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)
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.