Sometimes, you might have good reason to want to replace some of the values in a Population with better ones. Maybe you prefer one reference over another, maybe you have some unpublished measurements you want to include, or maybe you just want to experiment with changing some values. This page mostly discusses curating the data inside an Exoplanet population, but some of the methods might generally apply to other populations.
import exoatlas as ea
ea.version()'0.7.8'Changes are Temporary¶
Please note, the changes made with update_reference or update_values will take place only within the current Python session. The underlying standardized data file is unchanged. The bottom of this page shows instructions for saving and loading a curated population.
Using Different References in an Exoplanet Population¶
The main data in the Exoplanets population come from the NASA Exoplanet Archive Planetary Systems Composite Parameters table. There is one entry in this population for each planet in the archive.
e = ea.Exoplanets()
e✨ Exoplanets | 6128 elements ✨If we want to see all individual references for each planet, which is a much larger table containing many more rows than there are planets, we’ll need to load a .individual_references population. It might take a while.
e.load_individual_references()(loading individual references may take up to a few minutes)
✨ Individual References | 39443 elements ✨Once that’s loaded, there’s a secret sneaky internal population that we can access through the .individual_references attribute, containing every reference for every planet in the archive.
e.individual_references✨ Individual References | 39443 elements ✨Now, let’s say we want to update what reference is being used to provide the period (and related) values for a particular planet. First, we can check what the options are with .display_individual_references.
e.display_individual_references(planets="HD189733b", keys="period")Then, we can update the population to use one of those options instead of the default.
e.update_reference(planets="HD189733b", references="Ivshina + Winn 2022")Finally, we can confirm that our change took effect, by checking the references again.
e.display_individual_references(planets="HD189733b", keys="period")Updating Values in an Exoplanet Population¶
If we have some custom values we’d like to apply to a planet, we can update its data with the .update_value wrapper.
import astropy.units as u
e.update_values(
planets="HD189733b", radius=1 * u.R_jupiter, radius_uncertainty=0.1 * u.R_jupiter
)['hd189733b'] | radius: [12.66617] earthRad > 1.0 jupiterRad
['hd189733b'] | radius_uncertainty_lower: [-0.11209] earthRad > -0.1 jupiterRad
['hd189733b'] | radius_uncertainty_upper: [0.11209] earthRad > 0.1 jupiterRad
e["HD189733"].radius()Curating Your Own Exoplanet Population¶
You might want to be able to regularly curate many changes to the default exoplanet parameters from the NASA Exoplanet Archive, and also be able to reapply those changes even when downloading newly updated archive data. In general, that might look like the following:
Write a function called something like
curate_population(), that makes the changes you want by serialling combining.update_referenceand.update_values. You might consider saving this function in a local module and importing it whenever you need it.Generate an
Exoplanetspopulation (or some subset of it), and apply your function to it. This function will change the population in-place; if you want to access the unmodified parameters, you’ll need to create a new population.If you want to save and reuse your curation, use the
population_to_save.save(filename)method to save your curated population out to a local file andloaded_population = Population(filename)to load it back in.
import exoatlas as eadef curate_population(x):
"""
Curate the values in an exoplanet population by
making small changes to the values being used.
Parameters
----------
x : exoatlas.populations.Exoplanets
An exoplanet population that needs to be curated.
Returns
-------
Nothing, but the population `x` has been modified
in place with updated values and/or reference choices.
"""
# (down)load the individual references
x.load_individual_references()
# update another value
x.update_values(
planets="HD189733b",
radius=1 * u.R_jupiter,
radius_uncertainty=0.1 * u.R_jupiter,
)e = ea.TransitingExoplanets()
e = e[e.distance() < 50 * u.pc]
curate_population(e)(loading individual references may take up to a few minutes)
['hd189733b'] | radius: [12.66617] earthRad > 1.0 jupiterRad
['hd189733b'] | radius_uncertainty_lower: [-0.11209] earthRad > -0.1 jupiterRad
['hd189733b'] | radius_uncertainty_upper: [0.11209] earthRad > 0.1 jupiterRad
e.save("curated-population.ecsv")
Saved ✨ Transiting Exoplanets | 250 elements ✨ to curated-population.ecsv.
It can be reloaded with `x = Population('curated-population.ecsv')`
curated_and_saved = ea.Population("curated-population.ecsv")
curated_and_saved["HD189733"].radius().to('Rjup')🔔 Problems: 🔔¶
Please note there are still a couple of problems that need solving:
A curated population that has been saved out to a file and then reloaded via
Population(filename)will (currently) always load in as aPopulationobject. It therefore won’t have any of the special powers of the speciality predefined populations likeExoplanets.Sometimes (?) trying to due curation with
update_referencegobbles up bonkers memory, presumably something to do with trying to index large tables many times. This might potentially be solvable with some more careful memory management or index-resetting? Curating mostly works, but sometimes it feels like there’s a monster lurking underneath.
If any of these problems are catastrophic for you, please open an issue and we’ll try to fix it!
Now that you are confident your population is carefully curated, you can zoom along to play with Filtering, Visualizing, or Observing!