def ba_calc(arguments):
ba_graph = helpers.load_network(arguments.ba_graph)
sh_paths = helpers.load_from_json(arguments.point_pairs)
out = arguments.out
min_stretch = arguments.min_stretch
max_stretch = arguments.max_stretch
max_c = len(sh_paths)
arguments.lb = arguments.lb if 0 <= arguments.lb <= max_c else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= max_c else max_c
arguments.lb, arguments.ub = (min(arguments.lb, arguments.ub),
max(arguments.lb, arguments.ub))
sh_paths = sh_paths[arguments.lb:arguments.ub]
vf_g_closeness = vft.convert_to_vf(ba_graph, vfmode=vft.CLOSENESS)
results = [[] for x in xrange(min_stretch, max_stretch + 1)]
for stretch in xrange(min_stretch, max_stretch + 1):
logger.info('Calculate results with stretch %d' % stretch)
result = ba_generator(ba_graph, sh_paths, stretch, vf_g_closeness,
arguments.progressbar)
results[stretch - min_stretch] = result
helpers.save_to_json(out, results)
def upwalk(upwalk_f, out_folder_path):
data = helpers.load_from_json(upwalk_f)
keys = sorted(list(set(list(data['RANDOM'].keys() + data['REAL'].keys()))))
with open('{}/upwalk'.format(out_folder_path), 'w') as f:
f.write('IDX;REAL;RANDOM\n')
for k in keys:
f.write('{};{};{}\n'.format(k, data['REAL'].get(k, 0),
data['RANDOM'].get(k, 0)))
def main():
parser = argparse.ArgumentParser(description='Calculate meta information for real traces', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('network')
parser.add_argument('meta')
parser.add_argument('output', type=argparse.FileType('w'))
# parser.add_argument('--vfmode', type=str, default='labeled', dest='vfmode',
# choices=['labeled', 'closeness'])
# for paralelization
parser.add_argument('--lower-bound', '-lb', type=int, default=0, dest='lb')
parser.add_argument('--upper-bound', '-ub', type=int, default=-1, dest='ub')
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('--with-prelabeled', action='store_true')
parser.add_argument('--with-closeness', action='store_true')
parser.add_argument('--with-degree', action='store_true')
parser.add_argument('--with-lp-hard', action='store_true')
parser.add_argument('--with-lp-soft', action='store_true')
# parser.add_argument('--with-lp', action='store_true')
# parser.add_argument('--with-vf', action='store_true')
parser.add_argument('--try-per-trace',
type=int, default=1, dest='try_per_trace')
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
g = helpers.load_network(arguments.network)
meta = helpers.load_from_json(arguments.meta)
arguments.lb = arguments.lb if 0 <= arguments.lb <= len(meta) else 0
arguments.ub = arguments.ub if 0 <= arguments.ub <= len(meta) else len(meta)
flags = {
FLAG_PRELABELED: arguments.with_prelabeled,
FLAG_CLOSENESS: arguments.with_closeness,
FLAG_DEGREE: arguments.with_degree,
FLAG_LP_HARD: arguments.with_lp_hard,
FLAG_LP_SOFT: arguments.with_lp_soft
}
# if arguments.vfmode == 'labeled': mode = vft.ORDER_PRELABELED
# elif arguments.vfmode == 'closeness': mode = vft.ORDER_CLOSENESS
# else: raise RuntimeError('Unhandled vfmode')
meta = meta[arguments.lb:arguments.ub]
# update meta at place
purify(g, meta, flags, arguments.try_per_trace, arguments.progressbar)
logger.info('Save to %s' % arguments.output)
helpers.save_to_json(arguments.output, meta)
def wrap_watts_trace_gen(args):
g = helpers.load_network(args.network)
traceroutes = helpers.load_from_json(args.original_traceroutes)
max_c = len(traceroutes)
args.lb = args.lb if 0 <= args.lb <= max_c else 0
args.ub = args.ub if 0 <= args.ub <= max_c else max_c
args.lb, args.ub = (min(args.lb, args.ub), max(args.lb, args.ub))
