Welcome to SKA Science Data Challenge Scoring’s documentation!

This package is an open-source implementation of the code used to score and rank the submissions for the first SKA Science Data Challenge (SDC). The original IDL code is available at: https://astronomers.skatelescope.org/ska-science-data-challenge-1/

To score a submission for SDC1, 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.sdc1.sdc1_scorer.Sdc1Scorer.from_txt(). For example:

from ska_sdc.sdc1 import sdc1_scorer

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

scorer = sdc1_scorer.from_txt(sub_cat_path, truth_cat_path, freq=1400)

However, if your input catalogues are already dataframes, one should instantiate the constructor for ska_sdc.sdc1.sdc1_scorer.Sdc1Scorer class directly:

from ska_sdc.sdc1 import sdc1_scorer

scorer = sdc1_scorer(df1, df2, freq=1400)

where df1 and df2 are dataframes.

When the class has been instantiated, the ska_sdc.sdc1.sdc1_scorer.Sdc1Scorer.run() method can be called to run the scoring pipeline:

result = scorer.run()

which returns an instance of the Score class ska_sdc.sdc1.models.sdc1_score.Sdc1Score containing all the details related to the run.

Indices and tables