pyvista-xarray
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pyvista
Example with global data in spherical projection
https://www.ncei.noaa.gov/products/optimum-interpolation-sst
oisst-avhrr-v02r01.19810901.nc.zip
import xarray
ds = xarray.open_dataset('oisst-avhrr-v02r01.19810901.nc')
ds.err.plot()
The tricky part is performing the 3D coordinate transform - this can get very complicated very quickly and this library's goal is not to perform coordinate transforms.
I'm going to see what I can do to use existing tools to do this.
had a great chat with @bjlittle about using geovista to this end - he's created some fantastic functionality in that library to handle all of the coordinate transforms, and I think with a little bit of work, we can leverage geovista's transformer/bridge to handle the 3D reprojections here.
ref https://geovista.readthedocs.io/en/latest/ and https://github.com/bjlittle/geovista
We will follow up in a few weeks
@banesullivan I've made some time to spin back up and show some love to geovista.
I've bolstered the README.md in the short-term until I get space/time to fully bootstrap the docs for the 0.1.0 release.
However, you'll be glad to hear that I've also made most of the geovista/examples self-contained, so that they are agnostic of SciTools/iris and automatically download and cache the required geovista sample data that they require. I still have a couple more examples to self-contain, which I'm looking at now, but I'll let you know when I've done that :+1:
There are still a few major wrinkles to iron out prior to the 0.1.0 release, but I'm also super keen to start thinking about providing support for rectilinear/uniform/structured pyvista grids to underpin your pyvista-xarray use case. That would be pretty awesome.
@banesullivan BTW the above NOAA/NCEI OISST AVHRR sst dataset looks like this:
Which looks rather lush. I also added the dataset as a GeoVista pantry sample, and highlighted it on the README and also created an example, see example_from_1d__oisst.py.