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1. dionhaefner commented on Jun 1, 2019

   

   dionhaefner

   on Jun 1, 2019

   CollaboratorAuthor

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   I am planning to contribute an implementation leveraging __array_ufunc__ and __array_function__ for bh107. The following minimal example works:

   import os
   os.environ['NUMPY_EXPERIMENTAL_ARRAY_FUNCTION'] = '1'
   
   import numpy as np
   
   
   class MyArray:
       def __init__(self, array):
           self.array = array
   
       def __array_function__(self, func, types, args, kwargs):
           cls = self.__class__
           args = (a.array if isinstance(a, cls) else a for a in args)
           return cls(func(*args, **kwargs))
       
       def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
           cls = self.__class__
           inputs = (a.array if isinstance(a, cls) else a for a in inputs)
           return cls(getattr(ufunc, method)(*inputs, **kwargs))
           
       def __repr__(self):
           if np.isscalar(self.array):
               return repr(self.array)
           return self.__class__.__name__ + repr(self.array)[5:]

   Then, you can use it as

   >>> a = MyArray(np.random.rand(10))
   >>> np.divide(a, np.ones(10))
   MyArray([0.22199372, 0.14071593, 0.45037375, 0.2900408 , 0.28482036,
          0.1315491 , 0.65760868, 0.29693582, 0.780299  , 0.57618724])
   >>> np.add.accumulate(a)
   MyArray([0.22199372, 0.36270965, 0.8130834 , 1.1031242 , 1.38794456,
          1.51949366, 2.17710234, 2.47403816, 3.25433716, 3.8305244 ])
   >>> np.argsort(a)
   MyArray([5, 1, 0, 4, 3, 7, 2, 9, 6, 8])
   >>> np.allclose(a, np.ones(100))
   False

   That way, we would get sane default implementations for virtually all NumPy functions without -m bohrium or import bohrium as np magic, with the possibility to implement high-performance drop-ins for everything we want to support.
2. madsbk commented on Jun 3, 2019

   

   madsbk

   on Jun 3, 2019

   Contributor

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   That would be great!