Hi Jeremy, I'm the matplotlib author -- I'm writing under a different email address since my email server seems to be dying...
I'll start by saying that I for one won't criticize you for rolling you own plotting package rather than join forces with an existing project. I've been accused of the same, on more than one occasion :-) But I'm also aware of the problem that this creates -- the tyranny of choice. python is so fun to code in that many developers are looking for a reason to find an existant package inadequate so they have an excuse to write their own replacement. Hence we have a proliferation of web-app frameworks, plotting packages, array objects and so on. There is a lot of duplicated effort in many arenas and it would be nice to collaborate more. I read over your scipy list of problems that you found in matplotlib -- some were helpful and some, as you note, have been long fixed. One critique you might flesh out for me is the notion that matplotlib's object model is baroque -- most of the developers feel the object model is fairly solid. You weren't by chance, trying to use the procedural pylab (aka matlab) interface, were you, since pylab itself is just a wrapper of the OO matplotlib API? One area in the object model that we plan to change is to make high level plot objects (scatter, error, histogram) etc, proper objects, ala gnuplot. Right now they are methods that generate primitive objects (lines, rectangles, text, etc). Another area you identify as a problem with matplotlib is the need to regenerate the entire graph when one property is changed. This is true in one way and false in another. matplotlib does have a proper object model -- eg, you can selectively change the facecolor of a marker w/o regenerating the graph scene. But the *drawing* hierarchy (as opposed to the object hierarchy) needs some work . Once you have changed a property, the entire graph is redrawn. This is a known limitation and will change in the not-too-distant-future. One advantage of working in mainstream in open source software is the network effect. With 10 some-odd developers including institutions such as the U of C, STScI, JPL and NOAA, bugs and limitations of matplotlib tend to be fixed within minutes, days, weeks or months. Your package looks very nice. It specifically addresses two limitations in matplotlib that we would like to address -- a GUI interface for creating figures and a way to save the figure at any point as a high level description (rather than an image). I do wish you had publicly voiced the problems you ran into along the way; I just searched the archives and saw only one post from you on the users list which I answered 28 minutes later with http://sourceforge.net/mailarchive/message.php?msg_id=10124206 after which I never heard from you again. Such response times are fairly typical on the list, but if you don't report the bugs and follow up on the suggested fixes, we can't fix them. Anyway, nice work on veusz. Are you committed to the GPL license? matplotlib uses a more permissive license (PSF compatible) mainly to encourage contributions from the commercial sector. As you suggest, it is still possible for someone to take the work you've done on the GUI frontend and expose matplotlib as a backend based on your prior experiments. The NASA Jet Propulsion Laboroatory and others are supporting the QT backend, and they might be willing to contribute some time to porting your GUI frontend to matplotlib, provided the license of veusz was compatible with the mpl license. There are two specific issues you raised in the scipy post you linked to that I'll take issue with: >> In my experience, the plotting code isn't that hard to do. >> The hardest part was making the axes look good. In my experience, the hardest part of plotting code is supporting all the ways in which people want to use the code, and that includes embedding the plot in their GUI of choice, working across platforms, supporting script based, interactive shell work, application developers, web app developers and so on. There are quite a few plotting idioms that are mission critical to many developers, and supporting them all is a complex task. I'm fond of pointing out the combinatorial problem -- take every GUI, cross two major versions in the wild, cross the big-three platforms, cross the standard ways in which people want to work with their plots and you have a lot of work to do. veusz "solves" this problem by restricting choice, which is a respectable solution. matplotlib takes the opposite tack and tries to support the user in the environment in which they want to work. The second point is >> I've done 90% of what's needed there. Basically, I'll be happy >> when contouring and images are added. I tend to disagree that contouring and images are only 10% -- good contouring, contour labeling, and image support strong enough to satisfy people for whom images are their lifeblood is hard, and almost impossible to do efficiently in pure python. matplotlib isn't there yet, but the support for both is pretty strong and getting better, and matplotlib is increasingly being used by astronomers for image display. But I see from your web page that you too are an astronomer, so I expect that you will handle these areas nicely; again, too bad we couldn't collaborate more. Another tough area to do right of course is nonlinear transformations (polar, log, mapping projections) with the associated problems of proper tick locating and labeling... Cheers, JDH -- http://mail.python.org/mailman/listinfo/python-list