The get_data.py file reads in a csv file containing coordinates, and outputs 2 files: their images in dr16.npz, and the indices of these images in the given csv file (done as some objects fail to be retrieved from the ImgCutout service).
Change line 15 in get_data.py to read in the desired csv file with a column ra and dec for each object, for example from a query to the SDSS.
Using the .npz file obtained from get_data.py (loaded in the get_data function), the ULISSE.ipynb notebook can be used to find nearest neighbours (the lookalikes) for any given query, after running the whole notebook, using the last cell.
First, it obtains its image by passing its ra and dec coordinates to the get_query function. Then, it find the nearest neighbours with the get_nns function, and visualizes the result with the plot function .
If you find this work helpful, consider citing it using
@article{doorenbos2022ulisse,
title={ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei},
author={Doorenbos, Lars and Torbaniuk, Olena and Cavuoti, Stefano and Paolillo, Maurizio and Longo, Giuseppe and Brescia, Massimo and Sznitman, Raphael and M{\'a}rquez-Neila, Pablo},
journal={Astronomy \& Astrophysics},
volume={666},
pages={A171},
year={2022},
publisher={EDP Sciences}
}