def graph_loader(file_name):
"""
Loads a sonet.graph object from a pickle/graphml/... file
"""
try:
with Timr("GRAPH LOADING"):
return sg.load(file_name)
except IOError:
logging.exception("unable to load a graph from passed file: %s" % (file_name,))
sys.exit()
def graph_loader(file_name):
"""
Loads a sonet.graph object from a pickle/graphml/... file
"""
try:
with Timr("GRAPH LOADING"):
return sg.load(file_name)
except IOError:
logging.exception("unable to load a graph from passed file: %s",
file_name)
sys.exit()
def main():
op = create_option_parser()
(options, args) = op.parse_args()
if len(args) != 1:
print "Insert one (and only one) file to process\n"
op.print_help()
sys.exit(2)
fn = args[0]
lang, date, type_ = mwlib.explode_dump_filename(fn)
g = sg.load(fn)
g.time_slice_subgraph(start=options.start, end=options.end)
g.invert_edge_attr('weight', 'length')
vn = len(g.g.vs) # number of vertexes
en = len(g.g.es) # number of edges
timr = Timr()
if options.as_table:
tablr = Tablr()
tablr.start(1024 * 32, lang)
if options.group or options.users_role or options.histogram:
for group_name, group_attr in groups.iteritems():
g.defineClass(group_name, group_attr)
print ' * %s : nodes number : %d' % (group_name,
len(g.classes[group_name]))
else:
g.defineClass('all', {})
print " * filename: %s" % (fn, )
print " * lang: %s" % (lang, )
print " * date: %s" % (date, )
if options.details:
with Timr("details"):
print " * nodes number: %d" % (vn, )
print " * edges number: %d" % (en, )
nodes_with_outdegree = len(g.g.vs.select(_outdegree_ge=1))
nodes_with_indegree = len(g.g.vs.select(_indegree_ge=1))
self_loop_edges = len([edge for edge in g.g.es \
if edge.target == edge.source])
print " * nodes with out edges number: %d (%6f%%)" % (
nodes_with_outdegree, 100. * nodes_with_outdegree / vn)
print " * nodes with in edges number: %d (%6f%%)" % (
nodes_with_indegree, 100. * nodes_with_indegree / vn)
print " * max weights on edges : %s" % top(g.g.es['weight'])
print " * self-loop edges: %d" % self_loop_edges
#print " * diameter : %6f" % g.g.diameter(weights='length')
#print " * average weight : %6f" % numpy.average(g.g.es['weight'])
if options.density or options.reciprocity:
with Timr('density&reciprocity'):
for cls, vs in g.classes.iteritems():
if not len(vs) > 1:
continue
subgraph = vs.subgraph()
print " * %s : density : %.10f" % (cls, subgraph.density())
print " * %s : reciprocity : %.10f" % (cls,
subgraph.reciprocity())
if options.degree:
with Timr('degree'):
g.g.vs['indegree'] = g.g.degree(type=ig.IN)
g.g.vs['outdegree'] = g.g.degree(type=ig.OUT)
for cls, vs in g.classes.iteritems():
if not vs:
continue
ind = numpy.array(vs['indegree'])
outd = numpy.array(vs['outdegree'])
print " * %s : mean IN degree (no weights): %f" % (
cls, numpy.average(ind))
print " * %s : mean OUT degree (no weights): %f" % (
cls, numpy.average(outd))
print " * %s : max IN degrees (no weights): %s" % (cls,
top(ind))
print " * %s : max OUT degrees (no weights): %s" % (cls,
top(outd))
print " * %s : stddev IN degree (no weights): %f" % (
cls, numpy.sqrt(numpy.var(ind)))
print " * %s : stddev OUT degree (no weights): %f" % (
cls, numpy.sqrt(numpy.var(outd)))
if options.transitivity:
##print " * transitivity: %f" % (nx.transitivity(g), )
pass
if options.summary:
# don't use with --as-table
print " * summary: %s" % (g.g.summary(), )
if options.distance:
with Timr('split clusters'):
vc = g.g.clusters()
size_clusters = vc.sizes()
giant = vc.giant()
print " * length of 5 max clusters: %s" % top(size_clusters)
