sdc2

This is a skeleton framework for SDC2.

To score a submission for SDC2, one should first instantiate a Scorer. This can be done via two methods depending on the format of the input data.

If your input catalogues are in text format, one should use the class method: ska_sdc.sdc2.sdc2_scorer.Sdc2Scorer.from_txt(). For example:

from ska_sdc.sdc2 import sdc2_scorer

sub_cat_path = "/path/to/submission/catalogue.txt"
truth_cat_path = "/path/to/truth/catalogue.txt"

scorer = sdc2_scorer.from_txt(sub_cat_path, truth_cat_path)

However, if your input catalogues are already dataframes, one should instantiate the constructor for ska_sdc.sdc2.sdc2_scorer.Sdc2Scorer class directly:

from ska_sdc.sdc2 import sdc2_scorer

scorer = sdc2_scorer(df1, df2)

where df1 and df2 are dataframes.

When the class has been instantiated, the ska_sdc.sdc2.sdc2_scorer.Sdc2Scorer.run() method can be called to run the scoring pipeline:

result = scorer.run()

which returns an instance of the Score class ska_sdc.sdc2.models.sdc2_score.Sdc2Score containing all the details related to the run.

• The Sdc2Scorer class
• The Sdc2Score class
• Scoring pipeline
  • 1. Crossmatch preprocessing
    • Crossmatch preprocessing steps
  • 2. Catalogue crossmatching
  • 3. Crossmatch postprocessing
    • Crossmatch postprocessing steps
  • 4. Score computation