def __init__(self, state):
"""
Initializes your Player. You can set up persistent state, do analysis
on the input graph, engage in whatever pre-computation you need. This
function must take less than Settings.INIT_TIMEOUT seconds.
--- Parameters ---
state : State
The initial state of the game. See state.py for more information.
"""
graph = state.get_graph()
#e_dict = nx.eccentricity(graph)
station = nx.center(graph)[0] if nx.center(graph) else graph.nodes()[len(graph.nodes()) / 2] #max(e_dict, key=lambda i: x[i])
self.stations.append(station)
def nodelist(network):
"""Returns a drawable nodelist.
This is designed for the concentric circle-based drawing functions of networkx.
"""
# first off, find most largestly connected (hub) server for the center
center = nx.center(network)
center = center[0] # returns a list of centers, and we don't want that
# and create the layers off that
added_nodes = [
center,
]
shells = [
(center, ),
] # holds recursive shells
any_added = True
while any_added:
any_added = False
new_shell = []
for node in shells[-1]:
new_neighbors = nx.all_neighbors(network, node)
for n in new_neighbors:
if n not in added_nodes:
new_shell.append(n)
added_nodes.append(n)
any_added = True
if len(new_shell) > 0:
new_shell = (new_shell) # make a tuple
shells.append(new_shell)
return shells
def Distance_measures(P, tipo, ruta):
RUTA = ruta + '/NetWX/files/'
path = Path(RUTA)
path.mkdir(parents = True,exist_ok = True)
P_center = []
P_diameter = []
P_extrema_bounding = []
P_periphery = []
P_radius = []
for i in range(len(P)):
P_center.append(center(P[i]))
P_diameter.append(diameter(P[i]))
P_extrema_bounding.append(extrema_bounding(P[i]))
P_periphery.append(periphery(P[i]))
P_radius.append(radius(P[i]))
P_center = DataFrame(P_center)
P_diameter = DataFrame(P_diameter)
P_extrema_bounding = DataFrame(P_extrema_bounding)
P_periphery = DataFrame(P_periphery)
P_radius = DataFrame(P_radius)
P_center.to_csv(RUTA + tipo + " - center.txt", sep = '\t',header = None,index = False)
P_diameter.to_csv(RUTA + tipo +" - diameter.txt", sep = '\t',header = None,index = False)
P_extrema_bounding.to_csv(RUTA + tipo +" - extrema_bounding.txt", sep = '\t',header = None,index = False)
P_periphery.to_csv(RUTA + tipo + " - periphery.txt", sep = '\t',header = None,index = False)
P_radius.to_csv(RUTA + tipo + " - radius.txt", sep = '\t',header = None,index = False)
def twopi_layout(nx_graph, center=None):
"""
Render the twopi layout. Ported from C code available at
https://github.com/ellson/graphviz/blob/master/lib/twopigen/circle.c
:param networkx.MultiDiGraph nx_graph: the networkx graph
:param str center: the centered node
:return:
"""
if len(nx_graph.nodes()) == 0:
return {}
if len(nx_graph.nodes()) == 1:
return {nx_graph.nodes()[0]: (0, 0)}
#nx_graph = nx_graph.to_undirected()
data = ExplorerScene._init_layout(nx_graph)
if not center:
center = networkx.center(nx_graph)[0]
ExplorerScene._set_parent_nodes(nx_graph, data, center)
ExplorerScene._set_subtree_size(nx_graph, data)
data[center]['span'] = 2 * math.pi
ExplorerScene._set_subtree_spans(nx_graph, data, center)
data[center]['theta'] = 0.0
ExplorerScene._set_positions(nx_graph, data, center)
distances = networkx.shortest_path_length(nx_graph.to_undirected(),
center)
nx_pos = {}
for node in nx_graph.nodes():
hyp = distances[node] + 1
theta = data[node]['theta']
nx_pos[node] = (hyp * math.cos(theta) * 100,
hyp * math.sin(theta) * 100)
return nx_pos
def twopi_layout(nx_graph, center=None):
"""
Render the twopi layout. Ported from C code available at
https://github.com/ellson/graphviz/blob/master/lib/twopigen/circle.c
:param networkx.MultiDiGraph nx_graph: the networkx graph
:param str center: the centered node
:return:
"""
if len(nx_graph.nodes()) == 0:
return {}
if len(nx_graph.nodes()) == 1:
return {nx_graph.nodes()[0]: (0, 0)}
#nx_graph = nx_graph.to_undirected()
data = ExplorerScene._init_layout(nx_graph)
if not center:
center = networkx.center(nx_graph)[0]
ExplorerScene._set_parent_nodes(nx_graph, data, center)
ExplorerScene._set_subtree_size(nx_graph, data)
data[center]['span'] = 2 * math.pi
ExplorerScene._set_subtree_spans(nx_graph, data, center)
data[center]['theta'] = 0.0
ExplorerScene._set_positions(nx_graph, data, center)
distances = networkx.shortest_path_length(nx_graph.to_undirected(), center)
nx_pos = {}
for node in nx_graph.nodes():
hyp = distances[node] + 1
theta = data[node]['theta']
nx_pos[node] = (hyp * math.cos(theta) * 100, hyp * math.sin(theta) * 100)
return nx_pos
def group_nodes(graph, size=3):
root = nx.center(graph)[0]
groups = []
group = []
for n in nx.dfs_preorder_nodes(graph, root):
if graph.degree(n) < 2: continue
# hit the terminal
if len(group) >= 1 and not set(graph[n]).intersection(set(group)):
groups.append(group)
group = []
# size cut
if len(group) >= size:
groups.append(group)
group = []
group.append(n)
if group not in groups: groups.append(group)
for i in range(len(groups)):
# build sub-graph that includes degree = 1 elements
group = groups[i]
for n in group:
for nn in graph[n]:
