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

[Sebastien Bardeau]

Ooops sorry there was two mistakes with the 'hasslice' flag. This seems 
now to work for me.

   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:
      hasslice = False
      for i in index:
         if type(i) == slice:
            hasslice = True
      # 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 not 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]


Sebastien Bardeau wrote:
>> 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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>   

-- 
-------------------------
   Sebastien Bardeau
  L3AB - CNRS UMR 5804
 2 rue de l'observatoire
         BP 89
    F - 33270 Floirac
Tel: (+33) 5 57 77 61 46
-------------------------


-------------------------------------------------------------------------
Using Tomcat but need to do more? Need to support web services, security?
Get stuff done quickly with pre-integrated technology to make your job easier
Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642