lightkurve.lightcurve.
TessLightCurve
(time=None, flux=None, flux_err=None, time_format=None, time_scale=None, 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={})¶Bases: lightkurve.lightcurve.LightCurve
Subclass of LightCurve
which holds extra data specific to the TESS mission.
Time measurements
Data flux for every time point
Uncertainty on each flux data point
String specifying how an instant of time is represented, e.g. ‘bkjd’ or ‘jd’.
String which specifies how the time is measured, e.g. tdb’, ‘tt’, ‘ut1’, or ‘utc’.
Centroid column and row coordinates as a function of time
Array indicating the quality of each data point
Bitmask specifying quality flags of cadences that should be ignored
Cadence numbers corresponding to every time measurement
Tess Input Catalog ID number
Attributes Summary
Returns the time values as an Astropy 
Methods Summary

Append LightCurve objects. 

Bins a lightcurve in blocks of size 

DEPRECATED: use 

Returns a copy of the LightCurve object. 

Plots the light curve using Matplotlib’s 

Estimate the CDPP noise metric using the SavitzkyGolay (SG) method. 

Fill in gaps in time with linear interpolation. 

Removes the low frequency trend using scipy’s SavitzkyGolay filter. 

Folds the lightcurve at a specified 

Returns a normalized version of the light curve. 

Plot the light curve using Matplotlib’s 

Removes cadences where the flux is NaN. 

Removes outlier data points using sigmaclipping. 

Plots the light curve using Matplotlib’s 

Prints a description of all noncallable attributes. 

Writes the light curve to a csv file. 

Writes the KeplerLightCurve to a FITS file. 

