issue_comments: 497473031

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https://github.com/pydata/xarray/issues/2281#issuecomment-497473031 https://api.github.com/repos/pydata/xarray/issues/2281 497473031 MDEyOklzc3VlQ29tbWVudDQ5NzQ3MzAzMQ== 1217238 2019-05-30T20:24:56Z 2019-05-30T20:24:56Z MEMBER

@fspaolo where does that huge number come from? I thought you said you have 1500 nodes in total. Did you select a single point on the t dimension before you applied bisplrep?

2665872 is roughly 1600^2.

Also, (pardon the ignorance, I never dealt with geographical data) what kind of information does having your lat and lon being bidimensional convey? Does it imply lat[i, j] < lat[i +1, j] and lon[i, j] < lon[i, j+1] for any possible (i, j)?

I think this is true sometimes but not always. The details depend on the geographic projection, but generally a good mesh has some notion of locality -- nearby locations in real space (i.e., on the globe) should also nearby in projected space.

Anyways, as I've said above, I think it would be totally appropriate to build routines resembling scipy's griddata into interp() (but using the lower level KDTree/Delaunay interface). This will not be the most efficiency strategy, but should offer reasonable performance in most cases. Let's consider this open for contributions, if anyone is interested in putting together a pull request.

 
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