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plot.py
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plot.py
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import networkx as nx
import matplotlib.pyplot as plt
import statistics
import os
from operator import itemgetter as i
from functools import cmp_to_key
# START utility functions
def cmp(a, b):
return (a > b) - (a < b)
def multikeysort(items, columns):
comparers = [
((i(col[1:].strip()), -1) if col.startswith('-') else (i(col.strip()), 1))
for col in columns
]
def comparer(left, right):
comparer_iter = (
cmp(fn(left), fn(right)) * mult
for fn, mult in comparers
)
return next((result for result in comparer_iter if result), 0)
return sorted(items, key=cmp_to_key(comparer))
# END utility functions
# input dir
stats_dir = "./stats"
stats_file = os.path.join(stats_dir, "stats.csv")
with open(stats_file, "r") as out_stats:
lines = out_stats.readlines()
skipped_first = False
graphs = []
for line in lines:
if not skipped_first: # skip first file
skipped_first = True
continue
# parse values
content = line.split(",")
n = int(content[0])
m = int(content[1])
k = int(content[2])
deg = float(content[3])
avg_clustering = float(content[4])
diameter = float(content[5])
average_shortest_path_length = float(content[6])
# append as a dict
graphs.append({
'n': n,
'm': m,
'k': k,
'deg': deg,
'avg_clustering': avg_clustering,
'diameter': diameter,
'average_shortest_path_length': average_shortest_path_length
})
# sort by k then n
graphs = multikeysort(graphs, ['k','n'])
# for each value of m
for m_run in [9,10,64,160]:
# initialize a new plot data set
plots = {
'k1': {
'degrees': [],
'nodes': [],
'diameter': [],
'average_shortest_path_length': [],
'avg_clustering': []
},
'k5': {
'degrees': [],
'nodes': [],
'diameter': [],
'average_shortest_path_length': [],
'avg_clustering': []
},
'k20': {
'degrees': [],
'nodes': [],
'diameter': [],
'average_shortest_path_length': [],
'avg_clustering': []
}
}
# for each network
for g in graphs:
k = g['k']
n = g['n']
m = g['m']
deg = g['deg']
diameter = g['diameter']
avg_clustering = g['avg_clustering']
average_shortest_path_length = g['average_shortest_path_length']
# aggregate data with k and m as keys
if m == m_run:
plots[f'k{k}']["degrees"].append(deg)
plots[f'k{k}']["nodes"].append(n)
plots[f'k{k}']["diameter"].append(diameter)
plots[f'k{k}']["avg_clustering"].append(avg_clustering)
plots[f'k{k}']["average_shortest_path_length"].append(average_shortest_path_length)
# degree plot
for k in [1,5,20]:
plots_path = os.path.join("plots", f"avg_degree_m{m_run}.png")
plt.plot(plots[f"k{k}"]["nodes"],plots[f"k{k}"]["degrees"])
plt.xlabel('Nodes count')
plt.ylabel('Degree')
plt.title(f'm = {m_run}')
plt.gca().legend(['k=1','k=5','k=20'],loc='center right')
if k == 20: # generate only the last image with the 3 results
plt.savefig(plots_path, bbox_inches='tight')
plt.clf()
plt.clf()
# average_shortest_path_length plot
for k in [1,5,20]:
plots_path = os.path.join("plots", f"pathlength_m{m_run}.png")
plt.plot(plots[f"k{k}"]["nodes"],plots[f"k{k}"]["average_shortest_path_length"])
plt.xlabel('Nodes count')
plt.ylabel('Path length')
plt.title(f'm = {m_run}')
plt.gca().legend(['k=1','k=5','k=20'],loc="lower right")
if k == 20: # generate only the last image with the 3 results
plt.savefig(plots_path, bbox_inches='tight')
plt.clf()
plt.clf()
# avg_clustering plot
for k in [1,5,20]:
plots_path = os.path.join("plots", f"avg_clustering_m{m_run}.png")
plt.plot(plots[f"k{k}"]["nodes"],plots[f"k{k}"]["avg_clustering"])
plt.xlabel('Nodes count')
plt.ylabel('Clustering coefficient')
plt.title(f'm = {m_run}')
plt.gca().legend([f'k=1','k=5','k=20'])
if k == 20: # generate only the last image with the 3 results
plt.savefig(plots_path, bbox_inches='tight')
plt.clf()