Support GEOS Cube-Sphere Grids

[philipc2]

Closes #801

Overview

• Add support for reading and encoding the GEOS Cube-Sphere format in the UGRID conventions

[philipc2]

Plot of a ~200,000,000 element grid (CS5760)

[philipc2]

Plot of a ~900 element grid (CS12)

[philipc2]

@mathomp4

Let me know if you have any feedback!

[mathomp4]

@mathomp4

Let me know if you have any feedback!

Well, I think I need to learn more Python but I decided to try on a stretched grid file I finally got. I first tried plotting the grid but apparently this:

<uxarray.Grid>
Original Grid Type: GEOS-CS
Grid Dimensions:
  * n_node: 440646
  * n_face: 437400
  * n_max_face_nodes: 4
  * n_nodes_per_face: (437400,)
Grid Coordinates (Spherical):
  * node_lon: (440646,)
  * node_lat: (440646,)
  * face_lon: (437400,)
  * face_lat: (437400,)
Grid Coordinates (Cartesian):
Grid Connectivity Variables:
  * face_node_connectivity: (437400, 4)
Grid Descriptor Variables:

was a bit too much for my laptop. I waited a few minutes and in the end it was just solid blue.

But following some of your examples, I did get out:

which is nice! I then tried a plot.polygons() and my computer hated me again. 😄

I think what this demonstrates is that I need to convince my boss to help make a much coarser stretch grid...or learn more UXarray

[philipc2]

Glad you were able to get it working! Surprised that the Polygon plot took that long. Typically grids of that size should take no longer than a minute maximum for the first run.

[mathomp4]

Glad you were able to get it working! Surprised that the Polygon plot took that long. Typically grids of that size should take no longer than a minute maximum for the first run.

Hmm. Well, it was on an M3 MacBook Air...if that matters?

Note: it's also possible I called things incorrectly. I was just sort of trying to echo the cookbook. 😄

[philipc2]

Glad you were able to get it working! Surprised that the Polygon plot took that long. Typically grids of that size should take no longer than a minute maximum for the first run.

Hmm. Well, it was on an M3 MacBook Air...if that matters?

Note: it's also possible I called things incorrectly. I was just sort of trying to echo the cookbook. 😄

Yeah, I've run similar sized meshes on a 32GB M1 and had reasonable performance. Mind sharing the file so I can try to recreate it?

[mathomp4]

Yeah, I've run similar sized meshes on a 32GB M1 and had reasonable performance. Mind sharing the file so I can try to recreate it?

Yes...if I can figure out how to share it. Can you try:

https://gmao.gsfc.nasa.gov/gmaoftp/gmao_SIteam/test-fixapp-c270.geosgcm_prog_nat.20200415_0000z.nc4

I think that works. If you get it, let me know. It's about 500 MB because I didn't really clean it up

[philipc2]

import uxarray as ux
import geoviews.feature as gf
import cartopy.crs as ccrs

features = gf.coastline(
    projection=ccrs.PlateCarree(), line_width=1, scale="50m"
) * gf.states(projection=ccrs.PlateCarree(), line_width=1, scale="50m")

file_path = r"C:\Users\chmie\Downloads\test-fixapp-c270.geosgcm_prog_nat.20200415_0000z.nc4"

%%time
uxds = ux.open_dataset(file_path, file_path)

Took about 3s

%%time
uxds['T'][0][-1].plot.rasterize(method='polygon', exclude_antimeridian=True) * features

Took about 3s as well.

uxds['RH'][0][0].subset.bounding_circle((-90, 25), 10).plot.rasterize(method='polygon', exclude_antimeridian=True, pixel_ratio=4.0) * features

[philipc2]

I'd like to mention that the white spots are due to an upstream issue with Datashader, which will be fixed in the next release.

https://github.com/holoviz/datashader/pull/1329

[mathomp4]

Hmm. When I first tried your first block of code, I got:

ValueError: No plotting extension is currently loaded. Ensure you load an plotting extension with hv.extension or import it explicitly from holoviews.plotting before applying any options.

So, I added:

import holoviews as hv
hv.extension("bokeh")

And then the plots took about 2s. So...yeah, I must have really screwed things up in my testing to be ~100x slower. 😄

[mathomp4]

And I was wondering about the weird white spots, but I figured they were due to just...something.

Now, my laptop was not happy with:

uxgrid = ux.open_grid(file_path,file_path)

%%time
uxgrid.plot()

Actually, my browser sort of crashed before it could render...

[philipc2]

uxgrid = ux.open_grid(file_path,file_path)

%%time uxgrid.plot()

And I was wondering about the weird white spots, but I figured they were due to just...something.

Now, my laptop was not happy with:

uxgrid = ux.open_grid(file_path,file_path)

%%time
uxgrid.plot()

Actually, my browser sort of crashed before it could render...

The issue here is that you're using open_grid instead of open_dataset. The default plotting for a Grid object is an edge plot of the mesh, which is indeed very slow for any mesh larger than a few thousand elements.

[mathomp4]

Ohhhh. So:

uxds = ux.open_dataset()
uxds.uxgrid.plot()

would be faster? I'll try that tonight on my laptop!

(I'm learning!)

[philipc2]

Ohhhh. So:

uxds = ux.open_dataset()
uxds.uxgrid.plot()

would be faster? I'll try that tonight on my laptop!

(I'm learning!)

Plotting a data variable would be faster, since that performs a raster operation as opposed to a vector plot of the geometries.

Our grid (i.e. only mesh) plotting is all vector based, but our data variables can be plotted as rasters.

# defaults to raster plot
uxds['T'].plot()

# can specifically call a raster
uxds['T'].plot.rasterize(method='polygon')

[mathomp4]

Nice work on this!

Doubly so from me and the GEOS community. Nice to have another package that can read our files without need to do all sort of gymnastics!

[mathomp4]

Woo! Thanks. I eagerly await the next release! 😄