lightkurve.LightCurve.bin

LightCurve.bin(time_bin_size=None, time_bin_start=None, n_bins=None, aggregate_func=None, bins=None, binsize=None)

Bins a lightcurve in equally-spaced bins in time.

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.

Parameters

time_bin_sizeQuantity, float

The time interval for the binned time series. (Default: 0.5 days; default unit: days.)

time_bin_startTime, optional

The start time for the binned time series. Defaults to the first time in the sampled time series.

n_binsint, optional

The number of bins to use. Defaults to the number needed to fit all the original points. Note that this will create this number of bins of length time_bin_size independent of the lightkurve length.

aggregate_funccallable, optional

The function to use for combining points in the same bin. Defaults to np.nanmean.

binsint

The number of bins to divide the lightkurve into. In contrast to n_bins this sets the length of time_bin_size accordingly.

binsizeint

In Lightkurve v1.x, the default behavior of bin() was to create bins which contained an equal number data points in each bin. This type of binning is discouraged because it usually makes more sense to create equally-sized bins in time duration, which is the new default behavior in Lightkurve v2.x. Nevertheless, this binsize parameter allows users to simulate the old behavior of Lightkurve v1.x. For ease of implementation, setting this parameter is identical to passing time_bin_size = lc.time[binsize] - time[0], which means that the bins are not guaranteed to contain an identical number of data points.

Returns

binned_lcLightCurve

A new light curve which has been binned.

lightkurve.LightCurve.copy lightkurve.LightCurve.fill_gaps