Support Ramba distributed arrays. #5970

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1. TomNicholas

   added

   topic-arraysrelated to flexible array supportrelated to flexible array support

   enhancement

   on Nov 10, 2021
2. DrTodd13 commented on Nov 12, 2021

   

   DrTodd13

   on Nov 12, 2021

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   @TomNicholas Please add an in-progress label for the associated PR I opened.
3. DrTodd13 commented on Nov 17, 2021

   

   DrTodd13

   on Nov 17, 2021

   Last edited by keewis

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   @TomNicholas @keewis

   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?
4. dcherian commented on Nov 24, 2021

   

   dcherian

   on Nov 24, 2021

   Contributor

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   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.