EnsembleLC

class elk.ensemble.EnsembleLC(radius, cluster_age=None, output_path='./', identifier=None, location=None, percentile=80, cutout_size=99, scattered_light_frequency=5, n_pca=6, spline_knots=5, verbose=False, just_one_lc=False, minimize_memory=False, ignore_previous_downloads=False, ignore_scattered_light=False, auto_confirm=False)

Bases: object

Class for generating light curves from TESS cutouts

Parameters:

radiusfloat

Radius of the cluster. If a float is given then unit is assumed to be degrees. Otherwise, I’ll convert your unit to what I need.

cluster_agefloat, optional

Age of the cluster. If a float is given then unit is assumed to be dex. Otherwise, I’ll convert your unit to what I need.

output_pathstr, optional

Path to a folder in which to save outputs - must have subfolders Corrected_LCs/ and Figures/LCs/, by default “./”

identifierstr, optional

The name to call your object (i.e Cluster Name), by default None; Additionally can be used to query FFI is location is None.

locationstr, optional

Location of the object -must be in ICRS, by default None

percentileint, optional

Which percentile to use in the upper limit calculation, by default 80

cutout_sizeint, optional

How large to make the cutout, by default 99

scattered_light_frequencyint, optional

Frequency at which to check for scattered light, by default 5

n_pcaint, optional

Number of principle components to use in the DesignMatrix, by default 6

spline_knots: `int`, optional

Number of knots to include in the spline corrector, by default 5. If None, spline corrector will not be used

verbosebool, optional

Whether to print out information and progress bars, by default False

just_one_lcbool, optional

Whether to return after the first light curve that passes the quality tests, by default False

minimize_memorybool, optional

Minimize the use of memory of this class: This will cause it to (1) skip using the LightKurve cache and scrub downloads instead and (2) save light curves into files one by one and then remove them from memory, by default False

ignore_previous_downloadsbool, optional

Whether to ignore previously downloaded and corrected light curves

ignore_scattered_lightbool, optional

Whether to ignore the scattered light test, by default False

auto_confirmbool, optional

Whether to automatically confirm any message that you’d usually ask the user, by default False

Methods Summary

clear_cache()	Clear the folder containing manually cached lightkurve files
create_output_table()	Generate lightcurve output summary table for the cluster and save in self.output_path
downloadable(ind)
get_lcs()	Get light curves for each of the observations of the cluster.
has_tess_data()	Check whether TESS has data on the cluster
previously_downloaded()	Check whether the files have previously been downloaded for this cluster
scattered_light(quality_tpfs, ...)
summary_table()	Summarize ensemble light curve in an Astropy Table

Methods Documentation

clear_cache()

Clear the folder containing manually cached lightkurve files

create_output_table()

Generate lightcurve output summary table for the cluster and save in self.output_path

If the self.get_lcs() pipeline has not been run (i.e. self.lcs is empty and there is data), this will also automatically run the whole pipeline.

downloadable(ind)

get_lcs()

Get light curves for each of the observations of the cluster.

self.lcs contains the corrected light curves after the function completes.

has_tess_data()

Check whether TESS has data on the cluster

Returns:

has_databool

Whether these is at least one observation in TESS

previously_downloaded()

Check whether the files have previously been downloaded for this cluster

Returns:

existsbool

Whether the file exists

scattered_light(quality_tpfs, full_model_Normalized)

summary_table()

Summarize ensemble light curve in an Astropy Table

Returns:

output_tableTable

The full lightcurves output table that was saved