TessLightCurve¶

class lightkurve.lightcurve.TessLightCurve(time=None, flux=None, flux_err=None, flux_unit=Unit("electron / s"), time_format='btjd', time_scale='tdb', centroid_col=None, centroid_row=None, quality=None, quality_bitmask=None, cadenceno=None, sector=None, camera=None, ccd=None, targetid=None, ra=None, dec=None, label=None, meta=None)¶

Bases: lightkurve.lightcurve.LightCurve

Subclass of LightCurve which holds extra data specific to the TESS mission.

Attributes

timearray-like: Time measurements
fluxarray-like: Data flux for every time point
flux_errarray-like: Uncertainty on each flux data point
flux_unitUnit or str: Unit of the flux values. If a string is passed, it will be passed on the the constructor of Unit.
time_formatstr: String specifying how an instant of time is represented, e.g. ‘bkjd’ or ‘jd’.
time_scalestr: String which specifies how the time is measured, e.g. tdb’, ‘tt’, ‘ut1’, or ‘utc’.
centroid_col, centroid_rowarray-like, array-like: Centroid column and row coordinates as a function of time
qualityarray-like: Array indicating the quality of each data point
quality_bitmaskint: Bitmask specifying quality flags of cadences that should be ignored
cadencenoarray-like: Cadence numbers corresponding to every time measurement
targetidint: Tess Input Catalog ID number

Attributes Summary

`astropy_time`	Returns the time values as an Astropy `Time` object.
`flux_quantity`	Returns the flux as an Astropy `Quantity` object.
`flux_unit`

Methods Summary

`append`(self, others[, inplace])	Append one or more other `LightCurve` object(s) to this one.
`bin`(self[, binsize, bins, method])	Bins a lightcurve in chunks defined by `binsize` or `bins`.
`copy`(self)	Returns a copy of this `LightCurve` object.
`errorbar`(self[, linestyle])	Plots the light curve using Matplotlib’s `errorbar` method.
`estimate_cdpp`(self[, transit_duration, …])	Estimate the CDPP noise metric using the Savitzky-Golay (SG) method.
`fill_gaps`(self[, method])	Fill in gaps in time.
`flatten`(self[, window_length, polyorder, …])	Removes the low frequency trend using scipy’s Savitzky-Golay filter.
`fold`(self, period[, t0, transit_midpoint])	Folds the lightcurve at a specified `period` and reference time `t0`.
`from_timeseries`(ts)	Creates a new `LightCurve` from an AstroPy `TimeSeries` object.
`interact_bls`(self[, notebook_url, …])	Display an interactive Jupyter Notebook widget to find planets.
`normalize`(self[, unit])	Returns a normalized version of the light curve.
`plot`(self, \\kwargs)	Plot the light curve using Matplotlib’s `plot` method.
`remove_nans`(self)	Removes cadences where the flux is NaN.
`remove_outliers`(self[, sigma, sigma_lower, …])	Removes outlier data points using sigma-clipping.
`scatter`(self[, colorbar_label, show_colorbar])	Plots the light curve using Matplotlib’s `scatter` method.
`show_properties`(self)	Prints a description of all non-callable attributes.
`to_corrector`(self[, method])	Returns a corrector object to remove instrument systematics.
`to_csv`(self[, path_or_buf])	Writes the light curve to a CSV file.
`to_fits`(self[, path, overwrite, …])	Writes the KeplerLightCurve to a FITS file.
`to_pandas`(self[, columns])	Converts the light curve to a Pandas `DataFrame` object.
`to_periodogram`(self[, method])	Converts the light curve to a `Periodogram` power spectrum object.
`to_seismology`(self, \\kwargs)	Returns a `Seismology` object for estimating quick-look asteroseismic quantities.
`to_table`(self)	Converts the light curve to an Astropy `Table` object.
`to_timeseries`(self)	Converts the light curve to an AstroPy `TimeSeries` object.

Attributes Documentation

astropy_time¶

Returns the time values as an Astropy Time object.

The Time object will be created based on the values of the light curve’s time, time_format, and time_scale attributes.

Raises

ValueError: If the time_format attribute is not set or not one of the formats allowed by AstroPy.

