BasicLightcurve#

class elk.lightcurve.BasicLightcurve(time=None, flux=None, flux_err=None, sector=None, fits_path=None, hdu_index=None, periodogram_freqs=array([0.04, 0.05, 0.06, ..., 10.97, 10.98, 10.99]))[source]#

Bases: object

A basic lightcurve class for calculating statistics of corrected lightcurves and plotting them.

This class can either be instantiated manually using time, flux and flux_err values, or loaded in from a fits file that has previously been created.

Parameters

timendarray, optional: Time of observations, by default None
fluxndarray, optional: Flux from each observations, by default None
flux_errndarray, optional: Errors on the flux, by default None
sectorint, optional: TESS sector in which observations were taken, by default None
fits_pathstr, optional: Path to a fits file containing the lightcurve, by default None
hdu_indexint, optional: Index of the HDU containing the lightcurve, by default None
periodogram_freqsnumpy.ndarray, optional: Frequencies at which to evaluate any periodograms, by default np.arange(0.04, 11, 0.01)

Attributes Summary

`MAD`	The median absolute deviation of the normalised flux of the lightcurve
`normalized_flux`	The normalised flux of the lightcurve
`rms`	The root-mean-squared normalised flux of the lightcurve
`sigmaG`	The sigmaG of the normalised flux of the lightcurve
`skewness`	The skewness of the flux of the lightcurve
`std`	The standard deviation of the normalised flux of the lightcurve
`von_neumann_ratio`	The Von Neumann ratio for the normalised flux of the lightcurve.

Methods Summary

`J_stetson`(**kwargs)	Calculate the J Stetson statistic for the lightcurve
`analysis_plot`([name, run_all, show])	Plot a 3-panel plot of the lightcurve for quick analysis
`get_stats_table`(name[, run_all])	Create an Astropy Table of the statistics (for aggregation with other lightcurves)
`get_stats_using_defaults`()	Generate all statistics for the lightcurve using default settings
`plot`([title, fold_period])	Plot the lightcurve
`plot_acf`([title])	Plot the autocorrelation function
`plot_periodogram`([frequencies, title])	Plot the periodogram for the lightcurve
`to_acf`(**kwargs)	Calculate the autocorrelation function for the lightcurve
`to_periodogram`([frequencies])	Construct a periodogram using the lightcurve

Attributes Documentation

MAD[source]#: The median absolute deviation of the normalised flux of the lightcurve

normalized_flux[source]#: The normalised flux of the lightcurve

rms[source]#: The root-mean-squared normalised flux of the lightcurve

sigmaG[source]#: The sigmaG of the normalised flux of the lightcurve

skewness[source]#: The skewness of the flux of the lightcurve

std[source]#: The standard deviation of the normalised flux of the lightcurve

von_neumann_ratio[source]#: The Von Neumann ratio for the normalised flux of the lightcurve. See elk.stats.von_neumann_ratio for more information

Methods Documentation

J_stetson(**kwargs)[source]#

Calculate the J Stetson statistic for the lightcurve

Keyword arguments are passed directly to elk.stats.J_stetson

Returns

J_Stetson: The J Stetson statistic

analysis_plot(name=None, run_all=False, show=True)[source]#

Plot a 3-panel plot of the lightcurve for quick analysis

Plot includes the lightcurve, periodogram and autocorrelation function.

Parameters

namestr: Identifier for this lightcurve (e.g. name of cluster)
run_allbool, optional: Whether to run all statistics using default settings (see .get_stats_using_defaults()), by default False
showbool, optional: Whether to immediately show the plot, by default True

Returns

fig, axesFigure, AxesSubplot: Figure and axes on which the analysis has been plotted

get_stats_table(name, run_all=False)[source]#

Create an Astropy Table of the statistics (for aggregation with other lightcurves)

Parameters

namestr: Identifier for this lightcurve (e.g. name of cluster)
run_allbool, optional: Whether to run all statistics using default settings (see .get_stats_using_defaults()), by default False

Returns

stats_tableTable: Table of statistics

get_stats_using_defaults()[source]#

Generate all statistics for the lightcurve using default settings

NOTE: This may not be optimal for your use case, be sure to consider each setting! By default we use a list of frequencies generated with np.arange(0.04, 11, 0.01) for the periodogram.

Returns

statsdict: A dictionary of the various statistics (also stored in self.stats)

plot(title='auto', fold_period=None, **kwargs)[source]#

Plot the lightcurve

Parameters

titlestr, optional: Title for the plot, by default “auto” (resulting in “Lightcurve for Sector …”)
fold_periodfloat, optional: Period on which to fold the light curve, if None then no folding is done, by default None
**kwargs: `various`: Keyword arguments passed to elk.plot.plot_lightcurve

Returns

fig, axFigure, AxesSubplot: Figure and axis on which the lightcurve has been plotted

plot_acf(title='auto', **kwargs)[source]#

Plot the autocorrelation function

Parameters

titlestr, optional: Title for the plot, by default “auto” (resulting in “Autocorrelation function for Sector …”)
**kwargs: `various`: Keyword arguments passed to elk.plot.plot_acf

Returns

fig, axFigure, AxesSubplot: Figure and axis on which the autocorrelation function has been plotted

plot_periodogram(frequencies=None, title='auto', **kwargs)[source]#

Plot the periodogram for the lightcurve

Parameters

frequenciesndarray: Frequencies at which periodogram evaluated, if None then periodogram must have already been generated using .to_periodogram()
titlestr, optional: Title for the plot, by default “auto” (resulting in “Periodogram for Sector …”)
**kwargs: `various`: Keyword arguments passed to elk.plot.plot_periodogram

Returns

fig, axFigure, AxesSubplot: Figure and axis on which the periodogram has been plotted

to_acf(**kwargs)[source]#

Calculate the autocorrelation function for the lightcurve

Keyword arguments are passed directly to elk.stats.autocorr

Returns

Same as elk.stats.periodogram

to_periodogram(frequencies=None, **kwargs)[source]#

Construct a periodogram using the lightcurve

Parameters

frequenciesnumpy.ndarray, optional: Frequencies at which to evaluate the periodogram, by default self.periodogram_freqs
**kwargsvarious: Keyword arguments to pass to elk.stats.periodogram

Returns

Same as elk.stats.periodogram