#print " * #node in 5 max clusters/#all nodes: %s" % top(
# [1.*cluster_len/vn for cluster_len in size_clusters])
if options.distance:
with Timr('distance'):
gg = sg.Graph(giant)
print " * average distance in the giant component: %f" % \
gg.averageDistance(weight='length')
print " * average hops in the giant component: %f" % \
gg.averageDistance()
#print "Average distance 2: %f" % giant.average_path_length(True,
# False)
if options.efficiency:
with Timr('efficiency'):
print " * efficiency: %f" % g.efficiency(weight='length')
##TODO: compute for centrality only if "all" or "degree"
if (options.plot or options.histogram or options.power_law
or options.centrality):
with Timr('set weighted indegree'):
g.set_weighted_degree()
if options.centrality:
timr.start('centrality')
centralities = options.centrality.split(',')
if 'all' in centralities:
centralities = 'betweenness,pagerank,degree'.split(',')
if set(centralities).difference(
'betweenness,pagerank,degree'.split(',')):
logging.error('Unknown centrality')
sys.exit(0)
if "betweenness" in centralities:
print >> sys.stderr, "betweenness"
g.g.vs['bw'] = g.g.betweenness(weights='length', directed=True)
#g.g.vs['ev'] = g.g.evcent(weights='weight') # eigenvector centrality
if 'pagerank' in centralities:
print >> sys.stderr, "pagerank"
g.g.vs['pr'] = g.g.pagerank(weights='weight') # pagerank
if 'degree' in centralities:
print >> sys.stderr, "outdegree"
g.set_weighted_degree(type=ig.OUT)
#total_weights = sum(g.g.es['weight'])
max_edges = vn * (vn - 1)
for cls, vs in g.classes.iteritems():
if not vs:
continue
if "betweenness" in centralities:
norm_betweenness = numpy.array(g.classes[cls]['bw']) \
/ max_edges
print " * %s : average betweenness : %.10f" % (
cls, numpy.average(norm_betweenness))
print " * %s : stddev betweenness : %.10f" % (
cls, numpy.sqrt(numpy.var(norm_betweenness)))
print " * %s : max betweenness: %s" % (
cls, top(numpy.array(g.classes[cls]['bw']) / max_edges))
#print " * Average eigenvector centrality : %6f" % numpy.average(
# g.vs['ev'])
if 'pagerank' in centralities:
print " * %s : average pagerank : %.10f" % (
cls, numpy.average(g.classes[cls]['pr']))
print " * %s : stddev pagerank : %.10f" % (
cls, numpy.sqrt(numpy.var(g.classes[cls]['pr'])))
print " * %s : max pagerank: %s" % (cls,
top(g.classes[cls]['pr']))
if 'degree' in centralities:
wi = g.classes[cls]['weighted_indegree']
print " * %s : average IN degree centrality (weighted): %.10f" % (
cls, numpy.average(wi))
print " * %s : stddev IN degree centrality (weighted): %.10f" % (
cls, numpy.sqrt(numpy.var(wi)))
print " * %s : max IN degrees centrality (weighted): %s" % (
cls, top(wi))
del wi
wo = g.classes[cls]['weighted_outdegree']
print " * %s : average OUT degree centrality (weighted) : %.10f" %\
(cls, numpy.average(wo))
print " * %s : stddev OUT degree centrality (weighted) : %.10f" % \
(cls, numpy.sqrt(numpy.var(wo)))
print " * %s : max OUT degrees centrality (weighted): %s" % (
cls, top(wo))
del wo
timr.stop('centrality')
if options.power_law:
with Timr('power law'):
for cls, vs in g.classes.iteritems():
if not vs:
continue
indegrees = vs['weighted_indegree']
try:
alpha_exp = ig.statistics.power_law_fit(indegrees, xmin=6)
print " * %s : alpha exp IN degree distribution : %10f " %\
(cls, alpha_exp)
except ValueError:
print >> sys.stderr,\
" * %s : alpha exp IN degree distribution : ERROR" %\
(cls,)
if options.histogram:
list_with_index = lambda degrees, idx: [(degree, idx)
for degree in degrees
if degree]
all_list = []
nogrp_indegrees = g.g.vs.select(sysop_ne=True,