Examples

The section below demonstrates working with time values using the TESS light curve of Pi Mensae as an example, which we obtained as follows:

>>> import lightkurve as lk
>>> lc = lk.search_lightcurvefile("Pi Mensae", mission="TESS", sector=1).download().PDCSAP_FLUX  
>>> lc  
TessLightCurve(TICID: 261136679)

Every LightCurve object has a time attribute, which provides access to the original array of time values given in the native format and scale used by the data product from which the light curve was obtained:

>>> lc.time  
array([1325.29698328, 1325.29837215, 1325.29976102, ..., 1353.17431099,
       1353.17569985, 1353.17708871])
>>> lc.time_format  
'btjd'
>>> lc.time_scale  
'tdb'

To enable users to convert these time values to different formats or scales, Lightkurve provides an easy way to access the time values as an AstroPy Time object:

>>> lc.astropy_time  
<Time object: scale='tdb' format='jd' value=[2458325.29698328 2458325.29837215 2458325.29976102 ... 2458353.17431099
2458353.17569985 2458353.17708871]>

This is convenient because AstroPy Time objects provide a lot of useful features. For example, we can now obtain the Julian Day or ISO values that correspond to the raw time values:

>>> lc.astropy_time.iso  
array(['2018-07-25 19:07:39.356', '2018-07-25 19:09:39.354',
       '2018-07-25 19:11:39.352', ..., '2018-08-22 16:11:00.470',
       '2018-08-22 16:13:00.467', '2018-08-22 16:15:00.464'], dtype='<U23')
>>> lc.astropy_time.jd   
array([2458325.29698328, 2458325.29837215, 2458325.29976102, ...,
       2458353.17431099, 2458353.17569985, 2458353.17708871])

flux_quantity¶: Returns the flux as an Astropy Quantity object.

flux_unit¶

Methods Documentation

append(self, others, inplace=False)¶

Append one or more other LightCurve object(s) to this one.

Parameters

othersLightCurve, or list of LightCurve: Light curve(s) to be appended to the current one.
inplacebool: If True, change the current LightCurve instance in place instead of creating and returning a new one. Defaults to False.

Returns

new_lcLightCurve: Light curve which has the other light curves appened to it.

bin(self, binsize=None, bins=None, method='mean')¶

Bins a lightcurve in chunks defined by binsize or bins.

The flux value of the bins will be computed by taking the mean (method='mean') or the median (method='median') of the flux. The default is mean.

Parameters

binsizeint or None: Number of cadences to include in every bin. The default is 13 if neither bins nor binsize is assigned.
binsint, list of int, str, or None: Requires Astropy version >3.1 and >2.10 Instruction for how to assign bin locations grouping by the time of samples rather than index; overrides the binsize= if given. If bins is an int, it is the number of bins. If it is a list it is taken to be the bin edges. If it is a string, it must be one of ‘blocks’, ‘knuth’, ‘scott’ or ‘freedman’. See histogram for description of these algorithms.
method: str, one of ‘mean’ or ‘median’: The summary statistic to return for each bin. Default: ‘mean’.

Returns

binned_lcLightCurve: A new light curve which has been binned.

Notes

If the ratio between the lightcurve length and the binsize is not a whole number, then the remainder of the data points will be ignored.
If the original light curve contains flux uncertainties (flux_err), the binned lightcurve will report the root-mean-square error. If no uncertainties are included, the binned curve will return the standard deviation of the data.
If the original lightcurve contains a quality attribute, then the bitwise OR of the quality flags will be returned per bin.

copy(self)¶

Returns a copy of this LightCurve object.

This method uses Python’s copy.deepcopy function to ensure that all objects stored within the LightCurve instance are fully copied.

Returns

lc_copyLightCurve: A new light curve object which is a copy of the original.

errorbar(self, linestyle='', **kwargs)¶

Plots the light curve using Matplotlib’s errorbar method.

Parameters

axAxes: A matplotlib axes object to plot into. If no axes is provided, a new one will be generated.
normalizebool: Normalize the lightcurve before plotting?
xlabelstr: Plot x axis label
ylabelstr: Plot y axis label
titlestr: Plot set_title
stylestr: Path or URL to a matplotlib style file, or name of one of matplotlib’s built-in stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
linestylestr: Connect the error bars using a line?
kwargsdict: Dictionary of arguments to be passed to matplotlib.pyplot.scatter.

Returns

axAxes: The matplotlib axes object.

estimate_cdpp(self, transit_duration=13, savgol_window=101, savgol_polyorder=2, sigma=5.0)¶

Estimate the CDPP noise metric using the Savitzky-Golay (SG) method.

