issue_comments: 407546587

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https://github.com/pydata/xarray/issues/2288#issuecomment-407546587 https://api.github.com/repos/pydata/xarray/issues/2288 407546587 MDEyOklzc3VlQ29tbWVudDQwNzU0NjU4Nw== 8699967 2018-07-24T20:47:23Z 2018-07-24T20:48:17Z CONTRIBUTOR

In your own projects and use of raster-like data, do you ever deal with non-uniform/non-projected data?

I have dealt with non-uniform data in the geographic projection. I have found it easiest to deal with it if you can determine the original projection and project the coordinates back to that projection so it is uniform. But, I am by no means an expert in this arena. Most if the time I work "normal" data.

How do you prefer to handle/store individual lon/lat values for each pixel?

rasterio/GDAL/QGIS all seem to use the centroid.

Also it looks like xarray would have to be updated to add the "crs" coordinate since currently it is not considered a coordinate variable. So a new library may need to have custom to_netcdf/open_dataset methods, right?

Actually, it is not difficult to add as it stands: python ds.coords['crs'] = 0 ds.coords['crs'].attrs = dict(spatial_ref="PROJCS["UTM Zone 15, Northern Hemisphere",GEOGCS["WGS 84",D...") But, if a crs does not already exist on the dataset, I guess that a function that adds the crs properly would be useful so it can also add the grid_mapping to all of the variables.

Example: python ds.geo.set_crs("+init=epsg:4326")

I think that minor modifications will be needed once the crs is set properly on the xarray dataset. Because after that, the to_netcdf() will automatically produce georeferenced datasets.

I could see the first pass of the extension/library simply performing: 1. ds.geo.crs 2. ds.geo.set_crs() 3. ds.geo.to_projection()

 
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