Aladin(target="Antennae", fov=0.5, survey="P/DSS2/color", height=600)
aladin

As with any Aladin Lite implementation, you can interact with this widget. Try to:

  • scroll with your mouse pointer on the image to zoom in and out,

  • select other image surveys and manage the current view with

  • look at another target using the search field

  • find more information on something with the target tool

  • do a right click to discover more tools

You can also directly change the properties of the variable aladin. For example, to focus on M101 instead of the Antennae galaxies:

aladin.target = "M101"

Feel free to explore other targets and methods available for the Aladin widget.

Step #3: Find and visualize additional data#

Aladin Simbad Vizier

We want to look for data related to HII regions in these interacting galaxies. We make a query in Simbad for HII regions within 14 arcmins around the Antennae galaxies.

simbad.reset_votable_fields()
# biblio contains the bibcodes of all papers where the SIMBAD team found the object
simbad.add_votable_fields("otype", "biblio")
table_simbad = simbad.query_region("Antennae", radius="14m", criteria="otype='HII'")
table_simbad
Table length=317
main_idradeccoo_err_majcoo_err_mincoo_err_anglecoo_wavelengthcoo_bibcodeotypebiblio
degdegmasmasdeg
objectfloat64float64float32float32int16str1objectobjectobject
[ZFB2014] HII 47180.46208333333334-18.86958333333333------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z
[ZBF2015] Arp244 175180.4625-18.861666666666668------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 77180.47750000000002-18.881194444444443------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 120180.47916666666669-18.88586111111111------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 252180.47208333333333-18.885027777777776------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 264180.4754166666667-18.867416666666667------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 233180.48125000000002-18.86836111111111------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 195180.4779166666667-18.867416666666667------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 271180.47333333333336-18.886583333333334------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
RX J120155.4-185209180.48083333333335-18.86916666666667------HII1995MNRAS.277..397R|1997ApJ...478..542F
[MDL92] I180.36370833333336-19.015194444444447------2004AJ....127..660SHII1992A&A...256L..19M|2001AJ....122.2969H|2004AJ....127..660S
[ZBF2015] Arp244 187180.47750000000002-18.878333333333334------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 189180.46125-18.871333333333336------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
..............................
[ZBF2015] Arp244 61180.47750000000002-18.868972222222222------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 198180.48333333333335-18.876055555555556------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 234180.4779166666667-18.874194444444445------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 150180.4779166666667-18.88111111111111------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 204180.4691666666667-18.869------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 275180.47833333333335-18.880694444444444------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[HMR2016] N4038 H29180.481-18.880719444444445------O2016ApJ...830...10HHII2016ApJ...830...10H
[ZBF2015] Arp244 45180.48125000000002-18.873250000000002------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 155180.46333333333334-18.875500000000002------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 158180.46583333333334-18.875416666666666------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 202180.47708333333335-18.88138888888889------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 224180.47750000000002-18.87863888888889------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z
[ZBF2015] Arp244 211180.4691666666667-18.858333333333334------O2014MNRAS.445.1412ZHII2014MNRAS.445.1412Z|2015MNRAS.451.1307Z

Once we have identified a bibliographic reference of interest, we can search the corresponding catalogs on VizieR. Here, the Biblio column has quite some occurrences of the reference '2015MNRAS.451.1307Z'.

Note that by default VizieR will only return 50 entries (as in the web interface). To get all sources, set ROW_LIMIT=-1.

A good practice is to check first what data are available by querying only a subsample, then customize your Vizier query with the useful rows and columns before downloading the entire catalog.

cat_viz = Vizier(row_limit=10).get_catalogs("2015MNRAS.451.1307Z")
cat_viz
WARNING: UnitsWarning: The unit 'pix' has been deprecated in the VOUnit standard. [astropy.units.format.utils]
TableList with 2 tables:
	'0:J/MNRAS/451/1307/HIIreg' with 15 column(s) and 10 row(s) 
	'1:J/MNRAS/451/1307/list' with 13 column(s) and 10 row(s) 

In the example above, we see that 2 tables are associated with the reference 2015MNRAS.451.1307Z: a table called ‘HIIreg’ accessible by the index [0] and a table called ‘list’ accessible by the index [1]. The number of rows has been fixed by the parameter row_limit=10.