A common estimate of the noise in a lightcurve is the scatter that remains after all long term trends have been removed. This is the idea behind the Combined Differential Photometric Precision (CDPP) metric. The official Kepler Pipeline computes this metric using a wavelet-based algorithm to calculate the signal-to-noise of the specific waveform of transits of various durations. In this implementation, we use the simpler “sgCDPP proxy algorithm” discussed by Gilliland et al (2011ApJS..197….6G) and Van Cleve et al (2016PASP..128g5002V).

The steps of this algorithm are:

Remove low frequency signals using a Savitzky-Golay filter with window length savgol_window and polynomial order savgol_polyorder.
Remove outliers by rejecting data points which are separated from the mean by sigma times the standard deviation.
Compute the standard deviation of a running mean with a configurable window length equal to transit_duration.

We use a running mean (as opposed to block averaging) to strongly attenuate the signal above 1/transit_duration whilst retaining the original frequency sampling. Block averaging would set the Nyquist limit to 1/transit_duration.

Parameters

transit_durationint, optional: The transit duration in units of number of cadences. This is the length of the window used to compute the running mean. The default is 13, which corresponds to a 6.5 hour transit in data sampled at 30-min cadence.
savgol_windowint, optional: Width of Savitsky-Golay filter in cadences (odd number). Default value 101 (2.0 days in Kepler Long Cadence mode).
savgol_polyorderint, optional: Polynomial order of the Savitsky-Golay filter. The recommended value is 2.
sigmafloat, optional: The number of standard deviations to use for clipping outliers. The default is 5.

Returns

cdppfloat: Savitzky-Golay CDPP noise metric in units parts-per-million (ppm).

Notes

This implementation is adapted from the Matlab version used by Jeff van Cleve but lacks the normalization factor used there: svn+ssh://murzim/repo/so/trunk/Develop/jvc/common/compute_SG_noise.m

fill_gaps(self, method='gaussian_noise')¶

Fill in gaps in time.

By default, the gaps will be filled with random white Gaussian noise distributed according to \(\mathcal{N} (\mu=\overline{\mathrm{flux}}, \sigma=\mathrm{CDPP})\). No other methods are supported at this time.

Parameters

methodstring {‘gaussian_noise’}: Method to use for gap filling. Fills with Gaussian noise by default.

Returns

filled_lightcurveLightCurve: A new light curve object in which all NaN values and gaps in time have been filled.

flatten(self, window_length=101, polyorder=2, return_trend=False, break_tolerance=5, niters=3, sigma=3, mask=None, **kwargs)¶

Removes the low frequency trend using scipy’s Savitzky-Golay filter.

This method wraps scipy.signal.savgol_filter.

Parameters

window_lengthint: The length of the filter window (i.e. the number of coefficients). window_length must be a positive odd integer.
polyorderint: The order of the polynomial used to fit the samples. polyorder must be less than window_length.
return_trendbool: If True, the method will return a tuple of two elements (flattened_lc, trend_lc) where trend_lc is the removed trend.
break_toleranceint: If there are large gaps in time, flatten will split the flux into several sub-lightcurves and apply savgol_filter to each individually. A gap is defined as a period in time larger than break_tolerance times the median gap. To disable this feature, set break_tolerance to None.
nitersint: Number of iterations to iteratively sigma clip and flatten. If more than one, will perform the flatten several times, removing outliers each time.
sigmaint: Number of sigma above which to remove outliers from the flatten
maskboolean array with length of self.time: Boolean array to mask data with before flattening. Flux values where mask is True will not be used to flatten the data. An interpolated result will be provided for these points. Use this mask to remove data you want to preserve, e.g. transits.
**kwargsdict: Dictionary of arguments to be passed to scipy.signal.savgol_filter.

Returns

flatten_lcLightCurve: New light curve object with long-term trends removed.
If return_trend is set to True, this method will also return:
trend_lcLightCurve: New light curve object containing the trend that was removed.

fold(self, period, t0=None, transit_midpoint=None)¶

Folds the lightcurve at a specified period and reference time t0.

This method returns a FoldedLightCurve object in which the time values range between -0.5 to +0.5 (i.e. the phase). Data points which occur exactly at t0 or an integer multiple of t0 + n*period will have phase value 0.0.

Parameters

periodfloat: The period upon which to fold, in the same units as this LightCurve’s time attribute.
t0float, optional: Time corresponding to zero phase, in the same units as this LightCurve’s time attribute. Defaults to 0 if not set.
transit_midpointfloat, optional: Deprecated. Use t0 instead.

Returns

folded_lightcurveFoldedLightCurve: A new light curve object in which the data are folded and sorted by phase. The object contains an extra phase attribute.

