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  • Support Ramba distributed arrays. · 3 ✖
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977443695 https://github.com/pydata/xarray/issues/5970#issuecomment-977443695 https://api.github.com/repos/pydata/xarray/issues/5970 IC_kwDOAMm_X846Qptv dcherian 2448579 2021-11-24T02:42:01Z 2021-11-24T02:42:01Z MEMBER

Sorry for the late reply @DrTodd13 .

I think #4972 is the real answer here. It looks like @keewis has it mostly working. It hasn't been merged because test_sparse isn't passing yet. 

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   Support Ramba distributed arrays. 1050082867
972112384 https://github.com/pydata/xarray/issues/5970#issuecomment-972112384 https://api.github.com/repos/pydata/xarray/issues/5970 IC_kwDOAMm_X8458UIA DrTodd13 2584128 2021-11-17T21:57:28Z 2021-11-17T22:08:03Z NONE

@TomNicholas @keewis ```python import ramba import numpy as np import xarray

ra1 = ramba.random.randn(10,20) xa1 = xarray.DataArray(ra1) np1 = xa1.data.asarray() print("np1:", np1) print("xa1.data:", xa1.data, type(xa1.data), xa1.data.dtype, type(xa1.data.dtype))

xa2 = xa1 + 10.0 xa22 = xa2 * 7.1 xa3 = np.sin(xa22) xa4 = xa3.transpose() xa5 = xa4.sum() print("xa5:", xa5, type(xa5)) ```

Using the __array_function__ API and other functions (and not my ill-conceived PR that added ramba to xarray for direct support) , I was able to run the above example. Originally, there were a couple parts of the NumPy API that ramba didn't previously support and I added those. The most difficult of those was 0d arrays. Those are working at least well enough now for the above example so now like I said in the meeting, I'd like even more complicated examples to explore.

Can anybody suggest or provide one?

 
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   Support Ramba distributed arrays. 1050082867
966741762 https://github.com/pydata/xarray/issues/5970#issuecomment-966741762 https://api.github.com/repos/pydata/xarray/issues/5970 IC_kwDOAMm_X845n08C DrTodd13 2584128 2021-11-12T01:19:52Z 2021-11-12T01:19:52Z NONE

@TomNicholas Please add an in-progress label for the associated PR I opened.

 
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   Support Ramba distributed arrays. 1050082867

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