issue_comments: 497475534

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https://github.com/pydata/xarray/issues/2281#issuecomment-497475534 https://api.github.com/repos/pydata/xarray/issues/2281 497475534 MDEyOklzc3VlQ29tbWVudDQ5NzQ3NTUzNA== 539688 2019-05-30T20:32:24Z 2019-05-30T20:58:23Z NONE

@fspaolo where does that huge number come from? I thought you said you have 1500 nodes in total.

Not in total, I meant ~1500 on each x/y dimension (1500 x 1500). To be precise:

``` print(cube1.shape) # (t, y, x) (306, 240, 262)

print(cube2.shape) # (y, x, t) (1452, 1836, 104) ```

Did you select a single point on the t dimension before you applied bisplrep?

Yes.

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)?

It does in my case, but cube1 is a rotated grid, meaning that lat is not the same along the x-axis neither lon is the same along the y-axis, while cube2 is a standard lon/lat grid so I can represent it simply by a 1D lon array (x-axis) and a 1D lat array (y-axis). To have them both with 1D coordinates I would have to either rotate cube2 and work with 1D rotated lon/lat, or unrotate cube1 and work with 1D standard lon/lat... but this gets me to the same problem as in the interpolation above since I have to transform (re-project) every 2D grid in the cube.

 
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