Let’s check the ‘HIIreg’ table:

# print the metadata
cat_viz[0].meta
{'ID': 'J_MNRAS_451_1307_HIIreg',
 'name': 'J/MNRAS/451/1307/HIIreg',
 'description': 'Physical properties of the brightest HII regions in interacting galaxies (table A1) and isolated galaxies (table A2)'}
# print the table itself
cat_viz[0]
Table length=10
Seqn_NameNameNfRAJ2000DEJ2000logLHaRlogMHIIrhoHIIsigmaalpVirEWHalogAgeSimbad
log(1e-07W)pclog(solMass)solMass / pc3km / s0.1 nmlog(yr)
int16str3str7int16str10str11float32int16float32float32float32float32float32float32str6
1intNGC2146306 18 39.4+78 21 18.239.0401145.900.127015.040.0159.796.770Simbad
2intNGC2146306 18 38.1+78 21 26.838.8301165.800.097017.060.054.706.810Simbad
3intNGC2146306 18 44.6+78 21 50.638.5901425.800.055011.030.0125.306.780Simbad
4intNGC2146306 18 27.7+78 22 21.038.4701555.810.04108.620.0119.646.780Simbad
5intNGC2146306 18 35.0+78 21 28.038.4401245.650.056015.080.0103.356.790Simbad
6intNGC2146306 18 42.8+78 21 58.138.4101365.700.04704.0--155.866.770Simbad
7intNGC2146306 18 39.8+78 21 22.538.350815.300.096015.0100.0109.476.790Simbad
8intNGC2146306 18 48.0+78 21 43.338.3201445.690.03904.0--81.896.800Simbad
9intNGC2146306 18 42.1+78 21 12.038.310945.400.07304.0--103.956.790Simbad
10intNGC2146306 18 44.2+78 21 00.938.300965.400.07004.0--67.876.800Simbad

As for every function in python, we can call help(Vizier) in a code cell and run it to print the documentation.

We won’t do it here as it is quite long, but if we did, we could have read that columns should be a list, column_filters a dictionary, and row_limits is an int for which -1 means infinity. Now we can customize our VizieR query by selecting columns, filtering to keep only the interacting galaxies (n_Name='int'), and keeping only the first region for each galaxy (Seq=1, for visualization purposes).

cat_hii = Vizier(
    columns=["Name", "RAJ2000", "DEJ2000", "logLHa", "R", "sigma"],
    column_filters={"Seq": "==1", "n_Name": "int"},
    row_limit=-1,
).get_catalogs("2015MNRAS.451.1307Z")[0]
cat_hii
Table length=12
NameRAJ2000DEJ2000logLHaRsigma
log(1e-07W)pckm / s
str7str10str11float32int16float32
NGC214606 18 39.4+78 21 18.239.04011415.0
Arp24412 01 54.6-18 53 03.140.20028032.8
NGC52001 24 33.3+03 48 04.639.12025514.1
UGC399507 44 09.2+29 14 51.340.29027043.0
NGC378811 39 41.4+31 54 23.339.70028011.9
NGC378611 39 45.1+31 55 46.339.86030016.5
NGC278209 14 05.3+40 06 50.439.80016628.0
NGC299309 45 48.3-14 22 05.940.07025812.0
NGC299209 45 41.9-14 19 33.439.18010016.0
NGC399111 57 31.7+32 20 29.640.44047020.9
Arp27010 49 55.8+32 59 28.040.22022920.4
NGC376911 37 43.7+47 53 24.438.310664.0

We can also display the table on Aladin. We use aladin.add_table

aladin.add_table(cat_hii)

Go up to the Aladin Lite widget, zoom out to see all the sources, then rotate and zoom in to see the interacting galaxies. You can click on one source to display the information in the table.

Change the view to the AllWISE Infrared survey, either in the widget:

Base image layer -> The Wide-field Infrared Survey Explorer

or in the command line below.

You can find the list of available sky maps at https://aladin.u-strasbg.fr/hips/list

aladin.survey = "P/allWISE/color"

If you want to overlay the survey instead of replacing it:

aladin.overlay_survey= 'P/allWISE/color'

aladin.overlay_survey_opacity = 0.5

Exercise:

Retrieve the second table from Zaragoza-Cardiel+, 2015 (2015MNRAS.451.1307Z) containing data related to the image fits files and try to answer the questions:

  • What are the typical sizes of the fits images?

  • What is the approximate resolution of the images? [arcsec/pix]

# write your code here

Step #5: Cross-match tables#

Vizier Xmatch

We have HII data for a sample of interacting galaxies. Since we are also interested in the infrared fluxes we search for the Wide-field Infrared Survey Explorer (AllWISE) data release.

We want to match the sources in J/MNRAS/451/1307/HIIreg with the AllWISE source catalog that contains 747 million objects!

We use the X-Match python module for cross-identification of objects. It is particularly efficient and fast with very large catalogs, like AllWISE. All we need are the names of the catalogs, the names of the columns containing the coordinates of the sources, and the desired maximum distance for the match (optionally the area as well, otherwise all-sky is the default).

