avocado.AstronomicalObject.predict_gaussian_process

AstronomicalObject.predict_gaussian_process(bands, times, uncertainties=True, fitted_gp=None, **gp_kwargs)

Predict the Gaussian process in a given set of bands and at a given set of times.

Parameters:

• bands (list(str)) – bands to predict the Gaussian process in.
• times (list or numpy.array of floats) – times to evaluate the Gaussian process at.
• uncertainties (bool (optional)) – If True (default), the GP uncertainties are computed and returned along with the mean prediction. If False, only the mean prediction is returned.
• fitted_gp (function (optional)) – By default, this function will perform the GP fit before doing predictions. If the GP fit has already been done, then the fitted GP function (returned by fit_gaussian_process) can be passed here instead to skip redoing the fit.
• gp_kwargs (kwargs (optional)) – Additional arguments that are passed to fit_gaussian_process.

Returns:

• predictions (numpy.array) – A 2-dimensional array with shape (len(bands), len(times)) containing the Gaussian process mean flux predictions.
• prediction_uncertainties (numpy.array) – Only returned if uncertainties is True. This is an array with the same shape as predictions containing the Gaussian process uncertainty for the predictions.