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  • dhimmel · 3 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
290951551 https://github.com/pydata/xarray/issues/1053#issuecomment-290951551 https://api.github.com/repos/pydata/xarray/issues/1053 MDEyOklzc3VlQ29tbWVudDI5MDk1MTU1MQ== dhimmel 1117703 2017-04-01T22:34:12Z 2017-04-01T23:29:31Z NONE

First let me say, I know python, but I don't know linear algebra (I rely on @kkloste for algebra). I'm also new to xarray and recently used it for the first time to represent a hetnet (network with multiple node and relationship types) as a xarray.DataSet where each DataArray is an adjacency matrix (0 or 1 for whether an edge exists) for a specific edge type. I was drawn to xarray because it allows us to:

  1. assign row/column labels (representing node identity) to 2D arrays (adjacency matrixes in our case)
  2. reason across multiple adjacency matrixes by assigning dimension identities (node types)

The operations that we're using for our project are dot-product multiplying 2D arrays by 2D arrays and 1D arrays by 2D arrays. Currently, our arrays are numpy.ndarrays, but we may switch some of our 2D arrays to scipy.sparse matrices.

I'm intrigued, but how would this work? data_array + numpy_array yields a result with well-defined labels as long as numpy_array broadcasts against data_array.data, but data_array @ numpy_array does not if numpy_array has 2 or more dimensions.

My intuition was that we use @ on a DataArray in cases where DataArray.values @ numpy.ndarray or numpy.ndarray @ DataArray.values would work. In these situations, the user would be responsible for ensuring numpy.ndarray had the correct coordinates and dimensions. We're also interested in DataArray.values @ scipy.sparse.

However, it appears that xarray may do some inference based aligning dimensions/coordinates... and that I need to understand this process a bit more. Sorry if this reply doesn't help you move forward with this issue. I hopefully will be able to be more helpful as I become more familiar with xarray.

It also gets messy on Dataset objects

For clarity, I wasn't thinking of using @ on Datasets.

 
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   Support __matmul__ operator (@) 184238633
290940281 https://github.com/pydata/xarray/issues/1053#issuecomment-290940281 https://api.github.com/repos/pydata/xarray/issues/1053 MDEyOklzc3VlQ29tbWVudDI5MDk0MDI4MQ== dhimmel 1117703 2017-04-01T19:01:57Z 2017-04-01T19:01:57Z NONE

More specifically, I'd like to be able to do matrix multiplication between numpy ndarrays / matrices, scipy sparse matrices, and xarray DataArrays. @ seems like the most natural operator to enable this cross-package compatibility.

 
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   Support __matmul__ operator (@) 184238633
290939565 https://github.com/pydata/xarray/issues/1053#issuecomment-290939565 https://api.github.com/repos/pydata/xarray/issues/1053 MDEyOklzc3VlQ29tbWVudDI5MDkzOTU2NQ== dhimmel 1117703 2017-04-01T18:49:50Z 2017-04-01T18:49:50Z NONE

Would love support for PEP 465 @ notation.

Recently, @ came in handy when multiplying numpy.ndarray with scipy.sparse matrices. We're considering xarray for our project and compatibility with this unified operator would be a real plus!

 
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   Support __matmul__ operator (@) 184238633

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