shmem.py

'''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.'''

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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'):
    '''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')

    #we're going to use a flat ctypes array
    N = numpy.prod(shape)

    dtype = numpy.dtype(dtype)

    #if the dtype's relatively simple create the corresponding ctypes array
    #otherwise create a suitably sized byte array
    dt = dtype.char

    if not dt in sharedctypes.typecode_to_type.keys():
        dt = 'b'
        N *= dtype.itemsize

    a = sharedctypes.RawArray(dt, N)

    sa =  shmarray(a, shape, dtype)

    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='d')

    #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='d')

    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