Hi All,

I will be running the NumPy sprint at Scipy 2017 and I'm trying to put together a suitable list of things to sprint on. In my experience, sprinting on NumPy is hard, enhancements generally need lengthy review and even finding and doing simple bug fixes can take time. What I have in mind at this point, apart from what might be a getting started tutorial, could mostly be classified as janitorial work.

1.  Triage issues and close those that no longer apply. This is mind numbing work, but it has been almost three years since the last pass.
2.  Move the contents of `numpy/doc` into `doc/source` and make them normal *.rst files.
3.  Convert the doctest in `numpy/lib/tests/test_polynomial.py` to regular tests. Might be tricky as it mostly checks print formatting.