traceroutes = traceroutes[args.lb:args.ub]
watts_traceroutes = watts_trace_gen(g, traceroutes, args.progressbar)
helpers.save_to_json(args.traceroute_dest, watts_traceroutes)
def main():
parser = argparse.ArgumentParser(description='SANDBOX mode. Write something useful here', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('network')
parser.add_argument('meta')
parser.add_argument('--top-node-ratio',
type=float, default=.1, dest='top_node_ratio')
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
g = helpers.load_network(arguments.network)
meta = helpers.load_from_json(arguments.meta)
logger.info('Graph loaded from: %s' % arguments.network)
logger.info('Graph vertex count: %d' % g.vcount())
end = int(g.vcount() * arguments.top_node_ratio)
logger.info('Top node count: %d' % end)
try:
nodes = [(x.index, x['closeness']) for x in g.vs]
except KeyError:
logger.info('Calculate closeness values')
progress = progressbar1.DummyProgressBar(end=10, width=15)
if arguments.progressbar:
progress = progressbar1.AnimatedProgressBar(end=g.vcount(),
width=15)
for n in g.vs:
progress += 1
progress.show_progress()
closeness = g.closeness(n)
n['closeness'] = closeness
g.save('%s_with_closeness.gml' % arguments.network)
nodes = [(x.index, x['closeness']) for x in g.vs]
nodes = sorted(nodes, reverse=True, key=lambda x: x[1])
top_nodes = set([x[0] for x in nodes[:end]])
purify(g, meta, top_nodes, arguments.progressbar)
def ba_merge(arguments):
STRETCH, TRACE_COUNT, VF_COUNT, LP_COUNT = range(4)
out_file = arguments.out
results = dict()
for piece in arguments.res:
result_piece = helpers.load_from_json(piece)
for res in result_piece:
stretch = res[0]
try:
container = results[stretch]
except KeyError:
container = [stretch, 0, 0, 0]
results[stretch] = container
for idx in [TRACE_COUNT, VF_COUNT, LP_COUNT]:
container[idx] += res[idx]
results_list = []
for idx in sorted(results.iterkeys()):
results_list.append(results[idx])
helpers.save_to_json_file(out_file, results_list)
elif arguments.type == Networks.wiki:
traceroutes = wiki.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.metabolic:
traceroutes = metabolic.get_traceroutes(arguments.traceroutes_input)
elif arguments.type == Networks.wordnavi:
traceroutes = wordnavi.get_traceroutes(arguments.traceroutes_input)
else:
raise RuntimeError('Unknown network type')
msg = 'Save traceroutes to {trace}'.format(trace=arguments.json_traces)
logger.info(msg)
helpers.save_to_json(arguments.json_traces, traceroutes)
if "network" in arguments.convert:
# label network with caida labeling tool
# first load previously converted traceroutes
traceroutes = helpers.load_from_json(arguments.json_traces)
logger.info('Trace count: {c}'.format(c=len(traceroutes)))
# to increase accuracy
if arguments.type == Networks.airport:
traceroutes.extend([[y for y in reversed(x)] for x in traceroutes])
# convert with caida labeling tools
logger.info('Caida labeling the graph')
edge_list = helpers.caida_labeling(arguments.caida_folder, traceroutes,
arguments.network_cliques)
# save to given output file using caida output format
logger.info('Save to file %s' % arguments.network_output)
with open(arguments.network_output, 'w') as f:
for e in edge_list:
f.write('%s|%s|%s\n' % (e[0], e[1], e[2]))
def main():
parser = argparse.ArgumentParser(
description=('Syntetic route generator'),
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--progressbar', action='store_true')
parser.add_argument('--verbose', '-v', action='count', default=0)
parser.add_argument('network')
parser.add_argument('meta')