bureaucrat_ne=True,
steward_ne=True,
founder_ne=True,
bot_ne=True)['weighted_indegree']
all_list += list_with_index(nogrp_indegrees, 1)
sysops_indegrees = g.classes['sysop']['weighted_indegree']
all_list += list_with_index(sysops_indegrees, 2)
burs_indegrees = g.classes['bureaucrat']['weighted_indegree']
all_list += list_with_index(burs_indegrees, 3)
stewards_indegrees = g.classes['steward']['weighted_indegree']
all_list += list_with_index(stewards_indegrees, 4)
founders_indegrees = g.classes['founder']['weighted_indegree']
all_list += list_with_index(founders_indegrees, 5)
bots_indegrees = g.classes['bot']['weighted_indegree']
all_list += list_with_index(bots_indegrees, 6)
if options.gnuplot:
f = open('hist.dat', 'w')
else:
f = open('%swiki-%s-hist.dat' % (lang, date), 'w')
all_list.sort(reverse=True)
for indegree, grp in all_list:
for _ in range(grp - 1):
print >> f, 0,
print >> f, indegree,
for _ in range(grp, 6):
print >> f, 0,
print >> f, ""
f.close()
if options.gnuplot:
from popen2 import Popen3
process = Popen3('gnuplot hist.gnuplot')
process.wait()
os.rename('hist.png', '%swiki-%s-hist.png' % (lang, date))
os.rename('hist.dat', '%swiki-%s-hist.dat' % (lang, date))
if options.plot:
## TODO: evaluate if this can be done with
## http://bazaar.launchpad.net/~igraph/igraph/0.6-main/revision/2018
with Timr('plot'):
import math
## filter:
#print len(g.g.vs), len(g.g.es)
#g.set_weighted_degree(type=ig.OUT)
#g.g = g.g.subgraph(g.g.vs.select(weighted_indegree_ge=10,
# weighted_outdegree_ge=1))
#g.g.write_graphml('itwiki-20100729-stub-meta-history_in10_out1.graphml')
#print len(g.g.vs), len(g.g.es)
bots = g.g.vs.select(bot=True)
bots['color'] = ('purple', ) * len(bots)
logging.debug('bots: ok')
anonyms = g.g.vs.select(anonymous=True)
anonyms['color'] = ('blue', ) * len(anonyms)
sysops = g.g.vs.select(sysop=True)
sysops['color'] = ('yellow', ) * len(sysops)
bur_sysops = g.g.vs.select(bureaucrat=True, sysop=True)
bur_sysops['color'] = ('orange', ) * len(bur_sysops)
g.g.vs['size'] = [
math.sqrt(v['weighted_indegree'] + 1) * 10 for v in g.g.vs
]
logging.debug('plot: begin')
ig.plot(g.g,
target=lang + "_general.png",
bbox=(0, 0, 8000, 8000),
edge_color='grey',
layout='drl')
logging.debug('plot: end')
weights = g.g.es['weight']
max_weight = max(weights)
g.g.es['color'] = [(255. * e['weight'] / max_weight, 0., 0.)
for e in g.g.es]
g.g.es['width'] = weights
ig.plot(g.g,
target=lang + "_weighted_edges.png",
bbox=(0, 0, 4000, 2400),
layout='fr',
vertex_label=' ')
if options.as_table:
tablr.stop()
#tablr.printHeader()
#tablr.printData()
tablr.saveInDjangoModel()
if options.adjacency:
giant = g.g.clusters().giant()
#destAdj = "%s/%swiki-%s-adj.csv" % (os.path.split(fn)[0], lang, date)
destAdj = "%swiki-%s-adj.csv" % (lang, date)
#destRec = "%s/%swiki-%s-rec.csv" % (os.path.split(fn)[0], lang, date)
destRec = "%swiki-%s-rec.csv" % (lang, date)
sg.Graph(giant).writeAdjacencyMatrix(destAdj, 'username')
sg.Graph(giant).writeReciprocityMatrix('username', destRec)
if options.users_role:
l = g.get_user_class('username',
('anonymous', 'bot', 'bureaucrat', 'sysop'))
#destUR = "%s/%swiki-%s-ur.csv" % (os.path.split(fn)[0], lang, date)
destUR = "%swiki-%s-ur.csv" % (lang, date)
with open(destUR, 'w') as f:
for username, role in sorted(l):
print >> f, "%s,%s" % (username, role)
from random import shuffle
#destCls = "%s/%swiki-%s-%%s.csv" % (os.path.split(fn)[0], lang, date)
destCls = "%swiki-%s-%%s.csv" % (lang, date)
for cls in ('anonymous', 'bot', 'bureaucrat', 'sysop', 'normal_user'):
