xarray

As far as I know, there's no way to see what options are enabled.

It'd be nice to add either xr.get_options or xr.show_options or xr.options so that users can see what is enabled.

I think it would be nice to have a one-line description under each option

"arithmetic_join": "outer"
    controls DataArray/Dataset alignment in binary operations.

Describe the bug
According to the multiscene documentation, the property all_same_area does:

Determine if all contained Scenes have the same ‘area’.

However, I have created a multiscene where all scenes have the same area (they just differ between datasets), yet the property returns Fa

https://earthenginepartners.appspot.com/science-2013-global-forest

Code Sample, a minimal, complete, and verifiable piece of code

from pyresample.boundary import Boundary
b = Boundary(my_lons, my_lats)
print(b.contour_poly.area())

Problem description

The above code doesn't fail if the provided lons/lats are 2D (not sure on 3D+), but the class and all functions/utilities underneath it assume 1D arrays. The end results are incor

https://carpentries-incubator.github.io/geospatial-python/ is a good course that uses rioxarray. Could add a link to it in https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html

This should be updated to use write_cog instead:

Basic analysis:

Change detection:

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Describe the solution you'd like

https://labs.quansight.org/blog/2021/08/github-actions-benchmarks/

Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've c

Without thinking I put resampling="bilinear" and got an error when I called .compute()

Traceback (most recent call last):
  File "carajas.py", line 92, in <module>
    band_medianNP = band_median.compute()
  File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/xarray/core/dataarray.py", line 899, in compute
    return new.load(**kwargs)
  File "/home/ubuntu/anaco

Looping over the data_vars in fit/predict/transform/... should be done with parallel.

This should use the __getitem__ logic and return None if __getitem__ files so that the two are consistent (as expected from dicts and other mappings)

It's really impressive how much pyomeca has progressed! Have you ever considered including some form of simple 3D marker visualization?