# Applying Cuts¶

It is useful to be able to apply “cuts” to data before trying to fit a model to the data. This is particularly important when using some of the “guessing” algorithms in fit_lc and nest_lc that use a minimum signal-to-noise ratio to pick “good” data points. These algorithms will raise an exception if there are no data points meeting the requirements, so it is advisable to check if the data meets the requirements beforehand.

## Signal-to-noise ratio cuts¶

Require at least one datapoint with signal-to-noise ratio (S/N) greater than 5 (in any band):

>>> passes = np.max(data['flux'] / data['fluxerr']) > 5.
>>> passes
True


Require two bands each with at least one datapoint having S/N > 5:

>>> mask = data['flux'] / data['fluxerr'] > 5.
>>> passes = len(np.unique(data['band'][mask])) >= 2
>>> passes
True