users = g.classes[cls]['username']
shuffle(users)
with open(destCls % cls, 'w') as f:
for username in users:
print >> f, \
("%s,http://vec.wikipedia.org/w/index.php?title=" + \
"Discussion_utente:%s&action=history&offset=" + \
"20100000000001") % (username, username)
def main():
op = create_option_parser()
(options, args) = op.parse_args()
if len(args) != 1:
print "Insert one (and only one) file to process\n"
op.print_help()
sys.exit(2)
fn = args[0]
lang, date, type_ = mwlib.explode_dump_filename(fn)
g = sg.load(fn)
g.time_slice_subgraph(start=options.start, end=options.end)
g.invert_edge_attr('weight', 'length')
vn = len(g.g.vs) # number of vertexes
en = len(g.g.es) # number of edges
timr = Timr()
if options.as_table:
tablr = Tablr()
tablr.start(1024*32, lang)
if options.group or options.users_role or options.histogram:
for group_name, group_attr in groups.iteritems():
g.defineClass(group_name, group_attr)
print ' * %s : nodes number : %d' % (group_name,
len(g.classes[group_name]))
else:
g.defineClass('all', {})
print " * filename: %s" % (fn,)
print " * lang: %s" % (lang,)
print " * date: %s" % (date,)
if options.details:
with Timr("details"):
print " * nodes number: %d" % (vn,)
print " * edges number: %d" % (en,)
nodes_with_outdegree = len(g.g.vs.select(_outdegree_ge=1))
nodes_with_indegree = len(g.g.vs.select(_indegree_ge=1))
self_loop_edges = len([edge for edge in g.g.es \
if edge.target == edge.source])
print " * nodes with out edges number: %d (%6f%%)" % (
nodes_with_outdegree, 100.*nodes_with_outdegree/vn)
print " * nodes with in edges number: %d (%6f%%)" % (
nodes_with_indegree, 100.*nodes_with_indegree/vn)
print " * max weights on edges : %s" % top(g.g.es['weight'])
print " * self-loop edges: %d" % self_loop_edges
#print " * diameter : %6f" % g.g.diameter(weights='length')
#print " * average weight : %6f" % numpy.average(g.g.es['weight'])
if options.density or options.reciprocity:
with Timr('density&reciprocity'):
for cls, vs in g.classes.iteritems():
if not len(vs) > 1:
continue
subgraph = vs.subgraph()
print " * %s : density : %.10f" % (cls, subgraph.density())
print " * %s : reciprocity : %.10f" % (cls,
subgraph.reciprocity())
if options.degree:
with Timr('degree'):
g.g.vs['indegree'] = g.g.degree(type=ig.IN)
g.g.vs['outdegree'] = g.g.degree(type=ig.OUT)
for cls, vs in g.classes.iteritems():
if not vs:
continue
ind = numpy.array(vs['indegree'])
outd = numpy.array(vs['outdegree'])
print " * %s : mean IN degree (no weights): %f" % (
cls, numpy.average(ind))
print " * %s : mean OUT degree (no weights): %f" % (
cls, numpy.average(outd))
print " * %s : max IN degrees (no weights): %s" % (cls,
top(ind))
print " * %s : max OUT degrees (no weights): %s" % (cls,
top(outd))
print " * %s : stddev IN degree (no weights): %f" % (
cls, numpy.sqrt(numpy.var(ind)))
print " * %s : stddev OUT degree (no weights): %f" % (
cls, numpy.sqrt(numpy.var(outd)))
if options.transitivity:
##print " * transitivity: %f" % (nx.transitivity(g), )
pass
if options.summary:
# don't use with --as-table
print " * summary: %s" % (g.g.summary(), )
if options.distance:
with Timr('split clusters'):
vc = g.g.clusters()
size_clusters = vc.sizes()
giant = vc.giant()
print " * length of 5 max clusters: %s" % top(size_clusters)
#print " * #node in 5 max clusters/#all nodes: %s" % top(
# [1.*cluster_len/vn for cluster_len in size_clusters])
if options.distance:
with Timr('distance'):
gg = sg.Graph(giant)
print " * average distance in the giant component: %f" % \
