Mailman 3 February 2016 - Matplotlib-devel

wxMPL status?
by Chris Barker March 11, 2016

March 11, 2016

Hi all, Anyone know what's up with wxMPL? It looks like Ken hasn't updated in soe time, and it doesn't work with recent MPLs: from matplotlib.axes import _process_plot_var_args ImportError: cannot import name _process_plot_var_args We had a discussion about this a good while back: https://sourceforge.net/p/matplotlib/mailman/message/29770257/ anything ever come of that??? -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker(a)noaa.gov

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Changing rcParams locally, on the fly
by Phyks March 9, 2016

March 9, 2016

Hi, Is there a way to change locally the rcParams, for a specific figure after it has been created? I know about the rcParams setting (for global settings) and the with statement construction, but this requires to be done before the figure was created, and I need to change the settings on an already existing figure (in particular color cycler). Thanks -- View this message in context: http://matplotlib.1069221.n5.nabble.com/Changing-rcParams-locally-on-the-fl… Sent from the matplotlib - devel mailing list archive at Nabble.com.

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Comments on backports
by Thomas Caswell Feb. 29, 2016

Feb. 29, 2016

Folks, When doing backports please include the branch you backported the commit to (I have run across several places where I have _not_ done this and it is a bit annoying to sort out after the fact). A quick guide to how to backport (which should go in the docs): git remote update git checkout target_branch git merge --ff-only matplotlib/target_branch git cherry-pick -m TARGET_SHA gitk # to look at it # local tests? (use your judgement) git push DANGER target_branch # leave a comment on PR noting sha of the resulting commit # from the cherry-pick + branch it was moved to here matplotlib is a read-only remote to the matplotlib/matplotlib repo and DANGER in a read/write remote to the matplotlib/matplotlib repo. These commands work on git 2.7.1. Tom

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PR testing is broken?
by Eric Firing Feb. 20, 2016

Feb. 20, 2016

I submitted a PR to master, and to my surprise, neither Travis nor Appveyor started up. Is this a github glitch, or is something broken on our side? Eric

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Re: [Matplotlib-devel] [Matplotlib-users] paletted PNG backend
by Thomas Caswell Feb. 17, 2016

Feb. 17, 2016

Jesper, That is a cool application! I am not sure doing this directly in Agg would be the best place (as I am not sure how to conceptually combine anti-aliasing + compositing + color pallet). I think a better place to extend would be the png writer to directly export a palleted png ( https://github.com/matplotlib/matplotlib/blob/master/src/_png.cpp#L123) from the Agg RBGA buffer. You might also be interested in https://github.com/matplotlib/matplotlib/pull/5389 where Mike did some work to sort out which png settings are fastest. Tom PS The mailing list has moved to matplotlib-devel(a)python.org please re-subscribe and send future emails there. On Mon, Feb 15, 2016 at 3:19 AM Jesper Larsen <jesper.webmail(a)gmail.com> wrote: > Hi Matplotlib users, > > We are using Matplotlib for a web service which makes PNG images on the > fly for presentation on a map (web site using the web service is here: > https://ifm-beta.fcoo.dk) > > Performance and image size are two major concerns for us. We therefore > save the resulting RGBA PNG to a buffer and afterwards use Pillow (PIL) to > convert it to a P PNG (paletted PNG) to reduce the image size dramatically. > > This procedure does however use a significant amount of our total > processing time per image. I would therefore be interested in extending > e.g. the AGG backend to produce paletted PNGs directly. I am of course > aware that this might not be useful for many others since one would have to > provide some extra information when rendering with this backend (possibly > output palette and quantizing method). But on the other hand it might be > useful for others doing web services using matplotlib. > > My questions are: > > 1) Is it possible to extend the AGG backend for this and how? > > 2) Is it better to make a separate Pillow based backend for this (Pillow > is probably not as fast as AGG)? > > Best regards, > Jesper > > > > > ------------------------------------------------------------------------------ > Site24x7 APM Insight: Get Deep Visibility into Application Performance > APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month > Monitor end-to-end web transactions and take corrective actions now > Troubleshoot faster and improve end-user experience. Signup Now! > http://pubads.g.doubleclick.net/gampad/clk?id=272487151&iu=/4140 > _______________________________________________ > Matplotlib-users mailing list > Matplotlib-users(a)lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/matplotlib-users >

