def analyse_community(graph, save_name):
#partition = louvain.best_partition(graph)
partition = infomap.infomap(graph)
#dendro = louvain.generate_dendrogram(graph)
dendro, handle = infomap.iteration_loop(graph)
print 'Number of communities: ' + str(len(set(partition.values())))
logging.info('Number of communities: ' + str(len(set(partition.values()))))
com_node_map = {}
for community in set(partition.values()):
com_node_map[community] = len([nodes for nodes in partition.keys() if partition[nodes] == community])
for k, v in com_node_map.items():
print 'Community ' + str(k+1) + ' has ' + str(v) + ' nodes'
logging.info('Community ' + str(k+1) + ' has ' + str(v) + ' nodes')
nx.set_node_attributes(graph, 'community', partition)
link_ratio(graph, partition)
no_pe_com_map_list = nodes_per_community(dendro)
community_size_distribution(no_pe_com_map_list, save_name)
drawNetwork(graph, save_name)
import os
import sys
sys.path.append("louvain")
import louvain
sys.path.append("infomap")
import infomap
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import logging
global_frame_list = []
dispatch = {
'infomap_partition': infomap.infomap(graph)
'louvain_partition': louvain.best_partition(graph)
'infomap_dendro': infomap.iteration_loop(graph)
'louvain_dendro': louvain.generate_dendrogram(graph)
}
def run_mini_pipeline():
datadir = 'data'
for root, dirs, filenames in os.walk(datadir):
for file in filenames:
print 'computing subject ' + str(filenames.index(file))
logging.info('computing subject ' + str(filenames.index(file)))
file_handle = os.path.join(root, file)
df = pd.read_csv(file_handle, sep='\t')
np_ts_matrix=df.as_matrix()