Examples

The example below shows a light curve with a period dip which occurs near time value 1001 and has a period of 5 days. Calling the fold method will transform the light curve into a FoldedLightCurve object:

>>> import lightkurve as lk
>>> lc = lk.LightCurve(time=range(1001, 1012), flux=[0.5, 1.0, 1.0, 1.0, 1.0, 0.5, 1.0, 1.0, 1.0, 1.0, 0.5])
>>> folded_lc = lc.fold(period=5., t0=1006.)
>>> folded_lc   
<lightkurve.lightcurve.FoldedLightCurve>

An object of type FoldedLightCurve is useful because it provides convenient access to the phase values and the phase-folded fluxes:

>>> folded_lc.phase
array([-0.4, -0.4, -0.2, -0.2,  0. ,  0. ,  0. ,  0.2,  0.2,  0.4,  0.4])
>>> folded_lc.flux
array([1. , 1. , 1. , 1. , 0.5, 0.5, 0.5, 1. , 1. , 1. , 1. ])

We can still access the original time values as well:

>>> folded_lc.time_original
array([1004, 1009, 1005, 1010, 1001, 1006, 1011, 1002, 1007, 1003, 1008])

A FoldedLightCurve inherits all the features of a standard LightCurve object. For example, we can very quickly obtain a phase-folded plot using:

>>> folded_lc.plot()    

static from_timeseries(ts)¶

Creates a new LightCurve from an AstroPy TimeSeries object.

Parameters

tsTimeSeries: The AstroPy TimeSeries object. The object must contain columns named ‘time’, ‘flux’, and ‘flux_err’.

interact_bls(self, notebook_url='localhost:8888', minimum_period=None, maximum_period=None, resolution=2000)¶

Display an interactive Jupyter Notebook widget to find planets.

The Box Least Squares (BLS) periodogram is a statistical tool used for detecting transiting exoplanets and eclipsing binaries in light curves. This method will display a Jupyter Notebook Widget which enables the BLS algorithm to be used interactively. Behind the scenes, the widget uses the AstroPy implementation of BLS [1].

This feature only works inside an active Jupyter Notebook. It requires Bokeh v1.0 (or later) and AstroPy v3.1 (or later), which are optional dependencies. An error message will be shown if these dependencies are not available.

Parameters

notebook_url: str: Location of the Jupyter notebook page (default: “localhost:8888”) When showing Bokeh applications, the Bokeh server must be explicitly configured to allow connections originating from different URLs. This parameter defaults to the standard notebook host and port. If you are running on a different location, you will need to supply this value for the application to display properly. If no protocol is supplied in the URL, e.g. if it is of the form “localhost:8888”, then “http” will be used.
minimum_periodfloat or None: Minimum period to assess the BLS to. If None, default value of 0.3 days will be used.
maximum_periodfloat or None: Maximum period to evaluate the BLS to. If None, the time coverage of the lightcurve / 4 will be used.
resolutionint: Number of points to use in the BLS panel. Lower this value for faster but less accurate performance. You can also vary this value using the widget’s Resolution Slider.

References

1: http://docs.astropy.org/en/latest/stats/bls.html

Examples

Load the light curve for Kepler-10, remove long-term trends, and display the BLS tool as follows:

>>> import lightkurve as lk
>>> lc = lk.search_lightcurvefile('kepler-10', quarter=3).download()  
>>> lc = lc.PDCSAP_FLUX.normalize().flatten()  
>>> lc.interact_bls()  

normalize(self, unit='unscaled')¶

Returns a normalized version of the light curve.

The normalized light curve is obtained by dividing the flux and flux_err object attributes by the by the median flux. Optionally, the result will be multiplied by 1e2 (if unit='percent'), 1e3 (unit='ppt'), or 1e6 (unit='ppm').

Parameters

unit‘unscaled’, ‘percent’, ‘ppt’, ‘ppm’: The desired relative units of the normalized light curve; ‘ppt’ means ‘parts per thousand’, ‘ppm’ means ‘parts per million’.

Returns

normalized_lightcurveLightCurve: A new light curve object in which flux and flux_err have been divided by the median flux.

Warns

LightkurveWarning: If the median flux is negative or within half a standard deviation from zero.

Examples

>>> import lightkurve as lk
>>> lc = lk.LightCurve(time=[1, 2, 3], flux=[25945.7, 25901.5, 25931.2], flux_err=[6.8, 4.6, 6.2])
>>> normalized_lc = lc.normalize()
>>> normalized_lc.flux
array([1.00055917, 0.99885466, 1.        ])
>>> normalized_lc.flux_err
array([0.00026223, 0.00017739, 0.00023909])

plot(self, **kwargs)¶

Plot the light curve using Matplotlib’s plot method.

