xarray

pydata/xarray#5865 (reply in thread)

I wonder if it's possible to implement a built-in function like:
da.str.format("%.2f") or xr.string_format(da, "%.2f)

To wrap:

import xarray as xr

da = xr.DataArray([5., 6., 7.])
das = xr.DataArray("%.2f")
das.str % da

<xarray.DataArray (dim_0: 3)>
array(['5.00', '6.00', '7.00'], dtype='<U4')
Dim

One option to check for duplicate keys in the YAML files loaded/used by Satpy would be to add a custom constructor/loader as described in this gist:

https://gist.github.com/pypt/94d747fe5180851196eb

This wouldn't help the pre-commit in this PR, but at least the pre-commit is checking syntax.

_Originally posted by @djhoese in pytroll/satpy#1935 (comment)

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

This should be updated to use write_cog instead:

Basic analysis:

Change detection:

The docstring doesn't show it

Oops wrong tag.

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

Example for numerical weather prediction

to be added to initialised datasets

Data sources (to) implement(ed):

• GEFS https://www.ncei.noaa.gov/thredds/catalog/model-gefs-003/202008/20200831/catalog.html
• DWD https://opendata.dwd.de/weather/nwp/

relates to #600

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

To do

• Climatological means of net primary productivity
• Sentinel-2 / Landsat example
• Integration with geemap
• Temporal smoothing (#31)

Complete

• Downloading MODIS imagery
• [Visualizing fire progressions from GOES-R hotspots](https://wxee.readthedocs.io/en/latest/

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

da = xr.DataArray(np.ones(2,3), dims=("x", "y"))

This kind of dataarray exists in CMIP datasets. x,y have absolutely no data (no values, no attrs) associated with them, and so guess_coord_axis does not do anything.

We could have .cf.guess_coord_axis(add_indexes=True) that effectively does

da["x"] = np.arange(da.sizes["x"])
da["y"] = np.arange(da.sizes["y"])

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?