Testing storage/loading of networkx graphs

pickle_unpickle.py

from timeit import timeit
import pickle

from networkx.generators import fast_gnp_random_graph
from networkx.readwrite import json_graph

def create(N=1000):
  # create some random graph
  global G
  G = fast_gnp_random_graph(N, 0.7)

def to_adjacency(G):
  global data
  data = json_graph.adjacency_data(G)

def dump(data):
  with open('/tmp/graph.pickle', 'wb+') as f:
    pickle.dump(data, f)

def load():
  global data
  with open('/tmp/graph.pickle', 'rb') as f:
    data = pickle.load(f)

def from_adjacency(d):
  global H
  H = json_graph.adjacency_graph(d)

def time(what, code):
  s = timeit(code, number=1, globals=globals())
  print("\t{:04.3f}s ~ {}".format(s, what))

def timings(N):
  print("\nN={}\n".format(N))
  time("generating".format(N), "create({})".format(N))
  time("converting", "to_adjacency(G)")
  time("storing", "dump(data)")
  time("loading", "load()")
  time("converting", "from_adjacency(data)")

if __name__ == '__main__':
  timings(1000)
  timings(2000)
  timings(3000)
  timings(4000)

run_output.txt

N=1000

	0.666s ~ generating
	0.790s ~ converting
	0.237s ~ storing
	0.295s ~ loading
	1.152s ~ converting

N=2000

	2.761s ~ generating
	3.282s ~ converting
	1.068s ~ storing
	1.105s ~ loading
	4.941s ~ converting

N=3000

	6.377s ~ generating
	7.644s ~ converting
	2.464s ~ storing
	2.393s ~ loading
	12.219s ~ converting

N=4000

	12.458s ~ generating
	19.025s ~ converting
	8.825s ~ storing
	8.921s ~ loading
	27.601s ~ converting