On Fri, Feb 17, 2012 at 11:52 AM, Eric Firing <[email protected]> wrote:
> On 02/17/2012 05:39 AM, Charles R Harris wrote: > > > > > > On Fri, Feb 17, 2012 at 8:01 AM, David Cournapeau <[email protected] > > <mailto:[email protected]>> wrote: > > > > Hi Travis, > > > > On Thu, Feb 16, 2012 at 10:39 PM, Travis Oliphant > > <[email protected] <mailto:[email protected]>> wrote: > > > Mark Wiebe and I have been discussing off and on (as well as > > talking with Charles) a good way forward to balance two competing > > desires: > > > > > > * addition of new features that are needed in NumPy > > > * improving the code-base generally and moving towards a > > more maintainable NumPy > > > > > > I know there are load voices for just focusing on the second of > > these and avoiding the first until we have finished that. I > > recognize the need to improve the code base, but I will also be > > pushing for improvements to the feature-set and user experience in > > the process. > > > > > > As a result, I am proposing a rough outline for releases over the > > next year: > > > > > > * NumPy 1.7 to come out as soon as the serious bugs can be > > eliminated. Bryan, Francesc, Mark, and I are able to help triage > > some of those. > > > > > > * NumPy 1.8 to come out in July which will have as many > > ABI-compatible feature enhancements as we can add while improving > > test coverage and code cleanup. I will post to this list more > > details of what we plan to address with it later. Included for > > possible inclusion are: > > > * resolving the NA/missing-data issues > > > * finishing group-by > > > * incorporating the start of label arrays > > > * incorporating a meta-object > > > * a few new dtypes (variable-length string, > > varialbe-length unicode and an enum type) > > > * adding ufunc support for flexible dtypes and possibly > > structured arrays > > > * allowing generalized ufuncs to work on more kinds of > > arrays besides just contiguous > > > * improving the ability for NumPy to receive JIT-generated > > function pointers for ufuncs and other calculation opportunities > > > * adding "filters" to Input and Output > > > * simple computed fields for dtypes > > > * accepting a Data-Type specification as a class or JSON > file > > > * work towards improving the dtype-addition mechanism > > > * re-factoring of code so that it can compile with a C++ > > compiler and be minimally dependent on Python data-structures. > > > > This is a pretty exciting list of features. What is the rationale for > > code being compiled as C++ ? IMO, it will be difficult to do so > > without preventing useful C constructs, and without removing some of > > the existing features (like our use of C99 complex). The subset that > > is both C and C++ compatible is quite constraining. > > > > > > I'm in favor of this myself, C++ would allow a lot code cleanup and make > > it easier to provide an extensible base, I think it would be a natural > > fit with numpy. Of course, some C++ projects become tangled messes of > > inheritance, but I'd be very interested in seeing what a good C++ > > designer like Mark, intimately familiar with the numpy code base, could > > do. This opportunity might not come by again anytime soon and I think we > > should grab onto it. The initial step would be a release whose code that > > would compile in both C/C++, which mostly comes down to removing C++ > > keywords like 'new'. > > > > I did suggest running it by you for build issues, so please raise any > > you can think of. Note that MatPlotLib is in C++, so I don't think the > > problems are insurmountable. And choosing a set of compilers to support > > is something that will need to be done. > > It's true that matplotlib relies heavily on C++, both via the Agg > library and in its own extension code. Personally, I don't like this; I > think it raises the barrier to contributing. C++ is an order of > magnitude more complicated than C--harder to read, and much harder to > write, unless one is a true expert. In mpl it brings reliance on the CXX > library, which Mike D. has had to help maintain. And if it does > increase compiler specificity, that's bad. > This gets to the recruitment issue, which is one of the most important problems I see numpy facing. I personally have contributed a lot of code to NumPy *in spite of* the fact it's in C. NumPy being in C instead of C++ was the biggest negative point when I considered whether it was worth contributing to the project. I suspect there are many programmers out there who are skilled in low-level, high-performance C++, who would be willing to contribute, but don't want to code in C. I believe NumPy should be trying to find people who want to make high performance, close to the metal, libraries. This is a very different type of programmer than one who wants to program in Python, but is willing to dabble in a lower level language to make something run faster. High performance library development is one of the things the C++ developer community does very well, and that community is where we have a good chance of finding the programmers NumPy needs. I would much rather see development in the direction of sticking with C > where direct low-level control and speed are needed, and using cython to > gain higher level language benefits where appropriate. Of course, that > brings in the danger of reliance on another complex tool, cython. If > that danger is considered excessive, then just stick with C. > There are many small benefits C++ can offer, even if numpy chooses only to use a tiny subset of the C++ language. For example, RAII can be used to reliably eliminate PyObject reference leaks. Consider a regression like this: http://mail.scipy.org/pipermail/numpy-discussion/2011-July/057831.html Fixing this in C would require switching all the relevant usages of NPY_MAXARGS to use a dynamic memory allocation. This brings with it the potential of easily introducing a memory leak, and is a lot of work to do. In C++, this functionality could be placed inside a class, where the deterministic construction/destruction semantics eliminate the risk of memory leaks and make the code easier to read at the same time. There are other examples like this where the C language has forced a suboptimal design choice because of how hard it would be to do it better. Cheers, Mark > Eric > > > > > Chuck > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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