if graph.degree(nn) == 1: group.append(nn)
groups[i] = group
return groups
def updateGraphStats(self, graph):
origgraph = graph
if nx.is_connected(graph):
random = 0
else:
connectedcomp = nx.connected_component_subgraphs(graph)
graph = max(connectedcomp)
if len(graph) > 1:
pathlength = nx.average_shortest_path_length(graph)
else:
pathlength = 0
# print graph.nodes(), len(graph), nx.is_connected(graph)
stats = {
"radius": nx.radius(graph),
"density": nx.density(graph),
"nodecount": len(graph.nodes()),
"center": nx.center(graph),
"avgcluscoeff": nx.average_clustering(graph),
"nodeconnectivity": nx.node_connectivity(graph),
"components": nx.number_connected_components(graph),
"avgpathlength": pathlength
}
# print "updated graph stats", stats
return stats
def answer_twelve():
# Your Code Here
G = answer_six()
center = nx.center(G)[0]
node = answer_eleven()[0]
return len(nx.minimum_node_cut(G, center, node))
def get_nations_network_by_year(year):
cursor = get_db().cursor()
cursor.execute(
"""SELECT reporting, reporting_slug, partner, partner_slug, flow*Unit/ifnull(rate,1) as flow, expimp,
reporting_continent, partner_continent,reporting_type,partner_type
FROM flow_joined
WHERE reporting NOT LIKE "Worl%%"
AND partner NOT LIKE "Worl%%"
AND partner_slug IS NOT "NA"
AND partner_slug IS NOT "Unknown"
AND flow is not NULL
AND year = %s
""" % (year))
table = [list(r) for r in cursor]
json_sql_response = []
for row in table:
json_sql_response.append({
"reporting": row[0],
"reporting_id": row[1],
"partner": row[2],
"partner_id": row[3],
"flow": row[4],
"expimp": row[5],
"reporting_continent": row[6],
"partner_continent": row[7],
"reporting_type": row[8],
"partner_type": row[9]
})
# Create a graph instance
G = nx.Graph()
nodes = []
for row in table:
nodes.append(row[1])
nodes.append(row[3])
# add edge to the graph
G.add_edge(row[1], row[3])
nodes = set(nodes)
# add nodes to graph
G.add_nodes_from(nodes)
if len(G.nodes()) > 0:
stats = {
"average_clustering": nx.average_clustering(G),
"center": nx.center(G),
"diameter": nx.diameter(G),
"eccentricity": nx.eccentricity(G)
}
else:
stats = []
json_response = {}
json_response["stats"] = stats
json_response["network"] = json_sql_response
return json.dumps(json_response, encoding="UTF8")
def answer_ten():
# Your Code Here
G_sc = answer_six()
center = nx.center(G_sc)
return set(center) # Your Answer Here
def mkMatch(network, method):
g_ = network
if 'kclick' in method: # k-click communities
# k_ = int(method.replace('kclick', ''))
svs = [i for i in x.algorithms.community.k_clique_communities(g_, 10)]
gg = x.Graph()
for i, sv in enumerate(svs):
gg.add_node(i, weight=len(sv), children=sv)
elif method == 'lab': # label propagation
svs = [
i for i in x.algorithms.community.label_propagation_communities(g_)
]
gg = x.Graph()
for i, sv in enumerate(svs):
gg.add_node(i, weight=len(sv), children=sv)
elif method == 'cp':
sub = [i for i in x.connected_component_subgraphs(g_)]
g = sub[0]
per = set(x.periphery(g))
if len(sub) > 1:
for per_ in sub[1:]:
per.update(per_.nodes())
cen = set(x.center(g))
pc = per.union(cen)
nodes = set(g.nodes())
inter = nodes.difference(pc)
gg = x.Graph()
gg.add_node(0, label='center', weight=len(cen), children=cen)
gg.add_node(1, label='intermediary', weight=len(inter), children=inter)
gg.add_node(2, label='periphery', weight=len(per), children=per)
return gg # meta-network
def adjustUMI(umiList, dist=1):
umi_count, umi_set_list = countList(umiList)
# 1. compare all umis find singleton and creat Graph to get center
umiG = nx.Graph()
singleton_umi = deepcopy(umi_set_list)
umi_map = {}
for u1, u2 in combinations(umi_set_list, 2):
if hDistance(u1, u2) <= 1:
umiG.add_edges_from([(u1, u2)])
if u1 in singleton_umi:
singleton_umi.remove(u1)
if u2 in singleton_umi:
singleton_umi.remove(u2)
if singleton_umi:
for eachSgl in singleton_umi:
umi_map[eachSgl] = eachSgl
if list(nx.connected_components(umiG)):
for eachG in nx.connected_component_subgraphs(umiG):
centerUmilist = nx.center(eachG)
if len(centerUmilist) == 1:
for rawUmi in list(nx.connected_components(eachG))[0]:
umi_map[rawUmi] = centerUmilist[0]
else:
max_umiCount = 0
centerUmi = ""
for cenUmi in centerUmilist:
if umi_count[cenUmi] > max_umiCount:
max_umiCount = umi_count[cenUmi]
centerUmi = cenUmi
for rawUmi in list(nx.connected_components(eachG))[0]:
umi_map[rawUmi] = centerUmi
return umi_map
def answer_twelve():
G_sc = answer_six()
center = nx.center(G_sc)[0]
mynode = answer_eleven()[0]
return len(nx.minimum_node_cut(G_sc, center, mynode))
def info(graph):
"""Function that take the graph whose information is to be displayed
Information includes -
number of nodes - Total number of nodes
number of edges - Total number of edges
Average degree - Degree of nodes (Indegree+ Outdegree)
radius - Minimum eccentricity
diameter - Maximum eccentricity
eccentricity - Eccentricity of a node v is the maximum distance from v to
all other nodes in graph.
center - Set of nodes with eccentricity equal to radius.
periphery - Set of nodes with eccentricity equal to the diameter.
