Week #8: Support keepdims in numpy mean, hunt potential algorithms to be improved

What did you do this week?

• ENH: improve siegelslopes via pythranClean code, all checks passed.
• ENH: improve cspline1d, qspline1d, and relative funcs via Pythran Only improve the private funcs, has passed all the checks. However, find a potential problem: array assignment res[cond1] = ax[cond1] works fine for int[] or float[] or float[:,:] but not int[:,:]
• WIP: support keepdims in numpy mean It passed all the checks after I changed to use str(node.value).lower(). I added tests for keepdims=False but there are some check failures.
• ENH: improve _cplxreal, _falling_factorial, _bessel_poly, _arc_jac_sn… This enhancement is little and seems so meaningless that I opened the PR only in my own repo: they are already fast algorithms. Now I got stuck in finding potential algorithms to improve: often spending ~10 hrs to find algorithms, ~2hr to improve them.

What is coming up next?

Since it is not easy to find good algorithms anymore and we've already improved some, it is time to change the plan. Therefore, I will work on:

• Use Pytest and Decorator to support different dype input testing for Pythran imporved functions.
• Revisit the algorithms we worked, get a final conclusion maybe.
• Finish supporting keepdims in numpy mean in Pythran

Did you get stuck anywhere?

Stuck in supporting keepdims in numpy mean in Pythran and finding potential algorithms.