def worker(task_queue, shared_data):
for task in iter(task_queue.get, 'DIE'):
#The problem appears to be that shared_data is a MP.RawArray
#We need to pass the underlying c-pointer and not to copy!
sine_module.sine_add_2(shared_data,task)
#!/usr/bin/python
# see README file
import sine_module
################################################
#This code works exactly as hoped
import numpy
tmp = numpy.zeros(10)
for index in range(10):
sine_module.sine_add_2(index,tmp)
print "The correct answer is"
print tmp
################################################
#This is an attempt to use fortran functions within a parallel python
#script. To share data between processes we must use a
#multiprocessing.RawArray
import multiprocessing as MP
#The entry function for each process
def worker(task_queue, shared_data):
for task in iter(task_queue.get, 'DIE'):
#The problem appears to be that shared_data is a MP.RawArray
#We need to pass the underlying c-pointer and not to copy!
sine_module.sine_add_2(shared_data,task)
#Create the queue for communication and start-up the processes
task_queue = MP.Queue()
shared_data = MP.RawArray('d', [0.]*10);
process1 = MP.Process(target=worker, args=(task_queue, shared_data))
def worker(task_queue, shared_data):
for task in iter(task_queue.get, 'DIE'):
tmp = sine_module.sine_add_2(shared_data,task)
shared_data[task] = tmp[task]
def worker(task_queue, shared_data):
arr = numpy.frombuffer(shared_data, dtype=numpy.double)
for task in iter(task_queue.get, 'DIE'):
print task
sine_module.sine_add_2(task, arr)
def worker(task_queue, shared_data):
for task in iter(task_queue.get, "DIE"):
sine_module.sine_add_2(shared_data, task) # does not work