R
Roy Smith
We've recently started using pyflakes. The results seem to be similar
to most tools of this genre. It found a few real problems. It
generated a lot of noise about things which weren't really wrong, but
were easy to fix (mostly, unused imports), and a few plain old false
positives which have no easy "fix" (in the sense of, things I can change
which will make pyflakes STFU).
So, what's the best practice here? How do people deal with the false
positives? Is there some way to annotate the source code to tell
pyflakes to ignore something?
to most tools of this genre. It found a few real problems. It
generated a lot of noise about things which weren't really wrong, but
were easy to fix (mostly, unused imports), and a few plain old false
positives which have no easy "fix" (in the sense of, things I can change
which will make pyflakes STFU).
So, what's the best practice here? How do people deal with the false
positives? Is there some way to annotate the source code to tell
pyflakes to ignore something?