4. Score computation

ska_sdc.sdc2.utils.create_score.create_sdc_score(config, sieved_sub_df, n_det, train, detail)

Complete the scoring pipeline using the data generated by the previous steps. This requires the prepared truth and submission catalogues, and the candidate match catalogues created from the crossmatch step.

Parameters:

• sieved_sub_df (pandas.DataFrame) – The processed and sieved candidate match catalogue between submission and truth.
• n_det (int) – Total number of detected sources.
• train (bool) – Whether the score is determined based on training area only
• detail (bool) – If True, will include the detailed score and match data with the returned Sdc2Score object.