Creating¶

The primary goal of exoatlas is to create populations of exoplanets which which we can interact in fairly standardized way. Here we summarize a few different pre-defined Population objects we can create. From all these basic populations, you can make your own to play with by indexing, slicing, or masking as described in Populations.

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]:

'0.7.0'

Throughout this page we'll use a handy PlanetGallery() gallery visualization to see some basic properties of a particular population. For more on useful plots and plotting tools, see Visualizing.

PredefinedPopulation = commonly useful archival datasets¶

The atlas contains a few predefined populations, for which data are automatically downloaded from archives and standardized into tables in exoatlas's preferred format. This downloading and standardizing can take a while, so the results will be stored locally, in a directory you can locate with:

In [2]:

ea.locate_local_data()

ea.locate_local_data()

💾 `exoatlas` archive data will be stored in:
/Users/zabe0091/exoatlas-data

By default, downloaded data will be stored inside your current working directory. If you'll be using exoatlas from lots of different directories on your computer, and you don't want to keep downloading fresh copies of the archive into each working directory, you can define one path that should be used for exoatlas data for your entire computer by setting a global EXOATLAS_DATA environment. For example, on a mid-2020s Mac, this looks like editing the shell configuration file ~/.zshrc and adding a line like export EXOATLAS_DATA="/Users/zabe0091/exoatlas-data".

If you ever want to reset the local data, you do so with:

• ea.reset_local_data() to remove all local files, including raw downloads
• ea.reset_standardized_data() to remove standardized files but keep raw downloads

If you have recently updated your local version of exoatlas and run into problems with data acting weird, you may want to consider doing a data reset.

SolarSystem = major Solar System planets¶

The eight planets in our own Solar System are useful as a comparison sample. Their properties are built-in with exoatlas, so they don't need to be downloaded from any archive.

In [3]:

solar = ea.SolarSystem()
solar

solar = ea.SolarSystem() solar

Out[3]:

✨ Solar System | 8 elements ✨

In [4]:

vi.PlanetGallery().build(solar);

vi.PlanetGallery().build(solar);

Likewise, we have the dwarf planets, smaller minor planets, and moons:

In [5]:

dwarfs = ea.SolarSystemDwarfPlanets()
dwarfs

dwarfs = ea.SolarSystemDwarfPlanets() dwarfs

Out[5]:

✨ Solar System Dwarf Planets | 5 elements ✨

In [6]:

minor = ea.SolarSystemMinorPlanets()
minor

minor = ea.SolarSystemMinorPlanets() minor

Out[6]:

✨ Solar System Minor Planets | 10183 elements ✨

In [7]:

moons = ea.SolarSystemMoons()
moons

moons = ea.SolarSystemMoons() moons

Out[7]:

✨ Solar System Moons | 41 elements ✨

Exoplanets = confirmed, published, exoplanets¶

The NASA Exoplanet Archive maintains a list of confirmed transiting exoplanets. These are systems that have been published in the peer-reviewed literature. Most of them have been characterized in sufficient detail that they are unlikely to be false positives. This archive contains planets discovered through a variety of methods. We can access all exoplanets in the archive, regardless of discovery method, with:

In [8]:

exoplanets = ea.Exoplanets()
exoplanets

exoplanets = ea.Exoplanets() exoplanets

Out[8]:

✨ Exoplanets | 5926 elements ✨

For many purposes, it will be really handy to have just those exoplanets that transit their stars. To access those:

In [9]:

transiting = ea.TransitingExoplanets()
transiting

transiting = ea.TransitingExoplanets() transiting

Out[9]:

✨ Transiting Exoplanets | 4426 elements ✨

In [10]:

vi.PlanetGallery().build([transiting, solar]);

vi.PlanetGallery().build([transiting, solar]);

Subsets¶

Some common subsets have been defined as their own Population objects. To make your own subsets, see Populations for how to filter a sample of planets down according to its properties.

Which planets were discovered by the NASA Kepler mission or not?

In [11]:

kepler = ea.Kepler()
kepler

kepler = ea.Kepler() kepler

Out[11]:

✨ Kepler | 3307 elements ✨

In [12]:

nonkepler = ea.NonKepler()
nonkepler

nonkepler = ea.NonKepler() nonkepler

Out[12]:

✨ Non-Kepler | 1119 elements ✨

In [13]:

vi.PlanetGallery().build([kepler, nonkepler, solar]);

vi.PlanetGallery().build([kepler, nonkepler, solar]);

Which planets were discovered by the NASA TESS mission or not?