Parameters

axAxes: A matplotlib axes object to plot into. If no axes is provided, a new one will be created.
normalizebool: Normalize the lightcurve before plotting?
xlabelstr: Plot x axis label
ylabelstr: Plot y axis label
titlestr: Plot set_title
stylestr: Path or URL to a matplotlib style file, or name of one of matplotlib’s built-in stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
kwargsdict: Dictionary of arguments to be passed to matplotlib.pyplot.plot.

Returns

axAxes: The matplotlib axes object.

remove_nans(self)¶

Removes cadences where the flux is NaN.

Returns

clean_lightcurveLightCurve: A new light curve object from which NaNs fluxes have been removed.

remove_outliers(self, sigma=5.0, sigma_lower=None, sigma_upper=None, return_mask=False, **kwargs)¶

Removes outlier data points using sigma-clipping.

This method returns a new LightCurve object from which data points are removed if their flux values are greater or smaller than the median flux by at least sigma times the standard deviation.

Sigma-clipping works by iterating over data points, each time rejecting values that are discrepant by more than a specified number of standard deviations from a center value. If the data contains invalid values (NaNs or infs), they are automatically masked before performing the sigma clipping.

Note

This function is a convenience wrapper around astropy.stats.sigma_clip() and provides the same functionality. Any extra arguments passed to this method will be passed on to sigma_clip.

Parameters

sigmafloat: The number of standard deviations to use for both the lower and upper clipping limit. These limits are overridden by sigma_lower and sigma_upper, if input. Defaults to 5.
sigma_lowerfloat or None: The number of standard deviations to use as the lower bound for the clipping limit. Can be set to float(‘inf’) in order to avoid clipping outliers below the median at all. If None then the value of sigma is used. Defaults to None.
sigma_upperfloat or None: The number of standard deviations to use as the upper bound for the clipping limit. Can be set to float(‘inf’) in order to avoid clipping outliers above the median at all. If None then the value of sigma is used. Defaults to None.
return_maskbool: Whether or not to return a mask (i.e. a boolean array) indicating which data points were removed. Entries marked as True in the mask are considered outliers. This mask is not returned by default.
**kwargsdict: Dictionary of arguments to be passed to astropy.stats.sigma_clip.

Returns

clean_lcLightCurve: A new light curve object from which outlier data points have been removed.
outlier_maskNumPy array, optional: Boolean array flagging which cadences were removed. Only returned if return_mask=True.

Examples

This example generates a new light curve in which all points that are more than 1 standard deviation from the median are removed:

>>> lc = LightCurve(time=[1, 2, 3, 4, 5], flux=[1, 1000, 1, -1000, 1])
>>> lc_clean = lc.remove_outliers(sigma=1)
>>> lc_clean.time
array([1, 3, 5])
>>> lc_clean.flux
array([1, 1, 1])

Instead of specifying sigma, you may specify separate sigma_lower and sigma_upper parameters to remove only outliers above or below the median. For example:

>>> lc = LightCurve(time=[1, 2, 3, 4, 5], flux=[1, 1000, 1, -1000, 1])
>>> lc_clean = lc.remove_outliers(sigma_lower=float('inf'), sigma_upper=1)
>>> lc_clean.time
array([1, 3, 4, 5])
>>> lc_clean.flux
array([    1,     1, -1000,     1])

Optionally, you may use the return_mask parameter to return a boolean array which flags the outliers identified by the method. For example:

>>> lc_clean, mask = lc.remove_outliers(sigma=1, return_mask=True)
>>> mask
array([False,  True, False,  True, False])

scatter(self, colorbar_label='', show_colorbar=True, **kwargs)¶

Plots the light curve using Matplotlib’s scatter method.

Parameters

axAxes: A matplotlib axes object to plot into. If no axes is provided, a new one will be generated.
normalizebool: Normalize the lightcurve before plotting?
xlabelstr: Plot x axis label
ylabelstr: Plot y axis label
titlestr: Plot set_title
stylestr: Path or URL to a matplotlib style file, or name of one of matplotlib’s built-in stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
colorbar_labelstr: Label to show next to the colorbar (if c is given).
show_colorbarboolean: Show the colorbar if colors are given using the c argument?
kwargsdict: Dictionary of arguments to be passed to matplotlib.pyplot.scatter.

