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  • godfrey4000 · 2 ✖

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  • API for multi-dimensional resampling/regridding · 2 ✖

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272582585 https://github.com/pydata/xarray/issues/486#issuecomment-272582585 https://api.github.com/repos/pydata/xarray/issues/486 MDEyOklzc3VlQ29tbWVudDI3MjU4MjU4NQ== godfrey4000 18623439 2017-01-14T00:17:05Z 2017-01-14T00:17:05Z NONE

I'm ready to start working on this project. I already have a prototype regridding class that I developed as part of another project. Working on that, I discovered these points: - regridding takes a long time because the lattices can be huge - the design should accomodate parallel processing on a cluster - data needs to be normalized first (deal with missing values, etc.) - the user will want choices

Some of these choices are: - the destination lattice - the interpolation algorithm - subset of the dimension space   As the first step in a strategy to achieve this with a sequence of realizable goals, I plan to implement a regridding of just the latitude and longitude dimensions.

Is there a style guide that I can/should follow? Something like this: https://google.github.io/styleguide/pyguide.html? Does it or something else define naming conventions?

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  API for multi-dimensional resampling/regridding 96211612
271447717 https://github.com/pydata/xarray/issues/486#issuecomment-271447717 https://api.github.com/repos/pydata/xarray/issues/486 MDEyOklzc3VlQ29tbWVudDI3MTQ0NzcxNw== godfrey4000 18623439 2017-01-10T00:07:16Z 2017-01-10T00:07:16Z NONE

I have an immediate need in this area. My objective is to create a tool that will enable arithmetic on variables defined on lattices whose points don't coincide. Through my attempts thus far, it has become clear that I need data structures that incorporate spacial indexing and lattice indexing.

Since I have to tackle this issue to proceed, I thought I should follow the thinking discussed in this forum, so that it may be useful to others.

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  API for multi-dimensional resampling/regridding 96211612

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