html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/790#issuecomment-197601132,https://api.github.com/repos/pydata/xarray/issues/790,197601132,MDEyOklzc3VlQ29tbWVudDE5NzYwMTEzMg==,10050469,2016-03-16T23:22:10Z,2016-03-16T23:22:10Z,MEMBER,"Hi @jhamman , this is close to how I would've done it, but I am maybe not the most qualified (probably the gis specialists from rasterio would be more helpful). But still, a couple of remarks from my side:
- I wouldn't necessarily do the `try_to_get_latlon_coords` systematically. When the raster coords are lat-lon, the new coords are redundant. And when the coords are x-y, the lat-lon info are only partly useful (since the grid will be unstructured in lat-lon). Furthermore, I am not sure if `+init=EPSG:4326` is the only lat-lon proj available (there are surely more - at least if you leave the wgs-84 area)
- as mentioned by perrygeo in your rasterio post, the data model of geotiffs is not always clear. The pixel coordinates are very likely to be at the top-left corner of the pixel (as I assume in my small `salem` library). Most netcdf datasets we are using in the meteo/climate community are pixel-centered. I don't know if this is something that `xarray` wants to consider, but this becomes important if you want to make accurate projections. (in practice, the two concepts are equivalent for most applications, but you have to know what is what: in my small library I called those representations `center_grid` and `corner_grid`: https://github.com/fmaussion/salem/blob/master/salem/gis.py#L101 ) 

To your questions:
1. I agree that returning a dataset is a good idea. I don't know if `raster` is a good name, but I have no other idea right now
2. I don't know. The projection was always enough for me :flushed: 
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,140063713
https://github.com/pydata/xarray/issues/790#issuecomment-195955210,https://api.github.com/repos/pydata/xarray/issues/790,195955210,MDEyOklzc3VlQ29tbWVudDE5NTk1NTIxMA==,10050469,2016-03-13T13:13:56Z,2016-03-13T13:15:07Z,MEMBER,"Hi @jhamman , I tend to agree with your doubts. I'll still comment on your cons:

To (1): I also think that xarray should avoid opening the projection can of worms. But the minimum things that xarray could do with rasterio is to read corner coordinates, dx and dy and define the two coordinates ""x"" and ""y"" out of it, without taking care of whether these are meters, degrees of arc or whatever. As long at the other rasterio file attributes are available as attribute of the `DataArray` or `DataSet` objects, users can do their own mixture

To (2): some geotiffs files also have more than one band. I don't know if these bands are named or have metadata, so maybe xarray will have to take decisions about these names too (most probably 1, 2, 3...).

I'll add a (3): rasterio depends on GDAL, which is huge and every now and then causes trouble on conda. This might also cause troubles to the continuous integration of xarray 

Altogether this might be more complicated than worth it, but maybe the rasterio folks have interest in this and might provide more support. 

If the idea for xarray accessors is implemented (https://github.com/pydata/xarray/issues/706#issuecomment-169099306) this will allow more specific libraries like mine to do their own rasterio support at low cost.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,140063713
https://github.com/pydata/xarray/issues/790#issuecomment-195254611,https://api.github.com/repos/pydata/xarray/issues/790,195254611,MDEyOklzc3VlQ29tbWVudDE5NTI1NDYxMQ==,10050469,2016-03-11T08:25:04Z,2016-03-11T08:25:04Z,MEMBER,":+1: Rasterio shines at reading georeferencing metadata out of any file, and I guess it would be no big deal to treat the various info as attributes in an xarray dataset. It is also possible to do lazy reading out of rasterio files.

(example with a geotiff file: https://github.com/fmaussion/salem/blob/master/salem/datasets.py#L263)
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