The original notes were taken on etherpad. @mdboom has reorganized them and summarized them here.
Interactive modification of plots would address the issues on tweaking commented on at the plotting competition
Need a way to write out the tweaks "as code" afterward to paste back into a script (or into the IPython notebook)
Igor Pro has a good track record of having 1-1 correspondence between image and code
Veusz can also produce code from a manually tweaked plot
vispy: High performance plotting project (in early days)
We should create a MEP on openGL (and more generally for high performance plotting)
An analysis of performance bottlenecks should be done, perhaps a standard set of benchmarks to track over time (#2188)
in general, this kind of a benchmarking of the fall-off of performance would be useful to point users to.
Experiment with reducing quality during panning/zooming, and then going back to full quality on idle (much like older 3D packages used to show only a bounding box during rotation). (#2189)
We should add built-in instrumentation of matplotlib methods -- this would allow us to collect statistics about performance as well as more easily help users debug problems when they are hard to reproduce. (#2190)
Use something we either rent or control instead of travis in order to collect more data on different platforms.
We need a larger test matrix -- Python, Numpy, GUI framework, Freetype and libpng versions. From this we can keep on top of our version requirements.
In a follow-up breakout session, we thought the minimum requirements would be:
Mac and Linux virtual machines that a number of core developers can log into
Windows virtual machines if feasible
Github integration (like what Travis does)
The ability for other machines with different configurations to publish their results to a public web server
Saving the output of the tests
Saving build products ("daily", or more often)
Auto-building the docs
We have Launchpad daily builds https://code.launchpad.net/~takluyver/+recipe/matplotlib-daily
use notebook output and potentially store the output in the notebook to store the image output in the repo
have an interactive plotting documentation system in which you can edit plots online (Google App engine)
Enthought uses the casuarius constraint solver for their (Enaml)[http://docs.enthought.com/enaml/index.html] layout engine -- steal or borrow?
Tight-layout goes a long way already - thank you jae-joon
See #1109 for earlier (and continued) discussion
There is disagreement about whether constrained layout and plot editor are orthogonal and whether we could pursue both in parallel or should pick one or the other as a focus
We should revive this.
Plot method could take "plottable" objects (rather than just Numpy arrays), and those objects could obtain or generate data in other ways (such as for functional plotting)
This lead to a discussion about 2.0 (i.e. breaking some backward compatibility). However, it was noted that we were a room full of developers, not users.