issues: 317421267

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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
317421267 MDU6SXNzdWUzMTc0MjEyNjc= 2079 New feature: interp1d 6213168 closed 0     8 2018-04-24T22:45:03Z 2018-05-06T19:30:32Z 2018-05-06T19:30:32Z MEMBER      

I've written a series of wrappers for the 1-dimensional scipy interpolators.

Prototype code and colourful demo plots: https://gist.github.com/crusaderky/b0aa6b8fdf6e036cb364f6f40476cc67

Features

  • Interpolate a ND array on any arbitrary dimension
  • Nearest-neighbour, linear, quadratic, cubic, Akima, PCHIP, and custom interpolators are supported
  • dask supported on both on the interpolated array and x_new
  • Supports ND x_new arrays
  • The CPU-heavy interpolator generation (splrep) is executed only once and then can be applied to multiple x_new (splev)
  • Pickleable and distributed-friendly

Design hacks

  • Depends on dask module, even when all inputs are based on plain numpy.
  • Abuses attrs and the ability to invoke a.attrname to get the user experience of a new DataArray method.
  • Abuses the fact that the chunks of a dask.array.Array can contain anything and you won't notice until you compute them.

Limitations

  • Can't dump to netcdf. Not solvable without hacking into the implementation details of scipy.
  • Datasets are not supported. Trivial to fix after solving #1699.
  • Chunks are not supported on x_new. Trivial to fix after solving #1995.
  • Chunks are not supported along the interpolated dimension. This is very complicated to solve. If x and x_new were always monotonic ascending,it would be (not trivially) solvable with dask.array.ghost.ghost. If you make no assumptions about monotonicity, things become way more complicated. A solution would need to go in the dask module, and then be invoked trivially from here with dask='allowed'.
 
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