xarray

Is your feature request related to a problem? Please describe.
Sometimes you want to check that data values are present in another array, but only up to a certain tolerance.

Describe the solution you'd like
da.isin(test_values, tolerance=1e-6), where the tolerance argument is optional.

Not sure what the implementation should be but there are two vectorized [suggestions here](http

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.
Docstrings are hard to keep uptodate and are repetitive.

Describe the solution you'd like
Some templating, functions, etc.

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

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)

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

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