mirror of https://github.com/r4sas/Niflheim-api
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
2.9 KiB
113 lines
2.9 KiB
1 year ago
|
import pygraphviz as pgv
|
||
|
import time
|
||
|
import json
|
||
|
import networkx as nx
|
||
|
from networkx.algorithms import centrality
|
||
|
import urllib.request
|
||
|
|
||
|
def position_nodes(nodes, edges):
|
||
|
G = pgv.AGraph(strict=True, directed=False, size='10!')
|
||
|
|
||
|
for n in nodes.values():
|
||
|
G.add_node(n.ip, label=n.label, coords=n.coords)
|
||
|
|
||
|
for e in edges:
|
||
|
G.add_edge(e.a.ip, e.b.ip, len=1.0)
|
||
|
|
||
|
G.layout(prog='neato', args='-Gepsilon=0.0001 -Gmaxiter=100000')
|
||
|
|
||
|
return G
|
||
|
|
||
|
def compute_betweenness(G):
|
||
|
ng = nx.Graph()
|
||
|
for start in G.iternodes():
|
||
|
others = G.neighbors(start)
|
||
|
for other in others:
|
||
|
ng.add_edge(start, other)
|
||
|
|
||
|
c = centrality.betweenness_centrality(ng)
|
||
|
|
||
|
for k, v in c.items():
|
||
|
c[k] = v
|
||
|
|
||
|
return c
|
||
|
|
||
|
def canonalize_ip(ip):
|
||
|
return ':'.join( i.rjust(4, '0') for i in ip.split(':') )
|
||
|
|
||
|
def load_db():
|
||
|
url = "http://[316:c51a:62a3:8b9::2]/result.json"
|
||
|
f = urllib.request.urlopen(url)
|
||
|
return dict(
|
||
|
[
|
||
|
(canonalize_ip(v[0]), v[1]) for v in
|
||
|
[
|
||
|
l.split(None)[:2] for l in
|
||
|
json.loads(f.read())["yggnodes"].keys()
|
||
|
]
|
||
|
if len(v) > 1
|
||
|
]
|
||
|
)
|
||
|
|
||
|
def get_graph_json(G):
|
||
|
max_neighbors = 1
|
||
|
for n in G.iternodes():
|
||
|
neighbors = len(G.neighbors(n))
|
||
|
if neighbors > max_neighbors:
|
||
|
max_neighbors = neighbors
|
||
|
print('Max neighbors: %d' % max_neighbors)
|
||
|
|
||
|
out_data = {
|
||
|
'created': int(time.time()),
|
||
|
'nodes': [],
|
||
|
'edges': []
|
||
|
}
|
||
|
|
||
|
centralities = compute_betweenness(G)
|
||
|
db = load_db()
|
||
|
|
||
|
for n in G.iternodes():
|
||
|
neighbor_ratio = len(G.neighbors(n)) / float(max_neighbors)
|
||
|
pos = n.attr['pos'].split(',', 1)
|
||
|
centrality = centralities.get(n.name, 0)
|
||
|
size = 5*(1 + 1*centrality)
|
||
|
name = db.get(canonalize_ip(n.name))
|
||
|
# If label isn't the default value, set name to that instead
|
||
|
if n.attr['label'] != n.name.split(':')[-1]: name = n.attr['label']
|
||
|
|
||
|
out_data['nodes'].append({
|
||
|
'id': n.name,
|
||
|
'label': name if name else n.attr['label'],
|
||
|
'name': name,
|
||
|
'coords': n.attr['coords'],
|
||
|
'x': float(pos[0]),
|
||
|
'y': float(pos[1]),
|
||
|
'color': _gradient_color(neighbor_ratio, [(100, 100, 100), (0, 0, 0)]),
|
||
|
'size': size,
|
||
|
'centrality': '%.4f' % centrality
|
||
|
})
|
||
|
|
||
|
for e in G.iteredges():
|
||
|
out_data['edges'].append({
|
||
|
'sourceID': e[0],
|
||
|
'targetID': e[1]
|
||
|
})
|
||
|
|
||
|
return json.dumps(out_data)
|
||
|
|
||
|
|
||
|
def _gradient_color(ratio, colors):
|
||
|
jump = 1.0 / (len(colors) - 1)
|
||
|
gap_num = int(ratio / (jump + 0.0000001))
|
||
|
|
||
|
a = colors[gap_num]
|
||
|
b = colors[gap_num + 1]
|
||
|
|
||
|
ratio = (ratio - gap_num * jump) * (len(colors) - 1)
|
||
|
|
||
|
r = int(a[0] + (b[0] - a[0]) * ratio)
|
||
|
g = int(a[1] + (b[1] - a[1]) * ratio)
|
||
|
b = int(a[2] + (b[2] - a[2]) * ratio)
|
||
|
|
||
|
return '#%02x%02x%02x' % (r, g, b)
|