new_contents: '''shmarray.py
  
  Shared memory array implementation for numpy which delegates all the nasty stuff
  to multiprocessing.sharedctypes.
  
  Copyright (c) 2010, David Baddeley
  All rights reserved.'''
  
  #Licensced under the BSD liscence ...
  #
  #Redistribution and use in source and binary forms, with or without modification,
  #are permitted provided that the following conditions are met:
  #
  #Redistributions of source code must retain the above copyright notice, this list
  #of conditions and the following disclaimer.
  #
  #Redistributions in binary form must reproduce the above copyright notice, this
  #list of conditions and the following disclaimer in the documentation and/or other
  #materials provided with the distribution.
  #
  #Neither the name of the <ORGANIZATION> nor the names of its contributors may be
  #used to endorse or promote products derived from this software without specific
  #prior written permission.
  #
  #THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
  #ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
  #WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
  #IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
  #INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
  #BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
  #DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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  #OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
  #OF THE POSSIBILITY OF SUCH DAMAGE.
  
  import numpy
  from multiprocessing import sharedctypes
  from numpy import ctypeslib
  
  
  class shmarray(numpy.ndarray):
      '''subclass of ndarray with overridden pickling functions which record dtype, shape
      etc... but defer pickling of the underlying data to the original data source.
  
      Doesn't actually handle allocation of the shared memory - this is done in create,
      and zeros, ones, (or create_copy) are the functions which should be used for creating a new
      shared memory array.
  
      TODO - add argument checking to ensure that the user is passing reasonable values.'''
      def __new__(cls, ctypesArray, shape, dtype=float,
            strides=None, offset=0, order=None):
          
          #some magic (copied from numpy.ctypeslib) to make sure the ctypes array
          #has the array interface
          tp = type(ctypesArray)
          try: tp.__array_interface__
          except AttributeError: ctypeslib.prep_array(tp)
  
          obj = numpy.ndarray.__new__(cls, shape, dtype, ctypesArray, offset, strides,
                           order)
  
          # keep track of the underlying storage
          # this may not be strictly necessary as the same info should be stored in .base
          obj.ctypesArray = ctypesArray
          
          return obj
  
      def __array_finalize__(self, obj):
          
          if obj is None: return
          
          self.ctypesArray = getattr(obj, 'ctypesArray', None)
  
      def __reduce_ex__(self, protocol):
          '''delegate pickling of the data to the underlying storage, but keep copies
          of shape, dtype & strides.
  
          TODO - find how to get at the offset and order parameters and keep track of them as well.'''
          return shmarray, (self.ctypesArray, self.shape, self.dtype, self.strides)#, self.offset, self.order)
  
      def __reduce__(self):
          return __reduce_ex__(self, 0)
  
  
  def create(shape, dtype='d', alignment = 32):
      '''Create an uninitialised shared array. Avoid object arrays, as these
      will almost certainly break as the objects themselves won't be stored in shared
      memory, only the pointers'''
      shape = numpy.atleast_1d(shape).astype('i')
      dtype = numpy.dtype(dtype)
  
      #we're going to use a flat ctypes array
      N = numpy.prod(shape) + alignment
      # The upper bound of size we want to allocate to be certain
      #  that we can take an aligned array of the right size from it.
      N_bytes_big = N * dtype.itemsize
      # The final (= right) size of the array
      N_bytes_right = numpy.prod(shape) * dtype.itemsize
  
      dt = 'b'
  
      # We create the big array first
      a = sharedctypes.RawArray(dt, N_bytes_big)
  
      sa =  shmarray(a, (N_bytes_big,), dt)
  
      # We pick the first index of the new array that is aligned
      # If the address of the first element is 1 and we want 8-alignment, the 
      #  first aligned index of the array is going to be 7 == -1 % 8
      start_index = -sa.ctypes.data % alignment
      # Finally, we take the (aligned) subarray and reshape it.
      sa = sa[start_index:start_index + N_bytes_right].view(dtype).reshape(shape)
  
      return sa
  
  def zeros(shape, dtype='d'):
      """Create an shared array initialised to zeros. Avoid object arrays, as these
      will almost certainly break as the objects themselves won't be stored in shared
      memory, only the pointers"""
  
      sa = create(shape, dtype=dtype)
  
      #contrary to the documentation, sharedctypes.RawArray does NOT always return
      #an array which is initialised to zero - do it ourselves
      #http://code.google.com/p/python-multiprocessing/issues/detail?id=25
      sa[:] = numpy.zeros(1, dtype)
      return sa
  
  def ones(shape, dtype='d'):
      '''Create an shared array initialised to ones. Avoid object arrays, as these
      will almost certainly break as the objects themselves won't be stored in shared
      memory, only the pointers'''
      sa = create(shape, dtype=dtype)
  
      sa[:] = numpy.ones(1, dtype)
      return sa
  
  def create_copy(a):
      '''create a a shared copy of an array'''
      #create an empty array
      b = create(a.shape, a.dtype)
  
      #copy contents across
      b[:] = a[:]
  
      return b
  
subject: Copy in a shared numpy array implementation