Density
"""
try:
print("-" * 10, "Node Attributes", "-" * 10)
print(n.info(graph))
print("-" * 10, "Distance Measures", "-" * 10)
print("radius: %d" % n.radius(graph))
print("diameter: %d" % n.diameter(graph))
print("eccentricity: %s" % n.eccentricity(graph))
print("center: %s" % n.center(graph))
print("periphery: %s" % n.periphery(graph))
print("density: %s" % n.density(graph))
except Exception as ex:
print(ex)
return graph
def SaveGraph(self, filename):
logging.debug("From SVNFileNetwork.SaveGraph")
print "saving the file network graph to %s" % filename
plt.clf()
#pos = NX.spring_layout(self, dim=2, iterations=20)
logging.info("starting layout using pydot_layout")
pos = NX.pydot_layout(self, prog='neato')
print "finished layout"
logging.info("finished layout using pydot_layout")
plt.figure(figsize=(8.0, 8.0))
NX.draw_networkx_nodes(self, pos, node_size=10)
NX.draw_networkx_edges(self, pos)
# display revision nodes in different colors
revnodes = self.getNodes(SVNNetworkRevisionNode)
NX.draw_networkx_nodes(
self, pos, revnodes, node_color='g', node_size=30)
# display center nodes with larger size
NX.draw_networkx_nodes(
self, pos, NX.center(self), node_color='b', node_size=50)
ax = plt.gca()
plt.setp(ax.get_yticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=False)
plt.savefig(filename, dpi=100, format="png")
self.PrintGraphStat()
print "Saved ..."
def updateGraphStats(self, graph):
origgraph = graph
if nx.is_connected(graph):
random = 0
else:
connectedcomp = nx.connected_component_subgraphs(graph)
graph = max(connectedcomp)
if len(graph) > 1:
pathlength = nx.average_shortest_path_length(graph)
else:
pathlength = 0
# print graph.nodes(), len(graph), nx.is_connected(graph)
stats = {
"radius": nx.radius(graph),
"density": nx.density(graph),
"nodecount": len(graph.nodes()),
"center": nx.center(graph),
"avgcluscoeff": nx.average_clustering(graph),
"nodeconnectivity": nx.node_connectivity(graph),
"components": nx.number_connected_components(graph),
"avgpathlength": pathlength
}
# print "updated graph stats", stats
return stats
def print_graph_info(graph): e = nx.eccentricity(graph) print 'graph with %u nodes, %u edges' % (len(graph.nodes()), len(graph.edges())) print 'radius: %s' % nx.radius(graph, e) # min e print 'diameter: %s' % nx.diameter(graph, e) # max e print 'len(center): %s' % len(nx.center(graph, e)) # e == radius print 'len(periphery): %s' % len(nx.periphery(graph, e)) # e == diameter
def SaveGraph(self, filename):
logging.debug("From SVNFileNetwork.SaveGraph")
print("saving the file network graph to %s" % filename)
plt.clf()
#pos = NX.spring_layout(self, dim=2, iterations=20)
logging.info("starting layout using pydot_layout")
pos = NX.pydot_layout(self, prog='neato')
print("finished layout")
logging.info("finished layout using pydot_layout")
plt.figure(figsize=(8.0, 8.0))
NX.draw_networkx_nodes(self, pos, node_size=10)
NX.draw_networkx_edges(self, pos)
# display revision nodes in different colors
revnodes = self.getNodes(SVNNetworkRevisionNode)
NX.draw_networkx_nodes(
self, pos, revnodes, node_color='g', node_size=30)
# display center nodes with larger size
NX.draw_networkx_nodes(
self, pos, NX.center(self), node_color='b', node_size=50)
ax = plt.gca()
plt.setp(ax.get_yticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=False)
plt.savefig(filename, dpi=100, format="png")
self.PrintGraphStat()
print("Saved ...")
def basic_stats(self):
#not decided on what level to deal with this yet:
#either return error un not dealing with unconnected files,
#or making it deal with unconnected files: the latter.
#How about with dealing with each independently.
# if not nx.is_connected(g):
# conl= nx.connected_components(g)
# for n in conl:
# turn n into graph if it isnt
# calculate ec, per, cnt
# how and when to visualise the subgraphs?
# iterate to next n
if nx.is_connected(self.nx_graph):
ec = nx.eccentricity(self.nx_graph)
else:
ec = 'NA - graph is not connected'
per = nx.periphery(self.nx_graph)
cnt = nx.center(self.nx_graph)
result = { #"""fast betweenness algorithm"""
'bbc': nx.brandes_betweenness_centrality(self.nx_graph),
'tn': nx.triangles(self.nx_graph), # number of triangles
'ec': ec,
'per': per,
'cnt': cnt,
'Per': self.nx_graph.subgraph(per),
'Cnt': self.nx_graph.subgraph(cnt)
}
return result
def Properties(G, verbose=0):
txt = ''
pathlengths = []
for v in G.nodes():
spl = nx.single_source_shortest_path_length(G, v)
#print('%s %s' % (v,spl),file=sys.stderr)
for p in spl.values():
pathlengths.append(p)
txt += "average shortest path length %s\n" % (sum(pathlengths) /
len(pathlengths))
# histogram of path lengths
dist = {}
for p in pathlengths:
if p in dist:
dist[p] += 1
else:
dist[p] = 1
txt += "length #paths"
verts = dist.keys()
for d in sorted(verts):
txt += '%s %d\n' % (d, dist[d])
txt += 'radius: %d\n' % nx.radius(G)
txt += 'diameter: %d\n' % nx.diameter(G)
#txt+= 'eccentricity: %s\n' % nx.eccentricity(G)
txt += 'center: %s\n' % nx.center(G)
#txt+= 'periphery: %s\n' % nx.periphery(G)
txt += 'density: %s\n' % nx.density(G)
return txt
def extract_simple_features(self, graph):
res = {}
try:
print('diameter: ', nx.diameter(graph))
print('eccentricity: ', nx.eccentricity(graph))
print('center: ', nx.center(graph))
print('periphery: ', nx.periphery(graph))
res['connected'] = True
except Exception as e:
print('Graph not connected')
res['connected'] = False
res['density'] = '{:.6f}'.format(nx.density(graph))
res['Avg_degree'] = '{:.6f}'.format(
sum([i[1] for i in nx.degree(graph)]) / len(nx.degree(graph)))
res['Avg_weight'] = '{:.6f}'.format(
sum([graph[edge[0]][edge[1]]['weight'] for edge in graph.edges]) /
len([graph[edge[0]][edge[1]]['weight'] for edge in graph.edges]))
res['edges'] = len(graph.edges)
res['nodes'] = len(graph.nodes)
res['self_loops'] = len(list(nx.nodes_with_selfloops(graph)))
res['edge_to_node_ratio'] = '{:.6f}'.format(
len(graph.nodes) / len(graph.edges))
res['negative_edges'] = nx.is_negatively_weighted(graph)
return res
def parseCSVGraphAnalysis(G):
global headers
row = ''
data = nx.info(G).split('\n')
nameG = data[0].split(':')[1].strip()
if saveGEXF:
nx.write_gexf(G, nameG + '.gexf')
if headers:
row = 'Name' + separator + separator.join(
val.split(':')[0] for val in data[2:])
row += (separator + 'Density') if selected[0] else ''
row += (separator + 'Diameter') if selected[1] else ''
row += (separator + 'Degrees') if selected[2] else ''
row += (separator + 'Eccentricity') if selected[3] else ''
row += (separator + 'Center') if selected[4] else ''
row += (separator + 'Periphery') if selected[5] else ''
row += '\n'
headers = False
row += nameG + separator + separator.join(
val.split(':')[1].strip() for val in data[2:])
row += (separator + str(nx.density(G))) if selected[0] else ''
row += (separator + str(nx.diameter(G))) if selected[1] else ''
row += (separator + str(nx.degree(G))) if selected[2] else ''
row += (separator + str(nx.eccentricity(G))) if selected[3] else ''
row += (separator + str(nx.center(G))) if selected[4] else ''
row += (separator + str(nx.periphery(G))) if selected[5] else ''
return row
def nodelist(network):
"""Returns a drawable nodelist.
