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  • JiaweiZhuang · 3 ✖

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  • Does interp() work on curvilinear grids (2D coordinates) ? · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
497155229 https://github.com/pydata/xarray/issues/2281#issuecomment-497155229 https://api.github.com/repos/pydata/xarray/issues/2281 MDEyOklzc3VlQ29tbWVudDQ5NzE1NTIyOQ== JiaweiZhuang 25473287 2019-05-30T00:24:36Z 2019-05-30T00:33:49Z NONE

An MPI error?!

@fspaolo Could you post a minimal reproducible example on xESMF's issue tracker? Just to keep this issue clean. The error looks like an ESMF installation problem that can happen on legacy OS, and it can be easily fixed by Docker or other containers.

It is surprising that a package targeting n-dimensional gridded datasets (particularly those from the geo/climate sciences) does not handle such a common task with spatial gridded data.

Just a side comment: This is a common but highly non-trivial task... Even small edges cases like periodic longitudes and polar singularities can cause interesting troubles. Otherwise I would just code up an algorithm in Xarray from scratch instead of relying on a heavy Fortran library. But things will get improved over time...

 
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   Does interp() work on curvilinear grids (2D coordinates) ?  340486433
404685906 https://github.com/pydata/xarray/issues/2281#issuecomment-404685906 https://api.github.com/repos/pydata/xarray/issues/2281 MDEyOklzc3VlQ29tbWVudDQwNDY4NTkwNg== JiaweiZhuang 25473287 2018-07-12T23:58:48Z 2018-07-13T18:24:02Z NONE

Do you have any proposal?

I guess it is not an API design problem yet... The algorithm is not here since interpn doesn't deal with curvilinear grids.

I think we could make dr.interp(xc=lon, yc=lat) work for the N-D -> M-D case by wrapping scipy.interpolate.griddata

My concern with scipy.interpolate.griddata is that the performance might be miserable... griddata takes an arbitrary stream of data points in a D-dimensional space. It doesn't know if those source data points have a gridded/mesh structure. A curvilinear grid mesh needs to be flatten into a stream of points before passed to griddata(). Might not be too bad for nearest-neighbour search, but very inefficient for linear/bilinear method, where knowing the mesh structure beforehand can save a lot of computation.

Utilizingscipy.interpolate.griddata would be a nice feature, but it should probably be used for data point streams (more like a Pandas dataframe method?), not as a way to handle curvilinear grids.

PS: I have some broader concerns regarding interp vs xESMF: JiaweiZhuang/xESMF#24

 
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   Does interp() work on curvilinear grids (2D coordinates) ?  340486433
404387651 https://github.com/pydata/xarray/issues/2281#issuecomment-404387651 https://api.github.com/repos/pydata/xarray/issues/2281 MDEyOklzc3VlQ29tbWVudDQwNDM4NzY1MQ== JiaweiZhuang 25473287 2018-07-12T04:43:09Z 2018-07-13T00:11:44Z NONE

One way I can think of to make interp() work on this example: Define a new coordinate system (i.e. two new coordinate variables) on the source curvilinear grid, and rewrite the destination coordinate using those new coordinate variables (not lat, lon anymore).

But this is absolutely too convoluted...

Updated: see Gridded with Scipy for a similar idea.

 
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   Does interp() work on curvilinear grids (2D coordinates) ?  340486433

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