def bucketed_sccs(g, stamp=''):
sccs = graphtools.get_sccs_from_graph(g)
buckets = _count_size_of_property(sccs)
def bucketed_sccs(g, stamp=''):
sccs = graphtools.get_sccs_from_graph(g)
buckets = _count_size_of_property(sccs)
largest_scc = []
for start, end in slices:
g = cv_from_btc(start * _HMS, end * _HMS)
if len(g) == 0:
continue
stamp = str(start) + '_' + str(end)
tags_over_time.user_transaction_frequency(g, 'model_' + stamp)
tags_over_time.user_buy_frequency(g, 'model_' + stamp)
tags_over_time.user_sell_frequency(g, 'model_' + stamp)
undir_g = nx.Graph(g.copy())
avg_clust.append(nx.average_clustering(undir_g))
lccs.append(len(graphtools.get_lcc_from_graph(g)))
largest_scc.append(len(graphtools.get_sccs_from_graph(g)[0]))
dates.append(start) # hack for now to save time...
print 'finished %s tag over time' % i
i += 1
utils.save_lists(dates, avg_clust, stamp='model_avg_clust')
utils.save_lists(dates, lccs, stamp='model_lccs')
utils.save_lists(dates, largest_scc, stamp='model_largest_scc')
'''
_HMS = graphgen._HMS
slices = graphgen.generate_weighted_time_slices()