This is designed for the concentric circle-based drawing functions of networkx.
"""
# first off, find most largestly connected (hub) server for the center
center = nx.center(network)
center = center[0] # returns a list of centers, and we don't want that
# and create the layers off that
added_nodes = [center, ]
shells = [(center,), ] # holds recursive shells
any_added = True
while any_added:
any_added = False
new_shell = []
for node in shells[-1]:
new_neighbors = nx.all_neighbors(network, node)
for n in new_neighbors:
if n not in added_nodes:
new_shell.append(n)
added_nodes.append(n)
any_added = True
if len(new_shell) > 0:
new_shell = (new_shell) # make a tuple
shells.append(new_shell)
return shells
def analyze_distance():
center = nx.center(G)
barycenter = nx.barycenter(G)
diameter = nx.diameter(G)
table = prettytable.PrettyTable(['center', 'barycenter', 'diameter'])
table.add_row([center, barycenter, diameter])
print(table)
def make_graph(graph_object, color_map):
"""Makes the graph and returns variables needed for further visualization
:return center: center node
:return title: the label of the center node used for titling visualizations
:return node_count: the number of nodes
:return fig1: the graph
"""
fig1 = plt.figure()
center = nx.center(graph_object)[0]
title = graph_object.nodes[center]['label']
# degrees = nx.degree(graph_object)
node_count = len(nx.nodes(graph_object))
# layout for display
pos = nx.spring_layout(graph_object)
# draw function
nx.draw(graph_object, pos=pos, node_color=color_map, node_size=1000)
# add node labels
node_labels = nx.get_node_attributes(graph_object, 'label')
nx.draw_networkx_labels(graph_object, pos=pos, labels=node_labels)
# add edge labels
edge_labels = nx.get_edge_attributes(graph_object, 'PARTICIPATION_TYPE')
nx.draw_networkx_edge_labels(graph_object, pos, edge_labels)
return center, title, node_count, fig1
def get_nations_network_by_year(year):
cursor = get_db().cursor()
cursor.execute("""SELECT reporting, reporting_slug, partner, partner_slug, Flow, expimp,
reporting_continent, partner_continent,reporting_type,partner_type
FROM flow_joined
WHERE reporting NOT LIKE "Worl%%"
AND partner NOT LIKE "Worl%%"
AND Flow != "null"
AND year = %s
"""%(year)
)
table = [list(r) for r in cursor]
json_sql_response=[]
for row in table:
json_sql_response.append({
"reporting": row[0],
"reporting_id": row[1],
"partner": row[2],
"partner_id": row[3],
"flow": row[4],
"expimp": row[5],
"reporting_continent": row[6],
"partner_continent": row[7],
"reporting_type": row[8],
"partner_type": row[9]
})
# Create a graph instance
G=nx.Graph()
nodes = []
for row in table:
nodes.append(row[1])
nodes.append(row[3])
# add edge to the graph
G.add_edge(row[1], row[3])
nodes = set(nodes)
# add nodes to graph
G.add_nodes_from(nodes)
if len(G.nodes())>0:
stats = {
"average_clustering": nx.average_clustering(G),
"center": nx.center(G),
"diameter": nx.diameter(G),
"eccentricity": nx.eccentricity(G)
}
else:
stats=[]
json_response = {}
json_response["stats"] = stats
json_response["network"] = json_sql_response
return json.dumps(json_response,encoding="UTF8")
def verify_graph(nx_graph):
g_gen = Graph()
for edge in nx.edges(nx_graph):
g_gen.add_edge(*edge)
diameter, center = g_gen.diameter_center()
assert (center in nx.center(nx_graph)) == True
assert diameter == nx.diameter(nx_graph)
def answer_ten():
# Your Code Here
# r = nx.radius(G_sc)
# e = nx.eccentricity(G_sc)
# n = [node for node in e.items() if node[1] == r]
# set([node[0] for node in n])
return set(nx.center(G_sc)) # Your Answer Here
def find_central(G):
try:
cen = nx.center(G)
except nx.exception.NetworkXError:
print('Graph is not connected')
cen = {}
print('Central: ', cen)
return cen
def point_to_new_cluster(clust):
try:
sub = self.collapsed_clusters[clust.cluster]
return utilities.Cluster_Entry(sub, nx.center(self.clusters[sub])[0])
except KeyError:
return clust
def answer_twelve():
# Your Code Here
n = answer_eleven()[0]
c = nx.center(G_sc)[0]
# cut = nx.minimum_node_cut(G=G_sc, s=c, t=n)
conn = nx.node_connectivity(G_sc, s=c, t=n) - 1
return conn # Your Answer Here
def print_graph_info(graph):
e = nx.eccentricity(graph)
print 'graph with %u nodes, %u edges' % (len(
graph.nodes()), len(graph.edges()))
print 'radius: %s' % nx.radius(graph, e) # min e
print 'diameter: %s' % nx.diameter(graph, e) # max e
print 'len(center): %s' % len(nx.center(graph, e)) # e == radius
print 'len(periphery): %s' % len(nx.periphery(graph, e)) # e == diameter
def find_central(G): #set of nodes that have eccentricity equal to the radius
try:
cen = nx.center(G)
except nx.exception.NetworkXError:
print('Graph is not connected')
cen = {}
print('Central: ', cen)
return cen
def get_graph_center(G): center= nx.center(G) non_center = [node for node in G.nodes if node not in center] G2 = G.copy() G2.remove_nodes_from(non_center) return center, G2
def answer_ten():
G = answer_six()