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matplotlib limitations and design flaws
by Phillip Feldman Feb. 16, 2016

Feb. 16, 2016

Dear matplotlib developers (and users): I've expanded my web page "Python Limitations and Design Flaws" to include a good-size section on matplotlib (item number 3). The URL is as follows: http://phillipmfeldman.org/Python/Python_Limitations.html All feedback other than flames is welcome! Phillip

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GSoC: Call for mentors
by Michael Droettboom Feb. 16, 2016

Feb. 16, 2016

Calling all developers: matplotlib plans to participate in Google Summer of Code this year under the Numfocus organization. We'd love your help as mentors. The time committment is generally one weekly check-in meeting with your student, as well as extra time at the beginning and end of the summer. It is a great way to multiply our work over the summer and find new contributors long term. I have proposed an initial list of topics here: https://github.com/numfocus/gsoc/pull/72 If you'd like to volunteer to be a mentor for any of these projects or propose your own, please let me know or file a pull request. Mike -- Michael Droettboom Continuum Analytics

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cycler v.0.10.0 released
by Thomas Caswell Feb. 16, 2016

Feb. 16, 2016

Folks, I am happy to announce the next release of Cycler. This will become the minimal version for the upcoming mpl v2.0 release. http://matplotlib.org/cycler/ Feature release for `cycler`. This release includes a number of new features: - `Cycler` objecst learned to generate an `itertools.cycle` by calling them, a-la a generator. - `Cycler` objects learned to change the name of a key via the new `.change_key(old_key, new_key)` method. - `Cycler` objects learned how to compare each other and determine if they are equal or not (`==`). - `Cycler` objects learned how to join another `Cycler` to be concatenated into a singel longer `Cycler` via `concat` method of function. `A.concat(B)` or `concat(A, B)`. - The `cycler` factory function learned to construct a complex `Cycler` from iterables provided as keyword arguments. - `Cycler` objects learn do show their insides with the `by_key` method which returns a dictionary of lists (instead of an iterable of dictionaries). Tom

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Response to architecture query
by Benjamin Root Feb. 11, 2016

Feb. 11, 2016

Excerpt response to a private query about matplotlib's architecture. Some has asked me to post it as a possible addition to development docs. All of matplotlib started off as pure python. As time went on, it was re-worked into a backend/frontend design. The frontend is mostly in python, with critical portions, such as path and vector handling implemented in C++. We are also heavily reliant upon NumPy and use its C-API in those C++ parts. We also call out to various other libraries such as libfreetype to perform our font-handling. The frontend is also a bit of a misnomer, because it isn't really at the front, as most people are familiar with the pyplot interface, which sits on top of the frontend. The backends are what allows us to output the figures to the many different "devices" (filetypes, GUI toolkits, etc.). Most are implemented in python using the respective GUI toolkit's python bindings. The Tk backend, oddly enough, is implemented in C/C++ (don't ask me why, I haven't a clue), and the macosx backend is implemented in Obj-C. The AGG backend is implemented in C++. PDF and PS backends are implemented in python as well. AGG is critically important to matplotlib's functionality. Not only does it serve as the default headless backend, and is often mixed with the GUI backends (TkAgg, Qt4Agg, etc.) to produce consistent results across all platforms, it also serves as the basis for our image-handling architecture. This is a very broad-brush overview of the matplotlib architecture. It isn't a simple layered system, unfortunately, but we do have some critical portions off-loaded to C/C++ or NumPy. Probably some of the most difficult-to-understand portions of the codebase isn't those parts, though. It is the figure/axes/axis relationships for defining properties at the right time (lots of multiple-inheritance confusion) as well as the figure/canvas/manager relationships for interactivity. Cheers! Ben Root

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