'''
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 type(self).__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, int(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
