SkewT Updates #1180
SkewT Updates #1180
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dopplershift
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This will be a nice update to SkewT. Only question is is there a way to add an image test for the default aspect ratio with matplotlib >= 3.2. I know this would require adding the dev version of matplotlib to Travis. That's the only issue with codecov right now.
If you don't want to do this, then I think you have to merge, since I can't merge over required statuses.
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Probably should go ahead and add the test and mark it to skip on matplotlib < 3.2. I'll take a quick crack at updating with matplotlib git to see what that does. |
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Makes sense. At least having the test in there for when 3.2 is released would be worth it, even if we don't have it actually run on Travis right now. |
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I'm gonna skip worrying about testing with matplotlib master right now, because the version on that (and on the v3.2.x branch) is 3.1.1.post2178+g9461967b9. There's no sensible way to make our version checking pass for that and not pass for future bugfix releases on the 3.1.x branch, which won't have the aspect support for log scaling. |
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I've added the test, though it won't currently help with coverage until we can run something from 3.2. This test does capture the testing I've done to see how well we match the NWS SkewT diagram PDF--which at this point matches very well (once the proper rotation is set). |
This has the impact of making a blank SkewT much more useful. Refactor SkewT to set the log scaling, and appropriate ticking, at init time. Also set default plot ranges to improve UX and avoid matplotlib problems with log scaling + default limits + linear ticking (results in millions of ticks). This leaves plot() as a simple wrapper around matplotlib's plot now, and it doesn't set any other state. That seems like a big improvement.
Now that we have our transform figured out better, we can leverage matplotlib's support for locking down the aspect ratio (though this only works with log scaling on Matplotlib >= 3.2). This adds an option to control it and sets a default value that matches NWS plots.
This helps ensure that data values are correctly plotted against our default plot ranges. it also makes sure that these units are always set for other plot methods.
Specifying a default aspect ratio will break all these tests on Matplotlib 3.2, so go ahead and manually revert back to the old behavior for aspect.
Needed now that default/blank SkewT has more applied to it.
This will only really pass on matplotlib >= 3.2, so it sets a really large tolerance on other versions so that the code at least executes. This test also checks what a blank plot with no plots looks like.
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Well, I thought I was about to merge, but I accidentally left text on the test image for the aspect ratio. I've fixed that and went ahead and included my fix for #1176. |
When we moved to doing the tickon, etc. equivalent at draw time, that broke when saving figures with bbox_inches='tight', because that doesn't go through draw(). Hook into the (private) method that determines the tick bboxes.
Description Of Changes
plot()now wraps matplotlib'splot()rather thansemilogy().set_adjustable) to tweak the data limits instead.Checklist