gg.averageDistance(weight='length')
print " * average hops in the giant component: %f" % \
gg.averageDistance()
#print "Average distance 2: %f" % giant.average_path_length(True,
# False)
if options.efficiency:
with Timr('efficiency'):
print " * efficiency: %f" % g.efficiency(weight='length')
##TODO: compute for centrality only if "all" or "degree"
if (options.plot or options.histogram or options.power_law or
options.centrality):
with Timr('set weighted indegree'):
g.set_weighted_degree()
if options.centrality:
timr.start('centrality')
centralities = options.centrality.split(',')
if 'all' in centralities:
centralities = 'betweenness,pagerank,degree'.split(',')
if set(centralities).difference(
'betweenness,pagerank,degree'.split(',')):
logging.error('Unknown centrality')
sys.exit(0)
if "betweenness" in centralities:
print >> sys.stderr, "betweenness"
g.g.vs['bw'] = g.g.betweenness(weights='length', directed = True)
#g.g.vs['ev'] = g.g.evcent(weights='weight') # eigenvector centrality
if 'pagerank' in centralities:
print >> sys.stderr, "pagerank"
g.g.vs['pr'] = g.g.pagerank(weights='weight') # pagerank
if 'degree' in centralities:
print >> sys.stderr, "outdegree"
g.set_weighted_degree(type=ig.OUT)
#total_weights = sum(g.g.es['weight'])
max_edges = vn*(vn-1)
for cls, vs in g.classes.iteritems():
if not vs:
continue
if "betweenness" in centralities:
norm_betweenness = numpy.array(g.classes[cls]['bw'])/max_edges
print " * %s : average betweenness : %.10f" % (
cls, numpy.average(norm_betweenness))
print " * %s : stddev betweenness : %.10f" % (
cls, numpy.sqrt(numpy.var(norm_betweenness)))
print " * %s : max betweenness: %s" % (
cls, top(numpy.array(g.classes[cls]['bw'])/max_edges))
#print " * Average eigenvector centrality : %6f" % numpy.average(
# g.vs['ev'])
if 'pagerank' in centralities:
print " * %s : average pagerank : %.10f" % (
cls, numpy.average(g.classes[cls]['pr']))
print " * %s : stddev pagerank : %.10f" % (
cls, numpy.sqrt(numpy.var(g.classes[cls]['pr'])))
print " * %s : max pagerank: %s" % (
cls, top(g.classes[cls]['pr']))
if 'degree' in centralities:
wi = g.classes[cls]['weighted_indegree']
print " * %s : average IN degree centrality (weighted): %.10f" % (
cls, numpy.average(wi))
print " * %s : stddev IN degree centrality (weighted): %.10f" % (
cls, numpy.sqrt(numpy.var(wi)))
print " * %s : max IN degrees centrality (weighted): %s" % (
cls, top(wi))
del wi
wo = g.classes[cls]['weighted_outdegree']
print " * %s : average OUT degree centrality (weighted) : %.10f" %\
(cls, numpy.average(wo))
print " * %s : stddev OUT degree centrality (weighted) : %.10f" % \
(cls, numpy.sqrt(numpy.var(wo)))
print " * %s : max OUT degrees centrality (weighted): %s" % (
cls, top(wo))
del wo
timr.stop('centrality')
if options.power_law:
with Timr('power law'):
for cls, vs in g.classes.iteritems():
if not vs:
continue
indegrees = vs['weighted_indegree']
try:
alpha_exp = ig.statistics.power_law_fit(indegrees, xmin=6)
print " * %s : alpha exp IN degree distribution : %10f " %\
(cls, alpha_exp)
except ValueError:
print >> sys.stderr,\
" * %s : alpha exp IN degree distribution : ERROR" %\
(cls,)
if options.histogram:
list_with_index = lambda degrees, idx: [(degree, idx) for degree
in degrees if degree]
all_list = []
nogrp_indegrees = g.g.vs.select(sysop_ne=True, bureaucrat_ne=True,
steward_ne=True, founder_ne=True,
bot_ne=True)['weighted_indegree']
all_list += list_with_index(nogrp_indegrees, 1)
sysops_indegrees = g.classes['sysop']['weighted_indegree']
all_list += list_with_index(sysops_indegrees, 2)
