def get_graphs():
directories = sorted(os.listdir('out'), key=lambda x:max([0]+[os.stat(os.path.join('out', x, y))[stat.ST_MTIME] for y in os.listdir(os.path.join('out', x))]), reverse=True)
for directory in directories:
for filename in os.listdir(os.path.join('out', directory)):
filename = os.path.join('out', directory, filename)
if filename.endswith('.xml'):
try:
yield directory, read_graphml(filename)
except Exception:
traceback.print_exc(file=sys.stderr)
pass
def get_graphs():
directories = sorted(os.listdir('out'), key=lambda x:max([0]+[os.stat(os.path.join('out', x, y))[stat.ST_MTIME] for y in os.listdir(os.path.join('out', x))]), reverse=True)
for directory in directories:
for filename in os.listdir(os.path.join('out', directory)):
filename = os.path.join('out', directory, filename)
if filename.endswith('.xml'):
try:
yield directory, read_graphml(filename)
except Exception:
traceback.print_exc(file=sys.stderr)
pass
def main():
# Read the graph and generate a adjacency matrix
g = read_graphml("data/Intellifiber.graphml")
matrix = g.get_adjacency_matrix()
"""
Initialize the genetics module with parameters
1: Number of individuals in a population
2: Number of elements in one individual
3: Percentage of parents to retain
4: Mutation rate
5: Random selection rate
"""
gen = Genetics(100, 3, 0.1, 0.05, 0.01)
# Generate initial population based on available graph vertices
p = gen.population(g.get_nodes())
generation_history = []
# Evolve
for i in xrange(1000):
p = gen.evolve(p, matrix, g.get_nodes())
grade = gen.grade(p, matrix)
print("Generation (" + str(i) + ") ==> " + str(grade))
generation_history.append(grade)
# Print best individual across all generations
print(str(gen.optimum) + " with a value of " + str(gen.optimum_value))
# Plot grade over generation
x = range(0, len(generation_history))
plot.plot(x, generation_history, linewidth=2)
plot.xlabel("Generation")
plot.ylabel("Average Fitness")
plot.grid(True)
plot.savefig("Result.png")
plot.show()
from operator import itemgetter
# {cmd} journal regex
assert len(sys.argv) == 3
_, journal, regex = sys.argv
assert os.path.isfile(journal + ".gml")
print "Compiling regex: %s" % regex
p = re.compile(regex, re.U)
print "Loading GraphML network graph for %s" % journal
g = read_graphml(journal + ".gml")
print "Creating a directional graph to hold just the citation chains"
filtered_g = nx.DiGraph()
filtered_g.level = {}
filtered_g.position = {}
filtered_g.year = {}
rows = {}
# unescape '_' in the Journal
journal = " ".join(journal.split("_"))
print "Scanning nodes for title regex matches"
hits = set()
import matplotlib.pyplot as plt
try:
from pylab import *
except:
pass
from graphml import read_graphml
import sys, os
if len(sys.argv) == 2:
fn = sys.argv[1]
print "Reading in %s" % fn
g = read_graphml(fn)
print "Generating a generalized laplacian"
l = nx.generalized_laplacian(g)
print "Calculating eigenvalues"
e = eigenvalues(l)
print ("Largest eigenvalue:", max(e))
print ("Smallest eigenvalue:", min(e))
# plot with matplotlib if we have it
# shows "semicircle" distribution of eigenvalues
try:
print "Trying to plot semicircle of eigenvalues"
hist(e, bins=100) # histogram with 100 bins
xlim(0, 2) # eigenvalues between 0 and 2
show()
# plt.savefig("%s.eigen.png" % fn)
except:
from operator import itemgetter
# {cmd} journal regex
assert len(sys.argv) == 3
_, journal, regex = sys.argv
assert os.path.isfile(journal+".gml")
print "Compiling regex: %s" % regex
p = re.compile(regex, re.U)
print "Loading GraphML network graph for %s" % journal
g = read_graphml(journal+".gml")
print "Creating a directional graph to hold just the citation chains"
filtered_g = nx.DiGraph()
filtered_g.level = {}
filtered_g.position = {}
filtered_g.year = {}
rows = {}
# unescape '_' in the Journal
journal = " ".join(journal.split("_"))
print "Scanning nodes for title regex matches"
hits = set()
#!/usr/bin/env python
try:
import matplotlib.pyplot as plt
except:
raise
import networkx as nx
from graphml import read_graphml
import sys, os
if len(sys.argv) == 2:
fn = sys.argv[1]
print "Reading in %s" % fn
g = read_graphml(fn)
print "Simulating spring network..."
pos = nx.spring_layout(g, iterations=50)
print "Drawing a simple graph"
plt.figure(figsize=(10, 10))
#nx.draw(g,pos,node_size=40)
nx.draw_circular(g, node_size=40)
#plt.savefig("%s.spring_no_label.png" % fn) # save as png
plt.show() # display