Converts the light curve to a Pandas 

Converts the light curve to a 

Converts the light curve to an Astropy 
Attributes Documentation
astropy_time
¶Returns the time values as an Astropy Time
object
(if time_format
is set).
The Time object will be created using the values in the time
,
time_format
, and time_scale
attributes.
For Kepler data products, the times are Barycentric.
If the time_format
attribute is not set or not one of the formats
allowed by AstroPy.
Methods Documentation
append
(self, others)¶Append LightCurve objects.
Light curves to be appended to the current one.
Concatenated light curve.
bin
(self, binsize=13, method='mean')¶Bins a lightcurve in blocks of size binsize
.
The value of the bins will contain the mean (method='mean'
) or the
median (method='median'
) of the original data. The default is mean.
Number of cadences to include in every bin.
The summary statistic to return for each bin. Default: ‘mean’.
LightCurve
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 rootmeansquare 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.
cdpp
(self, **kwargs)¶DEPRECATED: use estimate_cdpp()
instead.
copy
(self)¶Returns a copy of the LightCurve object.
This method uses the copy.deepcopy
function to ensure that all
objects stored within the LightCurve are copied (e.g. time and flux).
A new LightCurve
object which is a copy of the original.
errorbar
(self, linestyle='', **kwargs)¶Plots the light curve using Matplotlib’s errorbar()
method.
A matplotlib axes object to plot into. If no axes is provided, a new one will be generated.
Normalize the lightcurve before plotting?
Plot x axis label
Plot y axis label
Plot set_title
Path or URL to a matplotlib style file, or name of one of matplotlib’s builtin stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
Connect the error bars using a line?
Dictionary of arguments to be passed to matplotlib.pyplot.scatter
.
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 SavitzkyGolay (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 waveletbased algorithm to calculate the signaltonoise 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).
Remove low frequency signals using a SavitzkyGolay 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.
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 30min cadence.
Width of SavitskyGolay filter in cadences (odd number). Default value 101 (2.0 days in Kepler Long Cadence mode).
Polynomial order of the SavitskyGolay filter. The recommended value is 2.
The number of standard deviations to use for clipping outliers. The default is 5.
SavitzkyGolay CDPP noise metric in units partspermillion (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
(lc, method='nearest')¶Fill in gaps in time with linear interpolation.
Method to use for gap filling. ‘nearest’ by default.
LightCurve
A new light curve object in which 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 SavitzkyGolay filter.
This method wraps scipy.signal.savgol_filter
.
The length of the filter window (i.e. the number of coefficients).
window_length
must be a positive odd integer.
The order of the polynomial used to fit the samples. polyorder
must be less than window_length.
If True
, the method will return a tuple of two elements
(flattened_lc, trend_lc) where trend_lc is the removed trend.
If there are large gaps in time, flatten will split the flux into
several sublightcurves 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.
Number of iterations to iteratively sigma clip and flatten. If more than one, will perform the flatten several times, removing outliers each time.
Number of sigma above which to remove outliers from the flatten
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.
Dictionary of arguments to be passed to scipy.signal.savgol_filter
.
Flattened lightcurve.
return_trend
is True
, the method will also return:Trend in the lightcurve data
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.
The period upon which to fold.
Time corresponding to zero phase. In the same units as the
LightCurve’s time
attribute. Defaults to 0 if not set.
Deprecated. Use t0
instead.
FoldedLightCurve
A new light curve object in which the data are folded and sorted by
phase. The object contains an extra phase
attribute.
normalize
(self)¶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.
LightCurve
A new light curve object in which flux
and flux_err
are divided
by the median.
plot
(self, **kwargs)¶Plot the light curve using Matplotlib’s plot()
method.
A matplotlib axes object to plot into. If no axes is provided, a new one will be generated.
Normalize the lightcurve before plotting?
Plot x axis label
Plot y axis label
Plot set_title
Path or URL to a matplotlib style file, or name of one of matplotlib’s builtin stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
Dictionary of arguments to be passed to matplotlib.pyplot.plot
.
The matplotlib axes object.
remove_nans
(self)¶Removes cadences where the flux is NaN.
LightCurve
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 sigmaclipping.
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.
Sigmaclipping 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
.
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.
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
.
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
.
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. Defaults to True
.
Dictionary of arguments to be passed to astropy.stats.sigma_clip
.
LightCurve
A new light curve object from which outlier data points have been removed.
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])
This example removes only points where the flux is larger than 1 standard deviation from the median, but leaves negative outliers in place:
>>> 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])
scatter
(self, colorbar_label='', show_colorbar=True, **kwargs)¶Plots the light curve using Matplotlib’s scatter()
method.
A matplotlib axes object to plot into. If no axes is provided, a new one will be generated.
Normalize the lightcurve before plotting?
Plot x axis label
Plot y axis label
Plot set_title
Path or URL to a matplotlib style file, or name of one of matplotlib’s builtin stylesheets (e.g. ‘ggplot’). Lightkurve’s custom stylesheet is used by default.
Label to show next to the colorbar (if c
is given).
Show the colorbar if colors are given using the c
argument?
Dictionary of arguments to be passed to matplotlib.pyplot.scatter
.
The matplotlib axes object.
show_properties
(self)¶Prints a description of all noncallable attributes.
Prints in order of type (ints, strings, lists, arrays, others).
to_csv
(self, path_or_buf=None, **kwargs)¶Writes the light curve to a csv file.
File path or object, if None is provided the result is returned as a string.
Dictionary of arguments to be passed to pandas.DataFrame.to_csv()
.
Returns a csvformatted string if path_or_buf=None
,
returns None otherwise.
to_fits
(self, path=None, overwrite=False, aperture_mask=None, **extra_data)¶Writes the KeplerLightCurve to a FITS file.
File path, if None
returns an astropy.io.fits.HDUList object.
Whether or not to overwrite the file
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 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 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.
List of columns to include in the DataFrame. The names must match
attributes of the LightCurve
object (e.g. time
, flux
).
pandas.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
.
method='lombscargle'
are:minimum_frequency
, maximum_frequency
, mininum_period
,
maximum_period
, frequency
, period
, nterms
,
nyquist_factor
, oversample_factor
, freq_unit
,
normalization
.
method='bls'
are:minimum_period
, maximum_period
, period
,
frequency_factor
, duration
.
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'
.
Keyword arguments passed to either
LombScarglePeriodogram
or
BoxLeastSquaresPeriodogram
.
Periodogram
objectThe power spectrum object extracted from the light curve.
to_table
(self)¶Converts the light curve to an Astropy Table
object.
astropy.table.Table
An AstroPy Table with columns ‘time’, ‘flux’, and ‘flux_err’.
Created with ♥ by the Lightkurve collaboration. Please cite us or join us on GitHub.