issue_comments: 320297159

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https://github.com/pydata/xarray/pull/1272#issuecomment-320297159 https://api.github.com/repos/pydata/xarray/issues/1272 320297159 MDEyOklzc3VlQ29tbWVudDMyMDI5NzE1OQ== 4992424 2017-08-04T16:45:56Z 2017-08-19T18:23:06Z NONE

Okay, it was a bit of effort but I implemented upsampling. For the padding methods I just re-index the Dataset or DataArray using the re-sampled time frequencies. I also added interpolation, but that was a bit tricky; we have to sort of break the split-apply-combine idiom to do that, so I created a Resampler mix-in which could contain the logic for the up-sampling. The DatasetResampler and DataArrayResampler each then implement similar logic for doing the interpolation. The up-sampling is designed to work with n-dimensional data.

The padding methods work 100% with dask arrays - since we're just calling xarray methods which themselves work with dask arrays! There are some eager computations (just the calculation of the up-sampled time frequencies) but I don't think that's a major issue; the actual re-indexing/padding is deferred. Interpolation works with dask arrays too, but eagerly does the computations.

Could use a review from @shoyer or @jhamman.

New TODO list:

  • [ ] Add example chart to the timeseries doc page comparing the different upsampling options
  • [x] Additional up-sampling test cases for both DataArrays and Datasets
  • [x] Code clean-up
  • [x] What's new
 
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