Intenté reescribir un código de lectura csv para poder ejecutarlo en varios núcleos en Python 3.2.2. Traté de usar el Pool
objeto de multiprocesamiento, que adapté de ejemplos de trabajo (y ya me funcionó para otra parte de mi proyecto). Me encontré con un mensaje de error que encontré difícil de descifrar y solucionar.
El error:
Traceback (most recent call last):
File "parser5_nodots_parallel.py", line 256, in <module>
MG,ppl = csv2graph(r)
File "parser5_nodots_parallel.py", line 245, in csv2graph
node_chunks)
File "/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/multiprocessing/pool.py", line 251, in map
return self.map_async(func, iterable, chunksize).get()
File "/Library/Frameworks/Python.framework/Versions/3.2/lib/python3.2/multiprocessing/pool.py", line 552, in get
raise self._value
AttributeError: __exit__
El código relevante:
import csv
import time
import datetime
import re
from operator import itemgetter
from multiprocessing import Pool
import itertools
def chunks(l,n):
"""Divide a list of nodes `l` in `n` chunks"""
l_c = iter(l)
while 1:
x = tuple(itertools.islice(l_c,n))
if not x:
return
yield x
def csv2nodes(r):
strptime = time.strptime
mktime = time.mktime
l = []
ppl = set()
pattern = re.compile(r"""[A-Za-z0-9"/]+?(?=[,\n])""")
for row in r:
with pattern.findall(row) as f:
cell = int(f[3])
id = int(f[2])
st = mktime(strptime(f[0],'%d/%m/%Y'))
ed = mktime(strptime(f[1],'%d/%m/%Y'))
# collect list
l.append([(id,cell,{1:st,2: ed})])
# collect separate sets
ppl.add(id)
return (l,ppl)
def csv2graph(source):
MG=nx.MultiGraph()
# Remember that I use integers for edge attributes, to save space! Dic above.
# start: 1
# end: 2
p = Pool()
node_divisor = len(p._pool)
node_chunks = list(chunks(source,int(len(source)/int(node_divisor))))
num_chunks = len(node_chunks)
pedgelists = p.map(csv2nodes,
node_chunks)
ll = []
ppl = set()
for l in pedgelists:
ll.append(l[0])
ppl.update(l[1])
MG.add_edges_from(ll)
return (MG,ppl)
with open('/Users/laszlosandor/Dropbox/peers_prisons/python/codetenus_test.txt','r') as source:
r = source.readlines()
MG,ppl = csv2graph(r)
¿Cuál es una buena forma de solucionar este problema?
None
debido a problemas de alcance.