On 03/08/2012 07:16 AM, Benjamin Root wrote: > > > On Thu, Mar 8, 2012 at 10:47 AM, John Hunter <jdh2...@gmail.com > <mailto:jdh2...@gmail.com>> wrote: > > > > On Thu, Mar 8, 2012 at 10:32 AM, Benjamin Root <ben.r...@ou.edu > <mailto:ben.r...@ou.edu>> wrote: > > > +1 as well. I just took another look at the Path object and I > see no such function. The lack of this function is a problem > for me as well in my existing apps. In order to deprecate > nxutils, this functionality needs to be added to Path. > Otherwise, nxutils *must* be reinstated before the next release. > > > Michael has already agreed to make a nxutils compatibility layer > that would have the same interface as the old nxutils. So we are > talking about performance, not core functionality. > > We should remember that Michael did the lion's share of the work on > porting mpl to python 3 > (https://github.com/matplotlib/matplotlib/pull/565/commits). He > elected not to port all of the C++ if he could replace some of the > functionality with the core. So those who rely on bare metal speed > the you are getting in nxutils should step up to either : > > 1) help with the port of nxutils to python 3 > > 2) help with exposing methods in _path.cpp that are almost as fast > or faster > > 3) live with slower speeds in the compatibility layer he has agreed > to write > > 4) ask (nicely) for someone to help you > > I prefer option 2 because this is fairly easy and avoids code > redundancy. It would take just a few lines of extra code to do this > with the python sequence protocol as inputs and python lists as > return values. It would take a bit more to support numpy arrays as > input and output, and we should get input from Michael about the > desirability of making _path.cpp depend on numpy. I don't see the > harm, but I'd like to verify. > > In my opinion, a slower implementation in a > nxutils.py compatibility module is not a release stopper, even if it > is undesirable. > > JDH > > > Don't get me wrong. If help is needed, I can certainly provide it since > it is my itch. I am just a little exasperated with how this issue has > been handled up to now. The shim is a very good idea and it should have > been done back when the py3k merge happened. I didn't press the issue > then because I was traveling and didn't have time to examine the issue > closely, and having _nxutils.so still in my build hide the problem from > me (my own fault). > > As for shim implementation, I would be willing to accept a slightly > slower function now (with the promise of improvements later), but if the > implementation is too much slower, then effort will need to be made to > get it up to acceptable levels. I know of several users **cough**my > future employer**cough** who uses functionality such as this, and they > would not be happy if their products are dragged down by such a bottleneck.
For my own purposes I decided to simply copy the pnpoly core and write a cython wrapper, since nxutils did not seem to have a secure future in mpl. If anyone is interested in this approach, see the files _pnpoly.c and utility.pyx (last function therein) in the num/src subdirectory of my "pycurrents" repo: http://currents.soest.hawaii.edu/hgstage/pycurrents/shortlog/tip. The wrapper is brand-new and very lightly tested; I suspect it could be improved considerably. (Also, at present it has only the points_in_poly function.) > > Probably about time I dug more into CXX wrapped stuff... mpl strategy question: stick with CXX and hand-wrapping exclusively, or start using cython? Eric > > Ben Root > > > > ------------------------------------------------------------------------------ > Virtualization& Cloud Management Using Capacity Planning > Cloud computing makes use of virtualization - but cloud computing > also focuses on allowing computing to be delivered as a service. > http://www.accelacomm.com/jaw/sfnl/114/51521223/ > > > > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users ------------------------------------------------------------------------------ Virtualization & Cloud Management Using Capacity Planning Cloud computing makes use of virtualization - but cloud computing also focuses on allowing computing to be delivered as a service. http://www.accelacomm.com/jaw/sfnl/114/51521223/ _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
