What did you do this week?
- [Merged] Review the PR DOC: clarify meaning of rvalue in stats.linregress
- Document ENH: improve _sosfilt_float via Pythran
- Leave the validation in the Python func: ENH: improve siegelslopes via pythran
- ENH: improve cspline1d, qspline1d, and relative funcs via Pythran
- In this case, I left cspline1d, qspline1d, cspline1d_eval, qspline1d_eval public function and doc in Python
- How about 'cubic' and 'quadratic'? They also seem to be a public function.
- Need to check if we need to support more types even if passes checks
- WIP: support keepdims in numpy mean
What is coming up next?
- Keep working on 3./4./5. mentioned above. Merge them hopefully
- Find more potential algorithms and improve them
- Completed BENCH: add more benchmarks for inferential statistics tests
Did you get stuck anywhere?
While supporting keepdims in numpy mean, I added a functionmean(E const &expr, types::none_type axis, dtype d, std::true_type keepdims) , but I'm not sure how can I declare the
return for this function . I think we need to calculated the out_shape so we can -> decltype(numpy::functor::asarray{}(sum(expr) / typename dtype::type(expr.flat_size())).reshape(out_shape))