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mschwoer
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| except TooFewPSMError: | ||
| logger.warning( | ||
| "Too few PSMs for FDR classification, assigning qval=1.0 and proba=1.0 to all PSMs." | ||
| ) | ||
| psm_df = pd.concat([df_target, df_decoy]) | ||
| psm_df["qval"] = 1.0 | ||
| psm_df["proba"] = 1.0 | ||
| return psm_df |
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This I am not sure about: we catch the error where it occurs and put the output together such that downstream tasks continue. There's two views on this
a) why does this method know what the downstream code filters for? (qval)
b) this method does FDR and knows what to output in case it fails (qval)
alternatives could be that the downstream code handles the TooFewPSMError explicitly
what do you think @GeorgWa ?
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As discussed in person, accepted
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Related to #806 (comment)
We should not fail if one of the batches yields no results.
Not sure about this is the best implementation, though.