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-144794506,https://api.github.com/repos/pydata/xarray/issues/597,144794506,MDEyOklzc3VlQ29tbWVudDE0NDc5NDUwNg==,1217238,2015-10-01T17:30:55Z,2015-10-01T17:30:55Z,MEMBER,"So unfortunately there isn't an easy way to handle irregular data like this with xray. Before you put this stuff in a single dataset, you would need to align the time variables, probably by doing `.reindex` with the [`method` argument](http://xray.readthedocs.org/en/stable/indexing.html#nearest-neighbor-lookups), which lets you do nearest-neighbor interpolation, or by using [`.resample`](http://xray.readthedocs.org/en/stable/time-series.html#resampling-and-grouped-operations). That way, you could make, for example, a dataset with the latest image for each location at the start of each month.

The other option (probably also useful) is to only concatenate one spatial tile along time, so you don't need to do any interpolation or resampling. The should work out of the box with `open_mfdataset`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,109202603
https://github.com/pydata/xarray/issues/597#issuecomment-144577690,https://api.github.com/repos/pydata/xarray/issues/597,144577690,MDEyOklzc3VlQ29tbWVudDE0NDU3NzY5MA==,1217238,2015-09-30T23:59:04Z,2015-09-30T23:59:04Z,MEMBER,"It would probably be helpful to show the results of printing the several of these datasets when opened via `open_dataset`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,109202603
https://github.com/pydata/xarray/issues/597#issuecomment-144577524,https://api.github.com/repos/pydata/xarray/issues/597,144577524,MDEyOklzc3VlQ29tbWVudDE0NDU3NzUyNA==,1217238,2015-09-30T23:57:38Z,2015-09-30T23:57:38Z,MEMBER,"What do these different time range and geographical regions look like?

If they are adjacent and non-overlapping, then xray could be a very good fit and I can help whip up an example to get you started. If this is not the case (and I know that can be an issue with satellite data), then it's going to be more awkward to put them together into a single logical Dataset. It might make more sense to work with individual Datasets, e.g., at the level of an individual satellite image.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,109202603