# Manual calculate:
# ec = nx.eccentricity(G)
# radius = nx.radius(G)
# return set([node for node, value in ec.items() if value==radius])
return set(nx.center(G))
def getDiffusionModelLabels(adjacency,
repetitions=1000,
threshold=0.5,
patient0=None):
import networkx as nx
import ndlib.models.ModelConfig as mc
from ndlib.models.epidemics.IndependentCascadesModel import IndependentCascadesModel
if patient0 is None:
patient0 = nx.center(nx.from_numpy_matrix(adjacency))[
0] # A-4: 50, B-4: 71; A-6: 42, B-4: 26
A = adjacency.copy()
g = nx.from_numpy_matrix(A)
cfg = mc.Configuration()
cfg.add_model_initial_configuration("Infected", set(list([patient0])))
if threshold is None:
threshold = 0.5
for e in g.edges():
cfg.add_edge_configuration("threshold", e, threshold)
model = IndependentCascadesModel(g)
model.set_initial_status(cfg)
if repetitions is None:
repetitions = 1000
res_probs = np.zeros((A.shape[0], repetitions))
res_Probs = np.zeros(res_probs.shape)
iteration_results = []
for rep in range(repetitions):
model.reset()
itr = 0
while True:
itr += 1
iteration_result = model.iteration()
iteration_results.append(iteration_result)
for node, status in iteration_result['status'].items():
if status == 1:
res_probs[node, rep] = itr
if len(iteration_result['status']) == 0:
break
res_Probs[res_probs[:, rep] > 1,
rep] = 1.0 / (res_probs[res_probs[:, rep] > 1, rep] - 1.)
res_Prob = np.mean(res_Probs, 1)
res_Prob[patient0] = np.mean(res_Prob[np.argwhere(
adjacency[patient0, :])][:, 0])
# import matplotlib.pyplot as plt
# plt.imshow(np.repeat(res_Prob.reshape((len(res_Prob),1)),10,axis=1))
return res_Prob
def jordanCenter(graph):
if len(graph.nodes) == 1:
return graph.nodes[0], 0
if not nx.is_connected(graph):
conComp = max(nx.connected_components(graph), key=len)
graph = graph.subraph(conComp)
center = nx.center(graph)[0]
radius = nx.radius(graph)
return center, radius
def answer_twelve():
nodes_to_cut = set()
G_sc = answer_six()
target = answer_eleven()[0]
sources = nx.center(G_sc)
for source in sources:
min_nodes_to_cut = nx.minimum_node_cut(G_sc, s=source, t=target)
nodes_to_cut.update(min_nodes_to_cut)
n_min_nodes_to_cut = len(nodes_to_cut)
return n_min_nodes_to_cut
def answer_twelve():
# Your Code Here
G_sc = answer_six()
node_11 = answer_eleven()[0]
center_nodes = nx.center(G_sc)
no_cut_nodes = len(
[nx.minimum_node_cut(G_sc, cn, node_11) for cn in center_nodes][0])
return no_cut_nodes
def f33(self):
return "ND"
start = 0
try:
res = len(nx.center(self.G))
except nx.exception.NetworkXError:
res = "ND"
stop = 0
# self.feature_time.append(stop - start)
return res
def f33(self):
return "ND"
start = 0
try:
res = len(nx.center(self.G))
except nx.exception.NetworkXError:
res = "ND"
stop = 0
# self.feature_time.append(stop - start)
return res
def _get_prototype_id(self):
node_graph = nx.Graph()
for i in range(self.total_instances):
for j in range(i + 1, self.total_instances):
node_graph.add_edge(i, j, weight = \
self.base_similarity_matrix[i, j])
T = nx.minimum_spanning_tree(node_graph)
list_of_centers = nx.center(T)
return list_of_centers[0]
def LeaderInCluster( graphObj, moldict ):
leaderList = []
for eachSubGraph in nx.connected_component_subgraphs( graphObj ):
centerlist = nx.center(eachSubGraph)
if len(centerlist) < 1:
raise "A subgraph with less than 1 center"
leaderID = RandomPickFromList(centerlist)
leaderList.append( leaderID )
graphSize = len(eachSubGraph)
moldict[ leaderID ][ "size" ] = graphSize
return leaderList
def test_center(testgraph):
"""
Testing center function for graphs.
"""
a, b = testgraph
nx_center = nx.center(a)
sg_center = sg.digraph_distance_measures.center(b, b.nodes())
nx_center = array(nx_center, dtype = uint32)
nx_center.sort()
sg_center.sort()
assert_equal(nx_center, sg_center)
def __get_centroid(self, cluster=None):
"""Get the centroid of a cluster in the graph or the centroid of the graph.
Parameters
----------
cluster : list
Nodes list of a cluster.
Returns
-------
centroid_node : int
Centroid node of a cluster.
"""
# centroid of a cluster
if cluster:
subgraph = self.graph.subgraph(cluster)
centroid = nx.center(subgraph)
# centroid of the graph
else:
centroid = nx.center(self.graph)
# choose randomly if more than one centroid found
centroid_node = choice(centroid) if len(centroid) > 1 else centroid
return centroid_node
def connectivity(self):
components = list(nx.connected_component_subgraphs(self.G))
print('Connected components number: ')
print(len(components))
giant = components.pop(0)
print('Giant component radius: ')
print(nx.radius(giant))
print('Giant component diameter: ')
print(nx.diameter(giant))
center = nx.center(giant)
print('Giant component center: ')
for i in xrange(len(center)):
print(self.singer_dict[int(center[i])].split('|')[0])
inf = self.get_graph_info(giant)
for i in xrange(len(inf)):
print(inf[i])
def append_central_nodes(graph, table_data):
"""Append periphery nodes to table_data."""
