Quickstart¶

Let's take a brief whirlwind tour of some of the basic tools in exoatlas. Most of the concepts seen here are decribed in more detail later, but hopefully this is enough for you to get started!

In [1]:

import exoatlas as ea
import exoatlas.visualizations as vi
import astropy.units as u

ea.version()

import exoatlas as ea import exoatlas.visualizations as vi import astropy.units as u ea.version()

Out[1]:

'0.6.6'

🌌 Make Populations of Planets¶

exoatlas can create objects containing properties for large samples of planets. Solar System planets come from JPL Solar System Dynamics, and exoplanets come from the NASA Exoplanet Archive.

In [2]:

solar = ea.SolarSystem()
solar

solar = ea.SolarSystem() solar

Saved a standardized text table to /Users/zabe0091/Dropbox/zach/code/exoatlas/docs/downloads-for-exoatlas/data/standardized-SolarSystem.txt

Out[2]:

✨ Solar System | 8 elements ✨

In [3]:

exoplanets = ea.Exoplanets()
exoplanets

exoplanets = ea.Exoplanets() exoplanets

Out[3]:

✨ Exoplanets | 5856 elements ✨

🧮 Extract Quantities and Uncertainties¶

For a population, exoatlas can provide easy access to archival table columns and their uncertainties. All quantities have astropy.units attached to them, to minimize confusion about unit conversion.

In [4]:

exoplanets.mass()

exoplanets.mass()

Out[4]:

$[4914.8985,~4684.8142,~1131.1513,~\dots,~3257.7412,~162.09249,~616.90494] \; \mathrm{M_{\oplus}}$

In [5]:

exoplanets.mass_uncertainty()

exoplanets.mass_uncertainty()

Out[5]:

$[38.665583,~852.45738,~34.397187,~\dots,~607.1272,~48.554444,~41.628932] \; \mathrm{M_{\oplus}}$

exoatlas can also calculate derived quantities and use astropy.uncertainty to propagate uncertainties for those derived quantities.

In [6]:

exoplanets.insolation()

exoplanets.insolation()

Out[6]:

$[93243.949,~156505.06,~156786.2,~\dots,~721.81522,~61680.406,~203775.14] \; \mathrm{\frac{W}{m^{2}}}$

In [7]:

exoplanets.insolation_uncertainty()

exoplanets.insolation_uncertainty()

Out[7]:

$[43707.948,~15388.588,~30593.499,~\dots,~24.242908,~47499.135,~33375.179] \; \mathrm{\frac{W}{m^{2}}}$

In [8]:

exoplanets.insolation_uncertainty() / exoplanets.insolation()

exoplanets.insolation_uncertainty() / exoplanets.insolation()

Out[8]:

$[{\rm NaN},~0.091129725,~0.19935903,~\dots,~0.036544067,~0.73106538,~0.18296476] \; \mathrm{}$

⚗️ Filter Populations by Properties¶

exoatlas can extract subsets of populations, selecting planets just in a specific range of interest for particular properties. These subsets have all the same powers as their parent populations. By setting attributes for these subset populations, we can affect how they'll be visualized.

In [9]:

nearby = exoplanets[exoplanets.distance() < 30 * u.pc]
nearby.label = "Nearby"
nearby.color = "coral"
nearby.marker = "P"
nearby.size = 20
nearby

nearby = exoplanets[exoplanets.distance() < 30 * u.pc] nearby.label = "Nearby" nearby.color = "coral" nearby.marker = "P" nearby.size = 20 nearby

Out[9]:

✨ Nearby | 486 elements ✨

In [10]:

easy = exoplanets[
    exoplanets.transmission_snr(telescope="JWST", wavelength=4 * u.micron) > 10
]
easy.label = "Easy"
easy.color = "orchid"
easy.marker = "*"
easy.outlined = True
easy.filled = False
easy.size = 100
easy.annotate_planets = True
easy

easy = exoplanets[ exoplanets.transmission_snr(telescope="JWST", wavelength=4 * u.micron) > 10 ] easy.label = "Easy" easy.color = "orchid" easy.marker = "*" easy.outlined = True easy.filled = False easy.size = 100 easy.annotate_planets = True easy

Out[10]:

✨ Easy | 9 elements ✨

In [11]:

first = exoplanets["HD209458b"]
first.size = 500
first.color = "magenta"
first

first = exoplanets["HD209458b"] first.size = 500 first.color = "magenta" first

Out[11]:

✨ HD209458b | 1 elements ✨

🎨 Visualize Populations Together¶

exoatlas provides some built-in visualizations, as well as a framework for constructing new plots that can easily compare multiple populations to each other.

In [12]:

ps = vi.PlanetGallery()
ps.build([solar, exoplanets, nearby, easy, first])

ps = vi.PlanetGallery() ps.build([solar, exoplanets, nearby, easy, first])

Gosh, what a complicated plot! But at least it demonstrates lots of ways of visualizing populations!

📓 Create Tables¶

exoatlas can save populations out into tables, for whatever other purposes you want!

In [13]:

easy.create_planning_table()

easy.create_planning_table()

Out[13]:

QTable length=9

name	ra	dec	period	transit_midpoint	transit_duration	radius	relative_insolation	stellar_radius	stellar_teff	distance
	deg	deg	d	d	h	earthRad		solRad	K	pc
str29	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64
GJ 436 b	175.5505363	26.7030669	2.64388312	2454510.80162	1.00536	4.17	30.70534787219414	0.46	3586.11	9.75321
HD 189733 b	300.1821223	22.7097759	2.21857567	2453955.525551	1.8233621	12.666	354.831406428542	0.75	5052.0	19.7638
HD 209458 b	330.7950219	18.8842419	3.52474859	2451659.93742	3.072	15.581	770.0370177658837	1.18	6091.0	48.3016
HIP 65 A b	0.1856063	-54.8308228	0.9809734	2458326.10418	0.78576	22.754	660.9743411240239	0.72	4590.0	61.7856
KELT-11 b	161.7069053	-9.3993646	4.7361	2457483.431	7.1376	15.132	1429.4314669204682	2.69	5375.0	99.1596
TOI-1420 b	322.9418092	66.3490054	6.9561063	2459517.43305	3.372	11.89	139.7923167957046	0.92	5510.0	201.918
WASP-107 b	188.3864277	-10.1462141	nan	2457584.329746	2.73864	10.536	51.082791389699636	0.66	4425.0	64.7414
WASP-127 b	160.5587646	-3.8349989	4.17806203	2456776.62124	4.35288	14.695	773.2310346883762	1.33	5620.0	159.507
WASP-69 b	315.0259661	-5.094857	3.86814	2459798.775459	2.1610195	12.442	166.25045130539405	0.86	4700.0	49.9605

🎉 and more...¶

Please explore the rest of the documentation to learn if/how you can use exoatlas to help with your research or teaching or learning!