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/597#issuecomment-144680733,https://api.github.com/repos/pydata/xarray/issues/597,144680733,MDEyOklzc3VlQ29tbWVudDE0NDY4MDczMw==,2526498,2015-10-01T09:49:11Z,2015-10-01T09:59:56Z,NONE,"I have created the NetCDF files myself from geotiffs and I have made them so that there is no geographical overlapping between them. Basically each file contains a 1x1 degree area and a year worth of satellite data. The only problem that I can see with this approach is that the time dimension is different between files (the satellite covers different areas at different times). This might be problem when aggregating a big area because if the time dimension has to be homogenised it will be filled with basically no data over the whole area (sparse arrays). Depending how this sparsity is implemented it can fill memory pretty quickly.
Some of these files can be found at:
http://dapds00.nci.org.au/thredds/catalog/uc0/rs0_dev/gdf_trial/20150709/LS5TM/catalog.html
A sample of `open_dataset` for a file is:
```
import xray
dap_file = 'http://dapds00.nci.org.au/thredds/dodsC/uc0/rs0_dev/gdf_trial/20150709/LS5TM/LS5TM_1987_-34_147.nc'
ds = xray.open_dataset(dap_file, decode_coords=False)
print(ds)
Dimensions: (latitude: 4000, longitude: 4000, time: 11)
Coordinates:
* time (time) datetime64[ns] 1987-05-27T23:26:36 1987-08-31T23:29:21 ...
* latitude (latitude) float64 -33.0 -33.0 -33.0 -33.0 -33.0 -33.0 -33.0 ...
* longitude (longitude) float64 147.0 147.0 147.0 147.0 147.0 147.0 147.0 ...
Data variables:
crs int32 ...
B10 (time, latitude, longitude) float64 ...
B20 (time, latitude, longitude) float64 ...
B30 (time, latitude, longitude) float64 ...
B40 (time, latitude, longitude) float64 ...
B50 (time, latitude, longitude) float64 ...
B70 (time, latitude, longitude) float64 ...
Attributes:
history: NetCDF-CF file created 20150709.
license: Generalised Data Framework NetCDF-CF Test File
spatial_coverage: 1.000000 degrees grid
featureType: grid
geospatial_lat_min: -34.0
geospatial_lat_max: -33.0
geospatial_lat_units: degrees_north
geospatial_lat_resolution: -0.00025
geospatial_lon_min: 147.0
geospatial_lon_max: 148.0
geospatial_lon_units: degrees_east
geospatial_lon_resolution: 0.00025
```
Thank you very much for your help.
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