issues: 340757861

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
340757861 MDU6SXNzdWUzNDA3NTc4NjE= 2284 interp over time coordinate 6153603 closed 0     2 2018-07-12T18:54:45Z 2018-07-29T06:09:41Z 2018-07-29T06:09:41Z CONTRIBUTOR      

Before I start, I'm very excited about the interp addition in 0.10.7. Great addition and thanks to @fujiisoup and @shoyer.

I see there was a bit of a discussion in the interp pull request, #2104, about interpolating over times and that it was suggested to wait for use cases. I can think of an immediate use case in my line of work. I frequently use regular gridded geophysical data (time, lat, lon), not unlike the sample tutorial air_temperature data, and the data must be interpolated to line up with corresponding satellite measurements that are irregularly spaced in lat, lon and time.

Being able to interpolate in one quick step would be fantastic. For example:

python ds = xr.tutorial.load_dataset('air_temperature') ds.interp(lat=60.5, lon=211, time='2013-01-01T03:14:37')

Problem description

Currently issues TypeError: cannot perform reduce with flexible type.

Desired Output

<xarray.Dataset> Dimensions: () Coordinates: lat float64 60.5 lon int64 211 time datetime64[ns] 2013-01-01T03:14:37 Data variables: air float64 273.5

 
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   completed 13221727 issue

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