We currently have two top-level functions called predict_epitopes_from_variants and predict_epitopes_from_effects with a very large number of arguments. Creating another top-level function (e.g. to predict from protein changes without genomic variants) would require duplicating a lot of thing long argument list. To make it easier to add features in the future I'm making a TopiaryPredictor object which holds properties shared between different top-level methods.
Coverage increased (+0.3%) to 87.556% when pulling cb77980a874b35adfb44d47aba42a3bd62061ec1 on TopiaryPredictor-object into a90625cee5f7d175c14411fc0f8134ee257c36d5 on master.
Coverage increased (+0.3%) to 87.528% when pulling b6654a4f6b9ee9c23e75d9fe7c8e7ec9bc345948 on TopiaryPredictor-object into a90625cee5f7d175c14411fc0f8134ee257c36d5 on master.
We currently have two top-level functions called
predict_epitopes_from_variants
andpredict_epitopes_from_effects
with a very large number of arguments. Creating another top-level function (e.g. to predict from protein changes without genomic variants) would require duplicating a lot of thing long argument list. To make it easier to add features in the future I'm making aTopiaryPredictor
object which holds properties shared between different top-level methods.This change is