You can use multiprocessing.Pool:
from multiprocessing import Pool
class Engine(object):
def __init__(self, parameters):
self.parameters = parameters
def __call__(self, filename):
sci = fits.open(filename + '.fits')
manipulated = manipulate_image(sci, self.parameters)
return manipulated
try:
pool = Pool(8) # on 8 processors
engine = Engine(my_parameters)
data_outputs = pool.map(engine, data_inputs)
finally: # To make sure processes are closed in the end, even if errors happen
pool.close()
pool.join()