avocado.Augmentor._choose_sampling_times

Augmentor._choose_sampling_times(reference_object, augmented_metadata, max_time_shift=50, block_width=250, window_padding=100, drop_fraction=0.1)

Choose the times at which to sample for a new augmented object.

This method should really be survey specific, but a default implementation is included here that works reasonably well for generic light curves. If you are implementing a survey specific version of this method, you only need to have the reference_object and augmented_metadata parameters. The other parameters are different knobs for this method.

This implementation of _choose_sampling_times requires that the method _choose_target_observation_count() be defined that returns how many observations we should attempt to have for the new light curve. If a different implementation of _choose_sampling_times is used, that method may not be required.

Parameters:

• reference_object (AstronomicalObject) – The object to use as a reference for the augmentation.
• augmented_metadata (dict) – The augmented metadata
• max_time_shift (float (optional)) – The new sampling times will be shifted by up to this amount relative to the original ones.
• block_width (float (optional)) – A block of observations with a width specified by this parameter will be dropped.
• window_padding (float (optional)) – Observations outside of a window bounded by the first and last observations in the reference objects light curve with a padding specified by this parameter will be dropped.
• drop_fraction (float (optional)) – This fraction of observations will always be dropped when creating the augmented light curve.

Returns:

sampling_times – A pandas Dataframe that has the following columns:

• time : the times of the simulated observations.
• band : the bands of the simulated observations.
• reference_time : the times in the reference light curve that correspond to the times of the simulated observations.

Return type:

pandas Dataframe