How do we create catalogs?¶

We're often both busy and sleepy while observing, so entering details of individual targets one-by-one is an easy way for mistakes to happen. To remedy, this we can make target catalogs, where we've done the hard work of getting precise coordinates ahead of time, and can just select a target at the click of a button during the actual night!

In [1]:

from kosmoscraftroom.catalogs import *

from kosmoscraftroom.catalogs import *

Make a catalog from a table of coordinates.¶

We can make a catalog out of an astropy Table, as long as it a columns of names and one with SkyCoord objects for the coordinates.

In [2]:

names = ["A", "B", "C"]
coordinates = SkyCoord(ra=[1, 2, 3] * u.hourangle, dec=[-1, 0, 1] * u.deg)
table = Table(dict(names=names, sky_coordinates=coordinates))
table

names = ["A", "B", "C"] coordinates = SkyCoord(ra=[1, 2, 3] * u.hourangle, dec=[-1, 0, 1] * u.deg) table = Table(dict(names=names, sky_coordinates=coordinates)) table

Out[2]:

Table length=3

names	sky_coordinates
	deg,deg
str1	SkyCoord
A	14.999999999999998,-1.0
B	29.999999999999996,0.0
C	44.99999999999999,1.0

In [3]:

tiny = TUICatalog("random-tiny-test")
tiny.from_table(table)

tiny = TUICatalog("random-tiny-test") tiny.from_table(table)

In [4]:

tiny.table

tiny.table

Out[4]:

Table length=3

names	sky_coordinates
	deg,deg
str1	SkyCoord
A	14.999999999999998,-1.0
B	29.999999999999996,0.0
C	44.99999999999999,1.0

Make a catalog from a previous TUI catalog.¶

There are some existing TUI catalogs floating around. We can load one into a TUI Catalog object, which might then be written out again or combined with other catalogs.

In [5]:

standards = TUICatalog(name="flux-calibrators")
standards.from_TUI("standards.tui")

standards = TUICatalog(name="flux-calibrators") standards.from_TUI("standards.tui")

In [6]:

standards

standards

Out[6]:

<'flux-calibrators' TUICatalog (67 targets)>

In [7]:

standards.table

standards.table

Out[7]:

Table length=67

names	sky_coordinates	category
	deg,deg
str11	SkyCoord	str16
G158-100	8.476333333333333,-12.132527777777778	flux-calibrators
HR153	9.242916666666664,53.89691666666667	flux-calibrators
LTT377	10.445083333333333,-33.652277777777776	flux-calibrators
BPM16274	12.51325,-52.13816666666666	flux-calibrators
LTT1020	28.706999999999997,-27.474916666666665	flux-calibrators
HR718	37.03975,8.460055555555554	flux-calibrators
EG21	47.62908333333333,-68.6006111111111	flux-calibrators
LTT1788	57.09237499999999,-39.14266666666666	flux-calibrators
GD50	57.20858333333332,-0.9751111111111112	flux-calibrators
...	...	...
BD+28d4211	327.79612499999996,28.86438888888889	flux-calibrators
G93-48	328.1055416666667,2.390083333333333	flux-calibrators
BD+25d4655	329.9250833333333,26.432805555555557	flux-calibrators
NGC7293	337.41024999999996,-20.837027777777777	flux-calibrators
HR8634	340.36516666666665,10.831444444444443	flux-calibrators
LTT9239	343.1703333333333,-20.590638888888886	flux-calibrators
LTT9491	349.89574999999996,-17.09161111111111	flux-calibrators
Feige110	349.9932916666666,-5.165500000000001	flux-calibrators
GD248	351.527875,16.005944444444445	flux-calibrators

Make a catalog from an exoatlas population.¶

We can convert an exoatlas population, filtered down however we like, into a TUI catalog.

In [8]:

from exoatlas import *

# define some populations
e = TransitingExoplanets()
e.update_values("SPECULOOS-3b", distance=24.754 * u.pc)

from exoatlas import * # define some populations e = TransitingExoplanets() e.update_values("SPECULOOS-3b", distance=24.754 * u.pc)

['speculoos3b'] | distance: nan pc > 24.754 pc

/Users/zach/Dropbox/zach/code/exoatlas/exoatlas/populations/population_core.py:822: UserWarning: 'distance' should probably have some uncertainties, which you didn't provide.
  warnings.warn(

In [9]:

nearby = e[e.distance < 30 * u.pc]
emission = e[e.emission_snr() > 20]
reflection = e[e.reflection_snr() > 5]

nearby = e[e.distance < 30 * u.pc] emission = e[e.emission_snr() > 20] reflection = e[e.reflection_snr() > 5]

In [10]:

n = TUICatalog("nearby")
n.from_exoatlas(nearby)

e = TUICatalog("emission")
e.from_exoatlas(emission)

r = TUICatalog("reflection")
r.from_exoatlas(reflection)

n = TUICatalog("nearby") n.from_exoatlas(nearby) e = TUICatalog("emission") e.from_exoatlas(emission) r = TUICatalog("reflection") r.from_exoatlas(reflection)

In [11]:

n, e, r

n, e, r

Out[11]:

(<'nearby' TUICatalog (68 targets)>,
 <'emission' TUICatalog (168 targets)>,
 <'reflection' TUICatalog (12 targets)>)