# first, setup all values to false
for node in graph.nodes(data=True):
node_name = gen_username(node[1]['first_name'],
node[1]['last_name'],
node[0])
if node_name in table_data:
table_data[node_name].append('')
else:
table_data[node_name] = ['']
# second, update these values to True for periphery
for uid in nx.center(graph):
node_name = gen_username(graph.node[uid]['first_name'],
graph.node[uid]['last_name'],
uid)
table_data[node_name][-1] = 'True'
def generateGraphFromFile():
g = nx.Graph()
nodes = []
with open("File.txt") as f:
for line in f:
line = line.replace("\n","")
lineArr = line.split(" ")
dummy = tuple(lineArr[0:3])
nodes.append(dummy)
g.add_weighted_edges_from(nodes)
matrixPaths = nx.floyd_warshall_numpy(g,nodelist=None, weight='weight')
#print matrixPaths
listNodes = g.nodes()
#print listNodes
center = nx.center(g,e=None)
#print center
pos=nx.spring_layout(g)
return (center, matrixPaths, listNodes,g,pos)
def report_components(g):
components = nx.connected_component_subgraphs(g)
print "Components: %d" % len(components)
c_data = {}
for i in range(len(components)):
c = components[i]
if len(c.nodes()) > 5: # Avoid reporting on many small components
c_data["nodes"] = len(c.nodes())
c_data["edges"] = len(c.edges())
c_data["avg_clustering"] = nx.average_clustering(c)
c_data["diameter"] = nx.diameter(c)
c_data["radius"] = nx.radius(c)
c_data["center"] = len(nx.center(c))
c_data["periphery"] = len(nx.periphery(c))
print "* Component %d:" % i
for k in c_data:
print "--- %s: %s" % (k, c_data[k])
return c_data
def SaveMST(self, filename):
logging.debug("From SVNFileNetwork.SaveMST")
mstGraph = self._getMSTGraph()
print "saving MST of the file network graph to %s" % filename
plt.clf()
#pos = NX.spring_layout(self, dim=2, iterations=20)
logging.info("starting layout using pydot_layout")
pos = NX.pydot_layout(mstGraph, prog='neato')
print "finished layout"
logging.info("finished layout using pydot_layout")
plt.figure(figsize=(8.0, 8.0))
NX.draw_networkx_nodes(mstGraph, pos, node_size=10)
NX.draw_networkx_edges(mstGraph, pos)
# display center nodes with larger size
NX.draw_networkx_nodes(
mstGraph, pos, NX.center(mstGraph), node_color='b', node_size=50)
# create node labels
label = dict()
for node in mstGraph.nodes_iter():
dirname, fname = os.path.split(node.name())
label[node] = fname
NX.draw_networkx_labels(mstGraph, pos, label, font_size=8)
# disable X and Y tick labels
ax = plt.gca()
plt.setp(ax.get_yticklabels(), visible=False)
plt.setp(ax.get_xticklabels(), visible=False)
plt.savefig(filename, dpi=100, format="png")
print "Saved MST..."
print "saving MST treemap"
jsfilename, ext = os.path.splitext(filename)
jsfilename = jsfilename + '.js'
print "treemap filename %s" % jsfilename
outfile = open(jsfilename, "w")
self.treemapNodeDict['Root'].writejson(
outfile, 'closeness', 'betweenness')
outfile.close()
# Define the equivalent NetworkX graph to test against
gnx = nx.Graph()
gnx.add_edge(0, 1)
gnx.add_edge(1, 2)
gnx.add_edge(1, 3)
gnx.add_edge(4, 1)
assert g.degree(0) == gnx.degree(0)
assert g.degree(1) == gnx.degree(1)
assert g.degree(4) == gnx.degree(4)
diameter, center = g.diameter_center()
assert (center in nx.center(gnx)) == True
# Use network x to generate a random graph and replicate it as a custom graph.
# Then check it returns the same value for some functions
def verify_graph(nx_graph):
g_gen = Graph()
for edge in nx.edges(nx_graph):
g_gen.add_edge(*edge)
diameter, center = g_gen.diameter_center()
assert (center in nx.center(nx_graph)) == True
assert diameter == nx.diameter(nx_graph)
verify_graph(nx.sedgewick_maze_graph())
def extended_stats(G, connectivity=False, anc=False, ecc=False, bc=False, cc=False):
"""
Calculate extended topological stats and metrics for a graph.
Many of these algorithms have an inherently high time complexity. Global
topological analysis of large complex networks is extremely time consuming
and may exhaust computer memory. Consider using function arguments to not
run metrics that require computation of a full matrix of paths if they
will not be needed.
