Add very minimal xarray plugin for engine="rasterio"

[alexamici]

Use xarray plugin infrastructure to add support for:

>>> import xarray as xr
>>> xr.open_dataset("myfile.jp2", engine="rasterio")
<xarray.Dataset>
Dimensions:      (x: 19087, y: 3932)
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 ...
  * x            (x) float64 ...
    spatial_ref  int64 0
Data variables:
    band_data     (band, y, x) float32 ...

also add automatic engine selection for files with .tif and .geotif extensions.

>>> xr.open_dataset("myfile.tif")
<xarray.Dataset>
Dimensions:      (x: 19087, y: 3932)
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 ...
  * x            (x) float64 ...
    spatial_ref  int64 0
Data variables:
    band_data        (band, y, x) float32 ...

• Closes ENH: Add xarray entrypoint #197
• Tests added
• Fully documented, including docs/history.rst for all changes and docs/rioxarray.rst for new API

[codecov]

Codecov Report

Merging #281 (511d99e) into master (74c2c0f) will decrease coverage by 1.08%.
The diff coverage is 0.00%.

❗ Current head 511d99e differs from pull request most recent head 18d7bd8. Consider uploading reports for the commit 18d7bd8 to get more accurate results

@@            Coverage Diff             @@
##           master     #281      +/-   ##
==========================================
- Coverage   93.68%   92.59%   -1.09%     
==========================================
  Files          12       13       +1     
  Lines        1362     1378      +16     
==========================================
  Hits         1276     1276              
- Misses         86      102      +16     

Impacted Files	Coverage Δ
rioxarray/xarray_plugin.py	0.00% <0.00%> (ø)

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[snowman2]

Use xarray plugin infrastructure to add support for:

>>> import xarray as xr
>>> xr.open_dataset("myfile.jp2", engine="gdal")
<xarray.Dataset>
Dimensions:      (x: 19087, y: 3932)
Coordinates:
  * y            (y) float64 ...
  * x            (x) float64 ...
    spatial_ref  int64 0
Data variables:
    band1        (y, x) float32 ...
Attributes:
    scale_factor:              1.0
    add_offset:                0.0
    grid_mapping:              spatial_ref

also add automatic engine selection for files with .tif and .geotif extensinos.

>>> xr.open_dataset("mygile.tif")
<xarray.Dataset>
Dimensions:      (x: 19087, y: 3932)
Coordinates:
  * y            (y) float64 ...
  * x            (x) float64 ...
    spatial_ref  int64 0
Data variables:
    band1        (y, x) float32 ...
Attributes:
    scale_factor:              1.0
    add_offset:                0.0
    grid_mapping:              spatial_ref

One thing I am noticing in these examples is that the attributes for the band data variables are on the Dataset. If you go this route, it would be important to make sure that those attributes are pushed to the DataArray.

[alexamici]

One thing I am noticing in these examples is that the attributes for the band data variables are on the Dataset. If you go this route, it would be important to make sure that those attributes are pushed to the DataArray.

You are right, I used .to_netcdf and started looking for fix-ups, but I missed the fact that array attributes where promoted to global attributes.

Anyway the whole PR is work in progress. If you like the idea we can discuss the direction to go here, or in the main issue.

[alexamici]

@snowman2 The current implementation is just to see where I'd be aiming following pydata/xarray#2844

>>> import xarray as xr
>>> xr.open_dataset("test/test_data/input/cog.tif", decode_coords="coordinates")  # or decode_coords=True
<xarray.Dataset>
Dimensions:      (band: 1, x: 500, y: 500)
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 2.715e+06 2.714e+06 ... 2.565e+06 2.565e+06
  * x            (x) float64 1.635e+06 1.635e+06 ... 1.784e+06 1.784e+06
Data variables:
    spatial_ref  int64 ...
    band_data    (band, y, x) int16 ...

>>> xr.open_dataset("test/test_data/input/cog.tif", decode_coords="all")
<xarray.Dataset>
Dimensions:      (band: 1, x: 500, y: 500)
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 2.715e+06 2.714e+06 ... 2.565e+06 2.565e+06
  * x            (x) float64 1.635e+06 1.635e+06 ... 1.784e+06 1.784e+06
    spatial_ref  int64 ...
Data variables:
    band_data    (band, y, x) int16 ...

I didn't manage to add the "grid_mapping": "spatial_ref" to band_data.encoding yet, but once we do it the resulting netCDF will be the same in both cases.

The best course of action would be to implement decode_coords="all" in open_rasterio though.

[alexamici]

@snowman2 I'll close the PR for now, waiting for a resolution of #282. Thanks for your attention!

[snowman2]

@alexamici are you aware of the target release date for xarray 0.18?

[snowman2]

@alexamici am I good to squash and merge? (rasterio 1.2.2 was just released and looks like it is breaking things, so I am not worried about test failures related to this PR).

[alexamici]

Release should be "soonish", but we have no target date to my knowledge.

Anyway the changes are totally backward compatible, since che xarray_plugin.py is not imported in older version of xarray and will only be imported by upcoming releases.

[alexamici]

You can merge now for me.

[snowman2]

Thanks @alexamici 👍