In [14]:

tess = ea.TESS()
tess

tess = ea.TESS() tess

Out[14]:

✨ TESS | 635 elements ✨

In [15]:

nontess = ea.NonTESS()
nontess

nontess = ea.NonTESS() nontess

Out[15]:

✨ NonTESS | 3791 elements ✨

In [16]:

vi.PlanetGallery().build([nontess, tess, solar]);

vi.PlanetGallery().build([nontess, tess, solar]);

Which planets were discovered from telescopes in space or telescopes on the ground?

In [17]:

space = ea.Space()
space

space = ea.Space() space

Out[17]:

✨ Space-based | 3978 elements ✨

In [18]:

ground = ea.Ground()
ground

ground = ea.Ground() ground

Out[18]:

✨ Ground-based | 448 elements ✨

In [19]:

vi.PlanetGallery().build([space, ground, solar]);

vi.PlanetGallery().build([space, ground, solar]);

Good vs Bad Mass?¶

Which planets have masses that have been detected at better than $5\sigma$?

In [20]:

good = ea.GoodMass(sigma=5)
good

good = ea.GoodMass(sigma=5) good

Out[20]:

✨ Good Mass | 876 elements ✨

In [21]:

bad = ea.BadMass(sigma=5)
bad

bad = ea.BadMass(sigma=5) bad

Out[21]:

✨ Bad Mass | 3551 elements ✨

In [22]:

vi.PlanetGallery().build([bad, good]);

vi.PlanetGallery().build([bad, good]);

Collections¶

Because we often might want to load up a bunch of population subsets at once, we provide built-in functions that create dictionaries of planets grouped by particular properties. For example, let's see exoplanets grouped by their original discovery method:

In [23]:

methods = ea.get_exoplanets_by_method()
methods

methods = ea.get_exoplanets_by_method() methods

Out[23]:

{np.str_('Astrometry'): ✨ Astrometry | 5 elements ✨,
 np.str_('Disk Kinematics'): ✨ Disk Kinematics | 1 elements ✨,
 np.str_('Eclipse Timing Variations'): ✨ Eclipse Timing Variations | 17 elements ✨,
 np.str_('Imaging'): ✨ Imaging | 83 elements ✨,
 np.str_('Microlensing'): ✨ Microlensing | 247 elements ✨,
 np.str_('Orbital Brightness Modulation'): ✨ Orbital Brightness Modulation | 9 elements ✨,
 np.str_('Pulsar Timing'): ✨ Pulsar Timing | 8 elements ✨,
 np.str_('Pulsation Timing Variations'): ✨ Pulsation Timing Variations | 2 elements ✨,
 np.str_('Radial Velocity'): ✨ Radial Velocity | 1128 elements ✨,
 np.str_('Transit'): ✨ Transit | 4389 elements ✨,
 np.str_('Transit Timing Variations'): ✨ Transit Timing Variations | 37 elements ✨,
 'Solar System': ✨ Solar System | 8 elements ✨}

In [24]:

vi.PlanetGallery().build(methods);

vi.PlanetGallery().build(methods);

In [25]:

teffs = ea.get_exoplanets_by_teff()
teffs

teffs = ea.get_exoplanets_by_teff() teffs

Out[25]:

{'O': ✨ O | 2 elements ✨,
 'B': ✨ B | 12 elements ✨,
 'A': ✨ A | 28 elements ✨,
 'F': ✨ F | 1130 elements ✨,
 'G': ✨ G | 2145 elements ✨,
 'K': ✨ K | 1640 elements ✨,
 'M': ✨ M | 500 elements ✨,
 'T': ✨ T | 2 elements ✨,
 'Y': ✨ Y | 1 elements ✨}

In [26]:

vi.PlanetGallery().build(teffs);

vi.PlanetGallery().build(teffs);

All of these populations assume the values quoted in the archive are correct. If you need to update values, or choose values from different references, please see Curating. If you're confident your population is good enough for your needs, you can skip along ahead!