Sample Graphic Notebook

[michaelavs]

@dopplershift
wrfout file is in this PR and environment has been updated to include wrf-python and netcdf4 dependencies. I think the majority of my issues are coming from imshow and incorporating a png file with cartopy projections.

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[brian-rose]

Hi @michaelavs, if you merge in the latest updates from main branch, it should fix the build failures here.

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[michaelavs]

I wanted some input on the file used in the python file to create the gif. The subsetted version of it is 409MB which is over the size that GitHub recommends/allows. I'm not sure if the code to create the gif is going to be built or not on the foundations website, if it isn't (either because it will be an inactive code or just not included), then there isn't really an issue. But if the code will be built/run, then I'd like to make sure that there won't be issues with the data file being the size it is.

Also, definitely looking for feedback on this option for the graphic. It's the third rendition of a graphic for the front page and is a bit different than what was discussed in the other PR/issue for creating the graphic. This dataset shows precipitation data for the timeframe of the Boulder floods (with a few additional days to add time to the gif loop). I think it helps to showcase a fun thing that can be done with Python for data visualization and goes with the goal of Project Pythia with respect to educating scientists on how to use Python.

[dopplershift]

@michaelavs Is there somewhere I can get to that file? I'd love to see if there's anything else to be done to try to reduce the data size.

[michaelavs]

@dopplershift
The data set can be found here: https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1328
Then scroll down a little bit to "Data Files", add the filter of "prcp_2013", and the original dataset is "daymet_v3_prcp_2013_na.nc4" (1.1GB).
I believe you will need an EarthData account to download it.

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[dopplershift]

Ok, managed to get the dataset down to 99M with some more subsetting from THREDDS and local rechunking for better compression. I've submitted a PR to the pythia-datasets repo to add the file.