In [12]:

n.table

n.table

Out[12]:

Table length=68

names	sky_coordinates	G	distance	category
	deg,deg	mag	pc
str27	SkyCoord	float64	float64	str6
TOI-198	2.2715087,-27.1217452	10.9187	23.7262	nearby
Gliese 12	3.9579137,13.5576175	11.3963	12.21	nearby
TOI-260	4.7730178,-9.9661505	9.3124	20.1852	nearby
TOI-244	10.5697537,-36.7179743	11.553	22.0337	nearby
LHS 1140	11.248632,-15.2741085	12.6706	14.9861	nearby
TOI-1468	16.6538724,19.2249195	12.1172	24.7399	nearby
TOI-4527	19.4722574,5.4711996	10.1927	18.1037	nearby
GJ 3090	20.4384281,-46.7147234	10.5521	22.4751	nearby
TOI-2443	40.1798608,1.1996757	9.0333	23.9258	nearby
...	...	...	...	...
SPECULOOS-3	312.3632611,33.6123832	15.4023	24.754	nearby
HD 207897	325.2016451,84.3337613	8.13037	28.2938	nearby
HD 207496	328.7181708,-77.3388025	7.97255	23.606	nearby
TOI-6255	331.5031606,39.2988284	11.6345	20.3167	nearby
LHS 3844	340.4962038,-69.1721081	13.3925	14.8846	nearby
TRAPPIST-1	346.6263919,-5.0434618	15.6451	12.4298888	nearby
HD 219134	348.3372026,57.1696255	5.2402	6.53127	nearby
L 168-9	350.0285917,-60.0657292	10.2316	25.1496	nearby
GJ 9827	351.7717763,-1.2853435	9.83773	29.661	nearby

Write catalog files for TUI and humans.¶

Let' s make a different TUI catalog for each of our sub-samples. By loading them separately into TUI, they can have different colors.

In [13]:

for x, c in zip([n, e, r, standards], ["red", "orange", "yellow", "black"]):
    x.to_TUI(color=c)

for x, c in zip([n, e, r, standards], ["red", "orange", "yellow", "black"]): x.to_TUI(color=c)

<'nearby' TUICatalog (68 targets)> has been saved to nearby.tui
<'emission' TUICatalog (168 targets)> has been saved to emission.tui
<'reflection' TUICatalog (12 targets)> has been saved to reflection.tui
<'flux-calibrators' TUICatalog (67 targets)> has been saved to flux-calibrators.tui

In [14]:

!head nearby.tui

!head nearby.tui

CSys=ICRS; RotType=Horizon; RotAng=90; DispColor=red


TOI-198              00:09:05.2 -27:07:18.3   
Gliese12             00:15:49.9 +13:33:27.4   
TOI-260              00:19:05.5 -09:57:58.1   
TOI-244              00:42:16.7 -36:43:04.7   
LHS1140              00:44:59.7 -15:16:26.8   
TOI-1468             01:06:36.9 +19:13:29.7   
TOI-4527             01:17:53.3 +05:28:16.3   

Now, let's make a human-friendly catalog that smooshes all these entries together and sorted by RA. Because the human friendly catalog will have the names of the original catalogs included as extra columns, we'll be able to figure where we should look in TUI to select each one.

In [15]:

combined = n + e + r + standards
combined.to_human_friendly()

combined = n + e + r + standards combined.to_human_friendly()

Human-friendly catalog (315 targets) has been saved to nearby+emission+reflection+flux-calibrators.txt

/Users/zach/Dropbox/zach/code/kosmos-craftroom/kosmoscraftroom/catalogs.py:83: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior
  f"d={row['distance']:.1f}pc",
/Users/zach/Dropbox/zach/code/kosmos-craftroom/kosmoscraftroom/catalogs.py:84: FutureWarning: Format strings passed to MaskedConstant are ignored, but in future may error or produce different behavior
  f"G={row['G']:.2f}",

In [16]:

!head nearby+emission+reflection+flux-calibrators.txt

!head nearby+emission+reflection+flux-calibrators.txt

HIP65A               00:00:44.5 -54:49:51.0   emission, d=61.8pc, G=10.59
HIP65A               00:00:44.5 -54:49:51.0   reflection, d=61.8pc, G=10.59
TOI-198              00:09:05.2 -27:07:18.3   nearby, d=23.7pc, G=10.92
Gliese12             00:15:49.9 +13:33:27.4   nearby, d=12.2pc, G=11.40
WASP-26              00:18:24.7 -15:16:02.7   emission, d=252.8pc, G=10.99
TOI-260              00:19:05.5 -09:57:58.1   nearby, d=20.2pc, G=9.31
WASP-1               00:20:40.1 +31:59:23.9   emission, d=393.1pc, G=11.53
HD2685               00:29:18.9 -76:18:14.5   emission, d=196.9pc, G=9.52
G158-100             00:33:54.3 -12:07:57.1   flux-calibrators, d=--pc, G=--
HR153                00:36:58.3 +53:53:48.9   flux-calibrators, d=--pc, G=--

Now, we can load the .tui catalogs into TUI, print out the .txt file (with a monospace font), and have a great night observing!