Parameters
----------
G : networkx multidigraph
connectivity : bool
if True, calculate node and edge connectivity
anc : bool
if True, calculate average node connectivity
ecc : bool
if True, calculate shortest paths, eccentricity, and topological metrics
that use eccentricity
bc : bool
if True, calculate node betweenness centrality
cc : bool
if True, calculate node closeness centrality
Returns
-------
stats : dict
dictionary of network measures containing the following elements (some
only calculated/returned optionally, based on passed parameters):
- avg_neighbor_degree
- avg_neighbor_degree_avg
- avg_weighted_neighbor_degree
- avg_weighted_neighbor_degree_avg
- degree_centrality
- degree_centrality_avg
- clustering_coefficient
- clustering_coefficient_avg
- clustering_coefficient_weighted
- clustering_coefficient_weighted_avg
- pagerank
- pagerank_max_node
- pagerank_max
- pagerank_min_node
- pagerank_min
- node_connectivity
- node_connectivity_avg
- edge_connectivity
- eccentricity
- diameter
- radius
- center
- periphery
- closeness_centrality
- closeness_centrality_avg
- betweenness_centrality
- betweenness_centrality_avg
"""
stats = {}
full_start_time = time.time()
# create a DiGraph from the MultiDiGraph, for those metrics that require it
G_dir = nx.DiGraph(G)
# create an undirected Graph from the MultiDiGraph, for those metrics that
# require it
G_undir = nx.Graph(G)
# get the largest strongly connected component, for those metrics that
# require strongly connected graphs
G_strong = get_largest_component(G, strongly=True)
# average degree of the neighborhood of each node, and average for the graph
avg_neighbor_degree = nx.average_neighbor_degree(G)
stats['avg_neighbor_degree'] = avg_neighbor_degree
stats['avg_neighbor_degree_avg'] = sum(avg_neighbor_degree.values())/len(avg_neighbor_degree)
# average weighted degree of the neighborhood of each node, and average for
# the graph
avg_weighted_neighbor_degree = nx.average_neighbor_degree(G, weight='length')
stats['avg_weighted_neighbor_degree'] = avg_weighted_neighbor_degree
stats['avg_weighted_neighbor_degree_avg'] = sum(avg_weighted_neighbor_degree.values())/len(avg_weighted_neighbor_degree)
# degree centrality for a node is the fraction of nodes it is connected to
degree_centrality = nx.degree_centrality(G)
stats['degree_centrality'] = degree_centrality
stats['degree_centrality_avg'] = sum(degree_centrality.values())/len(degree_centrality)
# calculate clustering coefficient for the nodes
stats['clustering_coefficient'] = nx.clustering(G_undir)
# average clustering coefficient for the graph
stats['clustering_coefficient_avg'] = nx.average_clustering(G_undir)
# calculate weighted clustering coefficient for the nodes
stats['clustering_coefficient_weighted'] = nx.clustering(G_undir, weight='length')
# average clustering coefficient (weighted) for the graph
stats['clustering_coefficient_weighted_avg'] = nx.average_clustering(G_undir, weight='length')
# pagerank: a ranking of the nodes in the graph based on the structure of
# the incoming links
pagerank = nx.pagerank(G_dir, weight='length')
stats['pagerank'] = pagerank
# node with the highest page rank, and its value
pagerank_max_node = max(pagerank, key=lambda x: pagerank[x])
stats['pagerank_max_node'] = pagerank_max_node
stats['pagerank_max'] = pagerank[pagerank_max_node]
# node with the lowest page rank, and its value
pagerank_min_node = min(pagerank, key=lambda x: pagerank[x])
stats['pagerank_min_node'] = pagerank_min_node
stats['pagerank_min'] = pagerank[pagerank_min_node]
# if True, calculate node and edge connectivity
if connectivity:
start_time = time.time()
# node connectivity is the minimum number of nodes that must be removed
# to disconnect G or render it trivial
stats['node_connectivity'] = nx.node_connectivity(G_strong)
# edge connectivity is equal to the minimum number of edges that must be
# removed to disconnect G or render it trivial
stats['edge_connectivity'] = nx.edge_connectivity(G_strong)
log('Calculated node and edge connectivity in {:,.2f} seconds'.format(time.time() - start_time))
# if True, calculate average node connectivity
if anc:
# mean number of internally node-disjoint paths between each pair of
# nodes in G, i.e., the expected number of nodes that must be removed to
# disconnect a randomly selected pair of non-adjacent nodes
start_time = time.time()
stats['node_connectivity_avg'] = nx.average_node_connectivity(G)
log('Calculated average node connectivity in {:,.2f} seconds'.format(time.time() - start_time))
# if True, calculate shortest paths, eccentricity, and topological metrics
# that use eccentricity
if ecc:
# precompute shortest paths between all nodes for eccentricity-based
# stats
start_time = time.time()
sp = {source:dict(nx.single_source_dijkstra_path_length(G_strong, source, weight='length')) for source in G_strong.nodes()}
log('Calculated shortest path lengths in {:,.2f} seconds'.format(time.time() - start_time))
# eccentricity of a node v is the maximum distance from v to all other
# nodes in G
eccentricity = nx.eccentricity(G_strong, sp=sp)
stats['eccentricity'] = eccentricity
# diameter is the maximum eccentricity
diameter = nx.diameter(G_strong, e=eccentricity)
stats['diameter'] = diameter
# radius is the minimum eccentricity
radius = nx.radius(G_strong, e=eccentricity)
stats['radius'] = radius
# center is the set of nodes with eccentricity equal to radius
center = nx.center(G_strong, e=eccentricity)
stats['center'] = center
# periphery is the set of nodes with eccentricity equal to the diameter
periphery = nx.periphery(G_strong, e=eccentricity)
stats['periphery'] = periphery
# if True, calculate node closeness centrality
if cc:
# closeness centrality of a node is the reciprocal of the sum of the
# shortest path distances from u to all other nodes
start_time = time.time()
closeness_centrality = nx.closeness_centrality(G, distance='length')
stats['closeness_centrality'] = closeness_centrality
stats['closeness_centrality_avg'] = sum(closeness_centrality.values())/len(closeness_centrality)
log('Calculated closeness centrality in {:,.2f} seconds'.format(time.time() - start_time))
# if True, calculate node betweenness centrality
if bc:
# betweenness centrality of a node is the sum of the fraction of
# all-pairs shortest paths that pass through node
start_time = time.time()
betweenness_centrality = nx.betweenness_centrality(G, weight='length')
stats['betweenness_centrality'] = betweenness_centrality
stats['betweenness_centrality_avg'] = sum(betweenness_centrality.values())/len(betweenness_centrality)
log('Calculated betweenness centrality in {:,.2f} seconds'.format(time.time() - start_time))
log('Calculated extended stats in {:,.2f} seconds'.format(time.time()-full_start_time))
return stats
def test_center(self):
assert_equal(set(networkx.center(self.G)),set([6, 7, 10, 11]))
rows and columns. We may also think of a submatrix as formed by deleting
the remaining rows and columns from M.
Given: An inconsistent character table C on at most 100 taxa.
Return: A submatrix of C′ representing a consistent character table on
the same taxa and formed by deleting a single row of C.
(If multiple solutions exist, you may return any one.)
'''
import numpy as np
import networkx as nx
def reverse_symbol(a):
return (a == 0).astype(int)
table = open('rosalind_cset.txt').read().split()
mat = np.array([list(i.rstrip()) for i in table], dtype=int)
n = len(table)
G = nx.Graph()
for i in range(n-1):
for j in range(1, n):
a = mat[i] + mat[j]
b = mat[i] + reverse_symbol(mat[j])
if 0 in a and 2 in a and 0 in b and 2 in b:
G.add_edge(i, j)
table.pop(nx.center(G)[0])
open('rosalind_cset_sub.txt', 'wt').write('\n'.join(table))
def step(self, state):
"""
Determine actions based on the current state of the city. Called every
time step. This function must take less than Settings.STEP_TIMEOUT
seconds.
