Let's consider adding `https://pypi.org/project/tifffile/` as a dependency for tif io. - [ ] add `read_tif` to `readers.py` which mirrors `xr.open_rasterio` DEM data is stored as COG on S3 ```python url = 'https://esip-pangeo-uswest2.s3-us-west-2.amazonaws.com/sciencebase/Southern_California_Topobathy_DEM_1m_cog.tif' da = xr.open_rasterio(url) da <xarray.DataArray (band: 1, y: 225217, x: 316120)> [71195598040 values with dtype=float32] Coordinates: * band (band) int64 1 * y (y) float64 3.825e+06 3.825e+06 3.825e+06 ... 3.6e+06 3.6e+06 * x (x) float64 1.795e+05 1.795e+05 1.795e+05 ... 4.956e+05 4.956e+05 Attributes: transform: (1.0, 0.0, 179523.99999999822, 0.0, -1.0, 3824832.0) crs: +init=epsg:26911 res: (1.0, 1.0) is_tiled: 1 nodatavals: (-3.4028230607370965e+38,) scales: (1.0,) offsets: (0.0,) Check out the overviews: src = rasterio.open(url, 'r') [src.overviews(i) for i in src.indexes] [[4, 8, 16, 32, 64, 128, 256, 512, 1023]] ``` *taken from:* https://nbviewer.jupyter.org/gist/rsignell-usgs/0f96bb9c0ca34a5dd0fc8131a7bbae1c
Activity
[-]add `open_tif` method which mirrors xr.open_rasterio[/-][+]add `read_tif` method[/+]geotiff: extract read_geotiff_gpu to _backends/gpu.py (#1885) (#1895)