# What is the AllWISE catalogue reference in VizieR?
cat_allwise = Vizier.get_catalogs("AllWISE")
cat_allwise
TableList with 1 tables:
	'0:II/328/allwise' with 28 column(s) and 50 row(s) 
xmatch_zbf_wise = XMatch.query(
    cat1="vizier:J/MNRAS/451/1307/HIIreg",
    cat2="vizier:II/328/allwise",
    max_distance=10 * u.arcsec,
    colRA1="RAJ2000",
    colDec1="DEJ2000",
    colRA2="RAJ2000",
    colDec2="DEJ2000",
)
xmatch_zbf_wise
Table length=1314
angDist_RAJ2000_DEJ2000recnoSeqn_NameNameNfRAJ2000DEJ2000logLHaRlogMHIIrhoHIIsigmaalpVirEWHalogAgeSimbadAllWISERAJ20002DEJ20002eeMajeeMineePAW1magW2magW3magW4magJmagHmagKmage_W1mage_W2mage_W3mage_W4mage_Jmage_Hmage_KmagIDccfexvarqphpmRAe_pmRApmDEe_pmDEd2M
arcsecdegdeglog(1e-07W)pclog(solMass)solMass.pc**-3km / s0.1 nmlog(yr)degdegarcsecarcsecdegmagmagmagmagmagmagmagmagmagmagmagmagmagmag
float64float64float64int32int16str3str7int16str10str11float32int16float32float32float32float32float32float32objectstr19float64float64float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32float32int64str4str1str4str4int32int32int32int32float32
9.439952250.77536.839056182027isoNGC6207016 43 06.0+36 50 20.637.69664.90.064.0--134.266.78SimbadJ164305.83+365029.8250.774321736.84162140.03740.036967.012.8712.9188.295.588------0.0730.0250.0220.037------2499136301351044678000033000AAAA104035942--
6.754932250.76791736.834944182128isoNGC6207016 43 04.3+36 50 05.837.661015.10.0314.0--22.276.91SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458
8.575258250.76791736.834944182128isoNGC6207016 43 04.3+36 50 05.837.661015.10.0314.0--22.276.91SimbadJ164303.79+364959.7250.765819936.83325380.03790.0338158.610.95310.9367.7585.483------0.0230.0220.0240.066------2499136301351044647d0d053200AAAA1133148432--
5.99581250.7637536.833083182229isoNGC6207016 43 03.3+36 49 59.137.66875.00.0394.0--20.66.93SimbadJ164303.79+364959.7250.765819936.83325380.03790.0338158.610.95310.9367.7585.483------0.0230.0220.0240.066------2499136301351044647d0d053200AAAA1133148432--
7.312318250.7637536.833083182229isoNGC6207016 43 03.3+36 49 59.137.66875.00.0394.0--20.66.93SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458
3.802161250.76666736.832444182330isoNGC6207016 43 04.0+36 49 56.837.66915.10.0364.0--8.97.09SimbadJ164303.79+364959.7250.765819936.83325380.03790.0338158.610.95310.9367.7585.483------0.0230.0220.0240.066------2499136301351044647d0d053200AAAA1133148432--
7.648264250.76666736.832444182330isoNGC6207016 43 04.0+36 49 56.837.66915.10.0364.0--8.97.09SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458
9.762527250.76916736.832833182534isoNGC6207016 43 04.6+36 49 58.237.49574.70.064.0--18.266.98SimbadJ164303.79+364959.7250.765819936.83325380.03790.0338158.610.95310.9367.7585.483------0.0230.0220.0240.066------2499136301351044647d0d053200AAAA1133148432--
4.218263250.77536.840583183041isoNGC6207016 43 06.0+36 50 26.137.26334.20.14.0--255.836.75SimbadJ164305.83+365029.8250.774321736.84162140.03740.036967.012.8712.9188.295.588------0.0730.0250.0220.037------2499136301351044678000033000AAAA104035942--
0.81480340.898751.37722218341isoNGC1073002 43 35.7+01 22 38.039.221246.040.13813.423.0522.676.59SimbadJ024335.67+012237.240.89865111.37701840.08070.0757126.314.86814.66410.0417.24------0.0330.0650.0660.129------40810150135102866000003nnnnAAAB0000--
9.55644140.898751.37722218341isoNGC1073002 43 35.7+01 22 38.039.221246.040.13813.423.0522.676.59SimbadJ024335.06+012236.840.89611451.37689840.07770.0726113.514.66514.54710.1167.751------0.0320.0590.070.201------40810150135102865900003nnnnAAAB519113184115--
2.05037840.916251.39805618352isoNGC1073002 43 39.9+01 23 53.038.87875.640.169.923.0641.026.54SimbadJ024339.95+012351.140.91646431.39752830.08910.0842142.715.05815.1110.0217.007------0.0360.1010.0620.092------408101501351028680000021111AAAA342161-432166--
