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  • Support for Scipy Sparse Arrays · 4 ✖
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1311930838 https://github.com/pydata/xarray/issues/7280#issuecomment-1311930838 https://api.github.com/repos/pydata/xarray/issues/7280 IC_kwDOAMm_X85OMnnW keewis 14808389 2022-11-11T16:39:52Z 2022-11-11T16:39:52Z MEMBER

changing the assignment to: python s.__class__.__array_namespace__ = ... should fix this, but indeed it is better to just wait on scipy.sparse to implement the array API (NEP47)

 
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   Support for Scipy Sparse Arrays 1445486904
1311919228 https://github.com/pydata/xarray/issues/7280#issuecomment-1311919228 https://api.github.com/repos/pydata/xarray/issues/7280 IC_kwDOAMm_X85OMkx8 mangecoeur 743508 2022-11-11T16:27:57Z 2022-11-11T16:27:57Z CONTRIBUTOR

@keewis using your solution things seem to more or less work, except that every operation of course 'loses' the __array_namespace__ attr so anything like slicing only half works, plus a lot of indexing operations are not implemented on scipy sparse arrays.

 
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   Support for Scipy Sparse Arrays 1445486904
1311902588 https://github.com/pydata/xarray/issues/7280#issuecomment-1311902588 https://api.github.com/repos/pydata/xarray/issues/7280 IC_kwDOAMm_X85OMgt8 mangecoeur 743508 2022-11-11T16:14:12Z 2022-11-11T16:14:12Z CONTRIBUTOR

Ok I had assumed that scipy would have directly implemented the array interface, I will see if there is already an issue open there. Then we can slowly see what else does/doesn't work.

 
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   Support for Scipy Sparse Arrays 1445486904
1311899371 https://github.com/pydata/xarray/issues/7280#issuecomment-1311899371 https://api.github.com/repos/pydata/xarray/issues/7280 IC_kwDOAMm_X85OMf7r keewis 14808389 2022-11-11T16:11:12Z 2022-11-11T16:11:12Z MEMBER

the reason that's failing is that scipy.sparse.coo_array does not implement __array_function__ or __array_namespace__. As soon as that's the case I'd expect it to work.

In fact, setting s.__array_namespace__ = lambda x: x will result in xr.DataArray(s) printing as <xarray.DataArray (dim_0: 4, dim_1: 4)> <4x4 sparse array of type '<class 'numpy.float64'>' with 4 stored elements in COOrdinate format> Dimensions without coordinates: dim_0, dim_1 which I think is what you were after? Though of course that does not mean that we can actually use it...

 
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   Support for Scipy Sparse Arrays 1445486904

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