--- Parameters ---
state : State
The state of the game. See state.py for more information.
--- Returns ---
commands : dict list
Each command should be generated via self.send_command or
self.build_command. The commands are evaluated in order.
"""
self.current_order_edges = set()
graph = state.get_graph()
commands = []
if not self.has_built_station:
station = max([node for node in nx.center(graph)], key=graph.degree)
commands.append(self.build_command(station))
self.stations.append(station)
# this one can always be assumed to be built
self.has_built_station = True
# Check if new station was successfully built
if self.new_station is not None and state.get_graph().node[self.new_station]["is_station"]:
self.stations.append(self.new_station)
self.new_station = None
pending_orders = state.get_pending_orders()
RATIO = 3
if len(self.stations) < self.max_stations:
if len(pending_orders) > sum([graph.degree(station) for station in self.stations]) / RATIO:
# New station
available_nodes = [node for node in graph.nodes() if node not in self.stations]
order_nodes = [o.get_node() for o in pending_orders]
def heuristic(n):
t = (graph.degree(n), min([nx.shortest_path_length(graph, n, s) for s in self.stations]))
# geometric mean of these two
# closest to orders
return (
-sum([nx.shortest_path_length(graph, n, order_node) for order_node in order_nodes]),
(t[0] * t[1]) ** (1 / 2),
)
self.new_station = max(available_nodes, key=heuristic)
commands.append(self.build_command(self.new_station))
# do not use it yet, check next turn if it was built
if len(pending_orders) > 0:
# Choose orders to fulfill
chosen_orders = sorted(pending_orders, key=lambda k: state.money_from(k))
# Assign each order to closest station
# Generate graph of free edges
self.available_graph = state.get_graph()
self.available_graph.remove_edges_from(
[edge for edge in self.available_graph.edges() if self.available_graph.edge[edge[0]][edge[1]]["in_use"]]
)
for order in chosen_orders:
key = lambda x: Player.shortest_from_station(graph, x, order.get_node())
possible_stations = sorted(self.stations, key=key)
# Check each station by order starting from closest
for station in possible_stations:
if station is None: # dummy, no path actually exists
continue
try:
paths = list(nx.all_shortest_paths(self.available_graph, station, order.get_node()))
except nx.NetworkXNoPath:
continue
# TODO: choose a good path, not just the first one in the list, closest to station/order is best?
found = False
for path in paths:
if self.path_is_valid(state, path):
commands.append(self.send_command(order, path))
self.current_order_edges |= set(path)
found = True
break
if found:
break
return commands
def main ():
""" main """
global options, args, pg
# TODO: Do something more interesting here...
pg = nx.read_gml('/home/cyn/FFHS/NA-15-ZH/PVA3/power.gml')
print("Exercise 3:\n---------------------------------------------------")
print("Nodes: "), pg.number_of_nodes()
print("Edges: "), pg.number_of_edges()
graph_degree = pg.degree()
print("Degree of Nodes: "), graph_degree
print("\nExercise 4:\n---------------------------------------------------")
average_degree(pg)
print("Degree_histogram list: "), nx.degree_histogram(pg)
export_histogram()
print("\nExercise 5:\n---------------------------------------------------")
max_degree = find_highest_degree(pg)
print("Highest degree: "), max_degree
print("Node/s with highest degree: "), \
find_nodes_with_degree(pg,max_degree)
cc = list(nx.connected_component_subgraphs(pg))
print("Number of CC in the Power-Grid: "), len(cc)
print("\nExercise 6:\n---------------------------------------------------")
print("Diameter: "), nx.diameter(pg)
print("Center: "), nx.center(pg)
print("\nExercise 7:\n---------------------------------------------------")
subnodes = list()
for x in range(10, max_degree+1):
if len(find_nodes_with_degree(pg,x)) != 0:
subnodes.extend(find_nodes_with_degree(pg,x))
sg = pg.subgraph(subnodes)
scc = list(nx.connected_component_subgraphs(sg))
print("Number of CC in Subgraph: "), len(scc)
max_cc = 0
largest_cc_id = 0
for x in range(0, len(scc)):
if scc[x].number_of_nodes() > max_cc:
max_cc = scc[x].number_of_nodes()
largest_cc_id = x
print("Largest component: "), max_cc
nx.draw_spectral(scc[largest_cc_id])
plt.savefig('/home/cyn/FFHS/NA-15-ZH/PVA3/largest_connected_component.png')
plt.close()
print("\nExercise 8:\n---------------------------------------------------")
lg = make_largest_diameter_graph(10)
average_degree(lg)
print("Center: "), nx.center(lg)
nx.draw_spectral(lg)
plt.savefig('/home/cyn/FFHS/NA-15-ZH/PVA3/largest_diameter_10.png')
plt.close()
sdg = make_smallest_diameter_graph(10)
average_degree(sdg)
print("Center: "), nx.center(sdg)
nx.draw_circular(sdg)
plt.savefig('/home/cyn/FFHS/NA-15-ZH/PVA3/smallest_diameter_10.png')
plt.close()
def test_bound_center(self):
assert_equal(set(networkx.center(self.G, usebounds=True)), set([6, 7, 10, 11]))
def find_optimal_tree_root(nx_tree_input):
tree_root=nx.center(nx_tree_input)
return tree_root[0]
final_graph = {}
client = pymongo.MongoClient()
db = client.final
for val in db.collection_names():
final_graph[int(val)] = load_from_mongo('final', str(val))[0]['reciprocal_friends']
final_graph[24551258] = intersection
# Start creating the graph peice by peice.
G=nx.from_dict_of_lists(final_graph)
pos=nx.spring_layout(G) # positions for all nodes
nx.draw_networkx_nodes(G,pos,node_size=3)
nx.draw_networkx_edges(G,pos,width=1)
nx.draw_networkx_labels(G,pos,font_size=2,font_family='sans-serif')
plt.show()
print("the number of nodes of the network is " + str(G.size()))
print("The diameter of the network is: " + str(nx.diameter(G)))
print("The average distance of the network is " + str(nx.center(G)))
def center(net):
return setSize(nx.center(net,e=eccentricity_list(net)), net.number_of_nodes(),'center')
