What did you do this week?

• Finished General implementation of supporting immediate arguments
• GSoC submission: blog
• Merge some cases WIP: TST: add tests for Pythran somersd

What is coming up next?

• ENH: optimize min max and median scipy.stats.binned_statistic by another contributor His performance is even better than our Pythran improvement( ENH: improved binned_statistic_dd via Pythran) He did vectorization improvement and reduced two loops into one. Maybe we can try Pythran again based on his implementation?
• Finish WIP: TST: add tests for Pythran somersd

What did you do this week?

• Support boolean arguments in numpy unique
• General implementation of supporting immediate arguments
• WIP: TST: add tests for Pythran somersd

What is coming up next?

• Finished General implementation of supporting immediate arguments
• Investigate more on WIP: TST: add tests for Pythran somersd
• Prepare for the final evaluation

Did you get stuck anywhere?

In General implementation of supporting immediate arguments, I met a AttributeError: 'FunctionDef' object has no attribute 'immediate_arguments', the potential solution is hard-code checking if it is FunctionDef object, if so then skip.

What did you do this week?

• WIP: TST: add tests for Pythran somersd
• WIP: support keepdims in numpy mean
• Revisit and summarize the unsuccessful PRs about Pythran in SciPy

PR	Reason
ENH: Pythran implementation of _compute_prob_outside_square and _compute_prob_inside_method to speedup stats.ks_2samp	Failed some tests before but works now
ENH: Pythran implementation of _cdf_distance	Pythran version is slightly better than the Python one after fixing np.searchsorted. Could be better after SciPy began to use XSIMD. Hold it for now.
WIP: ENH: improve _count_paths_outside_method via pythran	Relates to bus error on Mac but works fine on Linux for _count_paths_outside_method pythran version
WIP: ENH: improve sort_vertices_of_regions via Pythran and made it more readable	Test Failures: 1) Test_spherical_voronoi: inplace sort 2) Test_region_types: The specified input regions type is int64 list list. When the element in self.regions is numpy.int64, Pythran will automatically turn it to int type

What is coming up next?

• WIP: TST: add tests for Pythran somersd Keep working on this
• WIP: support keepdims in numpy mean : make it more general
• Test XSIMD for_cdf_distance

Did you get stuck anywhere?

Stuck in supporting keepdims in numpy mean in Pythran, thanks to Serge, he helped me fixed many problems and this will be completed this week.

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.

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
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Did you get stuck anywhere?

While supporting keepdims in numpy mean, I added a function mean(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))