> > This is a bit of an attitude issue that got me "off". The matplotlib > maintainers have no such obligation.
Neither does Anaconda. Please stop, this whole discussion is ridiculous, especially the "forking" nonsense. Conda will update their Matplotlib version in good time. Dealing with large dependency graphs of binary dependencies is a thankless pain in the ass [1]. Keep in mind that they are giving all of this away *for free*. [1] (seriously, unless you've actually built Python and the whole SciPy binary dependency stack from scratch on Windows, you have absolutely no business writing philosophical rants about any of this) On Mon, Nov 2, 2015 at 11:24 PM, <[email protected]> wrote: > Thanks for the follow-up. Again, I understand the convenience benefit of > the self-contained conda.jl Python. My concern is about where it leads > practically. Sorry to bring up the ideology stuff. YMMV. > > As a practical matter, if Continuum were much faster to post updates to > their repo at Anaconda.com this might be less of a problem. On the conda > group, someone requested a more upgraded matplotlib (this was from some > time ago and the requester wanted 1.4) and the Continuum reply was that the > matplotlib maintainers were free to also post their latest to what was then > called Binstar.com. This is a bit of an attitude issue that got me "off". > The matplotlib maintainers have no such obligation. They have a master > branch on their github repo and they release to PyPi. If Continuum wants > to create their own binary as a service, of course they may--but it's on > them to do so. > > There is now a bug in 1.4.3 with Numpy 1.10 that results in this message: > > FutureWarning: elementwise comparison failed; returning scalar instead, > but in the future will perform elementwise comparison > > This was fixed by matplotlib 1.5.0 about 2 weeks ago (along with other > things of course). Not that long ago and a little bit of a lag as mutual > dependencies get their issues sorted out isn't unreasonable. But, they > have and the master branch is fully released. > > This is hard for Julia and I was too harsh. With large dependencies there > is no totally easy way out. Until a week ago I was happily using > versions of Python I downloaded myself. PyCall was finding them and all > was good. I ran into one annoying bug after upgrading matplotlib (first > chart figure cannot be closed by code) so I was trying to sort that out. > Never figured out what broke. So, I switched to the conda.jl approach and > moved on. And then there was the latest... ...which lead to my post. >