6.77897840.9170831.37544418363isoNGC1073002 43 40.1+01 22 31.638.691155.730.0847.213.063.086.8SimbadJ024340.51+012234.140.91883311.37614020.04220.040884.412.25212.2598.9776.37615.10114.57314.2920.0250.0250.0290.0630.160.2410.16408101501351028598000051100AAAA-1534862470.394
...................................................................................................................................................
2.461872262.36458375.690333178975isoNGC6412017 29 27.5+75 41 25.237.541005.10.0264.0--1.637.24SimbadJ172927.37+754122.7262.364050775.68966190.04030.037477.512.0512.09712.9179.36912.5212.17712.1070.0230.021----0.0220.0190.02526131757013510282120000211nnAAUU-483426340.157
6.919677262.36458375.690333178975isoNGC6412017 29 27.5+75 41 25.237.541005.10.0264.0--1.637.24SimbadJ172925.67+754123.7262.356990375.68991730.32680.1897101.416.10416.51812.6199.184------0.1260.248----------2613175701351029820000023nnnBBUU-253460362559--
9.385763262.39916775.702444179177isoNGC6412017 29 35.8+75 42 08.837.471005.00.0274.0--10.67.06SimbadJ172937.39+754216.1262.40579375.70447380.03360.03378.611.65611.6368.1195.81514.42413.63913.5060.0220.0220.0190.0370.0820.0940.0862613175701351028211000054200AAAA0300300.861
2.430664262.3637575.690333179278isoNGC6412017 29 27.3+75 41 25.237.3804.80.034.0--1.437.25SimbadJ172927.37+754122.7262.364050775.68966190.04030.037477.512.0512.09712.9179.36912.5212.17712.1070.0230.021----0.0220.0190.02526131757013510282120000211nnAAUU-483426340.157
6.198088262.3637575.690333179278isoNGC6412017 29 27.3+75 41 25.237.3804.80.034.0--1.437.25SimbadJ172925.67+754123.7262.356990375.68991730.32680.1897101.416.10416.51812.6199.184------0.1260.248----------2613175701351029820000023nnnBBUU-253460362559--
2.619531262.36416775.690389179480isoNGC6412017 29 27.4+75 41 25.436.9604.40.034.0--3.337.12SimbadJ172927.37+754122.7262.364050775.68966190.04030.037477.512.0512.09712.9179.36912.5212.17712.1070.0230.021----0.0220.0190.02526131757013510282120000211nnAAUU-483426340.157
6.607669262.36416775.690389179480isoNGC6412017 29 27.4+75 41 25.436.9604.40.034.0--3.337.12SimbadJ172925.67+754123.7262.356990375.68991730.32680.1897101.416.10416.51812.6199.184------0.1260.248----------2613175701351029820000023nnnBBUU-253460362559--
5.639364250.7637536.83472217951isoNGC6207016 43 03.3+36 50 05.039.511546.330.1418.027.068.46.8SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458
7.969164250.7637536.83472217951isoNGC6207016 43 03.3+36 50 05.039.511546.330.1418.027.068.46.8SimbadJ164303.79+364959.7250.765819936.83325380.03790.0338158.610.95310.9367.7585.483------0.0230.0220.0240.066------2499136301351044647d0d053200AAAA1133148432--
7.499991250.76458336.83630617962isoNGC6207016 43 03.5+36 50 10.739.361316.150.14813.921.0138.946.78SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458
3.3028250.77536.84236117973isoNGC6207016 43 06.0+36 50 32.538.881385.940.0796.07.0264.236.75SimbadJ164305.83+365029.8250.774321736.84162140.03740.036967.012.8712.9188.295.588------0.0730.0250.0220.037------2499136301351044678000033000AAAA104035942--
2.552215250.77458336.840944180612isoNGC6207016 43 05.9+36 50 27.438.26725.20.14.0--264.146.75SimbadJ164305.83+365029.8250.774321736.84162140.03740.036967.012.8712.9188.295.588------0.0730.0250.0220.037------2499136301351044678000033000AAAA104035942--
7.317245250.76458336.83625181724isoNGC6207016 43 03.5+36 50 10.537.73724.90.0564.0--200.196.76SimbadJ164303.76+365003.8250.765668636.83441250.02120.0201135.010.79211.247.0284.23212.86612.60312.5230.030.0190.0130.0210.0430.0560.0582499136301351044679hd003n9n3AAAA-92925-1205260.458

This is the end of the tutorial.

Try to apply what you have learned here to your data sets and science cases!