Mailman 3 August 2020 - Matplotlib-users

Declarative interface to matplotlib
by Ram Rachum Aug. 24, 2020

Aug. 24, 2020

Hi everyone, I'd like to ask you a general question, as someone who isn't very experienced with matplotlib. I noticed that matplotlib is very imperative, in contrast to declarative. For example, I wanted to show an `imshow` plot with a colorbar, so I needed to first create the plot, then run `plt.colorbar()`, then show the plot. In more complicated plots, you could be doing 10+ actions one after another to create a plot. I would find a declarative interface to be much more elegant, i.e. one call to create the plot and lots of keyword arguments, some of them nested, to specify all the options. Of course, matplotlib being a very mature project, the current interface must be maintained forever. But, do you know of any such declarative interface to matplotlib? Either as part of the library, or a separate library that uses matplotlib? If such a thing doesn't exist, what would you think about adding one? Thanks, Ram.

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Adding artists without changing the underlying Axes etc.
by Andrew Athan Aug. 15, 2020

Aug. 15, 2020

I have implemented speedups based on the copy_from_bbox() and restore_region() APIs (i.e., blit APIs) in conjunction with an Agg canvas (my destination is actually a file, not screen). I create a bar() plot, then grab the plot with copy_from_bbox(), then I create Line2D() artists and add them using ax.add_line(). Following this, I remove the artists, restore_region() and wash, rinse, repeat(). I'm finding that the process can lead to creep in data to screen transforms, which I guess must be due to bounding box changes when I add lines that may exceed the bbox. That's my theory. Is there a mechanism or recommended methodology to tell artists (and/or the axes they are being added to) not to recalculate anything, so that the underlying plot remains unchanged by the add()? A.

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[ANN] Matplotlib 3.3.1
by Elliott Sales de Andrade Aug. 14, 2020

Aug. 14, 2020

Hi all, We are pleased to announce the release of 3.3.1. This is the first bug fix release of the 3.3.x series. Pre-built wheels are available for most major platforms, and can be installed using `pip install matplotlib==3.3.1`. Other packages may also be available already; please check with your preferred source. We are experimenting with a different method of producing wheels. Please notify us if you run into any issues with them. The 3.3.1 release represents the work of 17 authors over 73 pull requests, and we thank them for their contributions. This release contains several critical bug-fixes: * fix docstring import issues when running Python with optimization * fix |hist| with categorical data, such as with Pandas * fix install on BSD systems * fix nbagg compatibility with Chrome 84+ * fix ordering of scatter marker size in 3D plots * fix performance regression when plotting |Path|s * fix reading from URL in |imread| * fix several regressions with new date epoch handling * fix some bad constrained and tight layout interactions with colorbars * fix use of customized toolbars in TkAgg and WXAgg backends For further details, please see the What's new in Matplotlib 3.3.1 page: https://matplotlib.org/3.3.1/users/whats_new.html and the milestone on GitHub: https://github.com/matplotlib/matplotlib/milestone/56?closed=1 This release is signed by my GPG key. The fingerprint is: 23CA B59E 3332 F94D 26BE F037 8D86 E7FA E5EB 0C10 and it is also used to sign this message.

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