Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Sebastien Bardeau]

> One possible solution (there can be more) is using ndarray:
>
> In [47]: a=numpy.array([1,2,3], dtype="i4")
> In [48]: n=1    # the position that you want to share
> In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>   
Ok thanks. Actually that was also the solution I found. But this is much 
more complicated when arrays are N dimensional with N>1, and above all 
if user asks for a slice in one or more dimension. Here is how I 
redefine the __getitem__ method for my arrays. Remember that the goal is 
to return a 0-d array rather than a numpy.scalar when I extract a single 
element out of a N-dimensional (N>=1) array:

   def __getitem__(self,index): # Index may be either an int or a tuple
      # Index length:
      if type(index) == int: # A single element through first dimension
         ilen = 1
         index = (index,)    # A tuple
      else:
         ilen = len(index)
      # Array rank:
      arank = len(self.shape)
      # Check if there is a slice:
      for i in index:
         if type(i) == slice:
            hasslice = True
         else:
            hasslice = False
      # Array is already a 0-d array:
      if arank == 0 and index == (0,):
         return self[()]
      elif arank == 0:
         raise IndexError, "0-d array has only one element at index 0."
      # This will return a single element as a 0-d array:
      elif arank == ilen and hasslice:
         # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar!
         # (Numpy scalars do not share their data with the parent array)
         newindex = list(index)
         newindex[0] = slice(index[0],index[0]+1,None)
         newindex = tuple(newindex)
         return self[newindex].reshape(())
      # This will return a n-D subarray (n>=1):
      else:
         return self[index]

 Well... I do not think this is very nice. Someone has another idea? My 
question in my first post was: is there a way to get a single element of 
an array into
a 0-d array which shares memory with its parent array?

 Sebastien

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