Returns

axAxes: The matplotlib axes object.

show_properties(self)¶

Prints a description of all non-callable attributes.

Prints in order of type (ints, strings, lists, arrays, others).

to_corrector(self, method='sff')¶

Returns a corrector object to remove instrument systematics.

Parameters

methodsstring: Currently, only “sff” is supported. This will return a SFFCorrector class instance.

Returns

correcterlightkurve.Correcter: Instance of a Corrector class, which typically provides correct() and diagnose() methods.

to_csv(self, path_or_buf=None, **kwargs)¶

Writes the light curve to a CSV file.

This method will convert the light curve into the Comma-Separated Values (CSV) text format. By default this method will return the result as a string, but you can also write the string directly to disk by providing a file name or handle via the path_or_buf parameter.

Parameters

path_or_bufstring or file handle: File path or object. By default, the result is returned as a string.
**kwargsdict: Dictionary of arguments to be passed to pandas.DataFrame.to_csv().

Returns

csvstr or None: Returns a csv-formatted string if path_or_buf=None. Returns None otherwise.

to_fits(self, path=None, overwrite=False, flux_column_name='FLUX', aperture_mask=None, **extra_data)¶

Writes the KeplerLightCurve to a FITS file.

Parameters

pathstring, default None: File path, if None returns an astropy.io.fits.HDUList object.
overwritebool: Whether or not to overwrite the file
flux_column_namestr: The name of the label for the FITS extension, e.g. SAP_FLUX or FLUX
aperture_maskarray-like: Optional 2D aperture mask to save with this lightcurve object, if defined. The mask can be either a boolean mask or an integer mask mimicking the Kepler/TESS convention; boolean masks are automatically converted to the Kepler/TESS conventions
extra_datadict: Extra keywords or columns to include in the FITS file. Arguments of type str, int, float, or bool will be stored as keywords in the primary header. Arguments of type np.array or list will be stored as columns in the first extension.

Returns

hduastropy.io.fits: Returns an astropy.io.fits object if path is None

to_pandas(self, columns=('time', 'flux', 'flux_err'))¶

Converts the light curve to a Pandas DataFrame object.

By default, the object returned will contain the columns ‘time’, ‘flux’, and ‘flux_err’. This can be changed using the columns parameter.

Parameters

columnslist of str: List of columns to include in the DataFrame. The names must match attributes of the LightCurve object (e.g. time, flux).

Returns

dataframepandas.DataFrame: A data frame indexed by time and containing the columns flux and flux_err.

to_periodogram(self, method='lombscargle', **kwargs)¶

Converts the light curve to a Periodogram power spectrum object.

This method will call either lightkurve.periodogram.LombScarglePeriodogram.from_lightcurve() or lightkurve.periodogram.BoxLeastSquaresPeriodogram.from_lightcurve(), which in turn wrap astropy.stats.LombScargle and astropy.stats.BoxLeastSquares.

Optional keywords accepted if method='lombscargle' are: minimum_frequency, maximum_frequency, mininum_period, maximum_period, frequency, period, nterms, nyquist_factor, oversample_factor, freq_unit, normalization, ls_method.

Optional keywords accepted if method='bls' are minimum_period, maximum_period, period, frequency_factor, duration.

Parameters

method{‘lombscargle’, ‘boxleastsquares’, ‘ls’, ‘bls’}: Use the Lomb Scargle or Box Least Squares (BLS) method to extract the power spectrum. Defaults to 'lombscargle'. 'ls' and 'bls' are shorthands for 'lombscargle' and 'boxleastsquares'.
kwargsdict: Keyword arguments passed to either LombScarglePeriodogram or BoxLeastSquaresPeriodogram.

Returns

PeriodogramPeriodogram object: The power spectrum object extracted from the light curve.

to_seismology(self, **kwargs)¶

Returns a Seismology object for estimating quick-look asteroseismic quantities.

All **kwargs will be passed to the to_periodogram() method.

Returns

seismologySeismology object: Object which can be used to estimate quick-look asteroseismic quantities.

to_table(self)¶

Converts the light curve to an Astropy Table object.

Returns

tableastropy.table.Table: An AstroPy Table with columns ‘time’, ‘flux’, and ‘flux_err’.

to_timeseries(self)¶

Converts the light curve to an AstroPy TimeSeries object.

This feature requires AstroPy v3.2 or later (released in 2019). An ImportError will be raised if this version is not available.

Returns

timeseriesTimeSeries: An AstroPy TimeSeries object.