xarray

Noting down a comment by @DanJonesOcean on Twitter: https://twitter.com/DanJonesOcean/status/1441392596362874882

In general, having more examples on each xarray page (like the one below) would be good. Then they would come up quickly in function searches:

http://xarray.pydata.org/en/stable/generated/xarray.Dataset.merge.html#xarray.Dataset.merge

Our API docs are generated by the func

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

The docstring doesn't show it

Oops wrong tag.

This should be updated to use write_cog instead:

Basic analysis:

Change detection:

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

Is your feature request related to a problem? Please describe.
asv performance now depends on local machine performance

Describe the solution you'd like
Independent of my laptop

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

Describe alternatives you've considered
keep as is

In determining the correct reader for the file provided we currently have two options (as of #224).

1. Providing reader param to AICSImage (i.e. img = AICSImage("s3://some-file.ext", reader=readers.lif_reader.LifReader)
2. Not providing a reader, and AICSImage looping over all SUPPORTED_READERS.

Option 1 is the fastest + safest method for loading a file into AICSImage (without using

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.

• Downloading MODIS imagery
• Visualizing fire progressions from GOES-R hotspots
• Climatological means of net primary productivity
• Sentinel-2 / Landsat example
• hvplot for animated time serires
• Integration with eemont
• Integration with geemap
• Clima

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?