burs_indegrees = g.classes['bureaucrat']['weighted_indegree']
all_list += list_with_index(burs_indegrees, 3)
stewards_indegrees = g.classes['steward']['weighted_indegree']
all_list += list_with_index(stewards_indegrees, 4)
founders_indegrees = g.classes['founder']['weighted_indegree']
all_list += list_with_index(founders_indegrees, 5)
bots_indegrees = g.classes['bot']['weighted_indegree']
all_list += list_with_index(bots_indegrees, 6)
if options.gnuplot:
f = open('hist.dat', 'w')
else:
f = open('%swiki-%s-hist.dat' % (lang, date), 'w')
all_list.sort(reverse=True)
for indegree, grp in all_list:
for _ in range(grp - 1):
print >> f, 0,
print >> f, indegree,
for _ in range(grp, 6):
print >> f, 0,
print >> f, ""
f.close()
if options.gnuplot:
from popen2 import Popen3
process = Popen3('gnuplot hist.gnuplot')
process.wait()
os.rename('hist.png', '%swiki-%s-hist.png' % (lang, date))
os.rename('hist.dat', '%swiki-%s-hist.dat' % (lang, date))
if options.plot:
## TODO: evaluate if this can be done with
## http://bazaar.launchpad.net/~igraph/igraph/0.6-main/revision/2018
with Timr('plot'):
import math
## filter:
#print len(g.g.vs), len(g.g.es)
#g.set_weighted_degree(type=ig.OUT)
#g.g = g.g.subgraph(g.g.vs.select(weighted_indegree_ge=10,
# weighted_outdegree_ge=1))
#g.g.write_graphml('itwiki-20100729-stub-meta-history_in10_out1.graphml')
#print len(g.g.vs), len(g.g.es)
bots = g.g.vs.select(bot=True)
bots['color'] = ('purple',)*len(bots)
logging.debug('bots: ok')
anonyms = g.g.vs.select(anonymous=True)
anonyms['color'] = ('blue',)*len(anonyms)
sysops = g.g.vs.select(sysop=True)
sysops['color'] = ('yellow',)*len(sysops)
bur_sysops = g.g.vs.select(bureaucrat=True, sysop=True)
bur_sysops['color'] = ('orange',)*len(bur_sysops)
g.g.vs['size'] = [math.sqrt(v['weighted_indegree']+1)*10 for v
in g.g.vs]
logging.debug('plot: begin')
ig.plot(g.g, target=lang+"_general.png", bbox=(0, 0, 8000, 8000),
edge_color='grey', layout='drl')
logging.debug('plot: end')
weights = g.g.es['weight']
max_weight = max(weights)
g.g.es['color'] = [(255.*e['weight']/max_weight, 0., 0.) for e
in g.g.es]
g.g.es['width'] = weights
ig.plot(g.g, target=lang+"_weighted_edges.png", bbox=(0, 0, 4000,
2400),
layout='fr', vertex_label=' ')
if options.as_table:
tablr.stop()
#tablr.printHeader()
#tablr.printData()
tablr.saveInDjangoModel()
if options.adjacency:
giant = g.g.clusters().giant()
#destAdj = "%s/%swiki-%s-adj.csv" % (os.path.split(fn)[0], lang, date)
destAdj = "%swiki-%s-adj.csv" % (lang, date)
#destRec = "%s/%swiki-%s-rec.csv" % (os.path.split(fn)[0], lang, date)
destRec = "%swiki-%s-rec.csv" % (lang, date)
sg.Graph(giant).writeAdjacencyMatrix(destAdj, 'username')
sg.Graph(giant).writeReciprocityMatrix('username', destRec)
if options.users_role:
l = g.get_user_class('username', ('anonymous', 'bot', 'bureaucrat',
'sysop'))
#destUR = "%s/%swiki-%s-ur.csv" % (os.path.split(fn)[0], lang, date)
destUR = "%swiki-%s-ur.csv" % (lang, date)
with open(destUR, 'w') as f:
for username, role in sorted(l):
print >> f, "%s,%s" % (username, role)
from random import shuffle
#destCls = "%s/%swiki-%s-%%s.csv" % (os.path.split(fn)[0], lang, date)
destCls = "%swiki-%s-%%s.csv" % (lang, date)
for cls in ('anonymous', 'bot', 'bureaucrat', 'sysop', 'normal_user'):
users = g.classes[cls]['username']
shuffle(users)
with open(destCls % cls, 'w') as f:
for username in users:
print >> f, \
("%s,http://vec.wikipedia.org/w/index.php?title="+\
"Discussion_utente:%s&action=history&offset="+\
"20100000000001") % (username, username)