parser.add_argument('all_trace_out', metavar='all-trace-out')
parser.add_argument('syntetic_out', metavar='syntetic-out')
parser.add_argument('--trace-count',
'-tc',
type=int,
dest='trace_count',
default=5000)
parser.add_argument('--random-sample',
dest='random_sample',
action='store_true')
parser.add_argument('--closeness-error',
'-ce',
type=float,
dest='closeness_error',
default=0.0)
parser.add_argument('--core-limit-percentile',
'-cl',
type=int,
dest='core_limit',
default=0)
parser.add_argument('--toggle-node-error-mode', action='store_true')
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS) - 1, arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
show_progress = arguments.progressbar
g = helpers.load_network(arguments.network)
g = g.simplify()
meta = helpers.load_from_json(arguments.meta)
if arguments.random_sample:
random.shuffle(meta)
meta = meta[:arguments.trace_count]
N = g.vcount()
cl = sorted([x['closeness'] for x in g.vs], reverse=True)
logger.info('Min closeness: %s' % np.min(cl))
logger.info('Max closeness: %s' % np.max(cl))
logger.info('Mean closenss: %s' % np.mean(cl))
logger.info('10%% closeness: %s' % np.percentile(cl, 10))
logger.info('90%% closeness: %s' % np.percentile(cl, 90))
logger.info('Core limit: [r]%d%%[/]' % arguments.core_limit)
change_probability = 100 * arguments.closeness_error
logger.info('Change probability: [r]%6.2f%%[/]' % change_probability)
core_limit = np.percentile(cl, arguments.core_limit)
logger.info('Core limit in closeness: [bb]%f[/]' % core_limit)
if arguments.toggle_node_error_mode:
logger.info('[r]Node error mode[/]')
msg = (
"If given node's closeness >= core_limit then the new ",
"closeness in this node is ",
#"rand(closeness_error ... old closeness)"
"OLD_CLOSENSS * +/- closeness_error%")
logger.info(''.join(msg))
logger.info('Minimum node closeness: [g]%f[/]' %
arguments.closeness_error)
for n in g.vs:
if n['closeness'] < core_limit:
continue
# sign = -1 if random.uniform(-1, 1) < 0 else 1
# n['closeness'] = n['closeness'] * (1 + sign * arguments.closeness_error)
new_closeness = random.uniform(arguments.closeness_error,
n['closeness'])
n['closeness'] = new_closeness
g_labeled = vft.label_graph_edges(g, vfmode=vft.CLOSENESS)
peer_edge_count = len([x for x in g_labeled.es if x['dir'] == LinkDir.P])
logger.info('Peer edge count: %d' % peer_edge_count)
changed_edges = []
if not arguments.toggle_node_error_mode:
msg = ("If the closeness values of the endpoints in given edge is ",
"larger than the core_limit and ",
"random(0,1) < closeness_error then change the direction ",
"for this edge")
logger.info(''.join(msg))
changed_u = changed_d = 0
changed_edges = []
changed_edgess = []
for edge in g_labeled.es:
s, t = edge.source, edge.target
s_cl = g_labeled.vs[s]['closeness']
t_cl = g_labeled.vs[t]['closeness']
if (s_cl < core_limit or t_cl < core_limit): continue
if random.uniform(0, 1) > arguments.closeness_error: continue
# if abs(s_cl - t_cl) / min(s_cl, t_cl) > 0.02: continue
new_edge_dir = LinkDir.U if random.uniform(0,
1) > 0.5 else LinkDir.D
if new_edge_dir != edge['dir']:
if edge['dir'] == LinkDir.U:
changed_u += 1
else:
changed_d += 1
edge['dir'] = new_edge_dir
changed_edges.append(edge)
changed_edgess.append((edge.source, edge.target))
# if edge['dir'] == LinkDir.U:
# changed_u += 1
# changed_edgess.append((edge.source, edge.target))
# edge['dir'] = LinkDir.D
# changed_edges.append(edge)
# elif edge['dir'] == LinkDir.D:
# changed_d += 1
# changed_edgess.append((edge.source, edge.target))
# edge['dir'] = LinkDir.U
# changed_edges.append(edge)
logger.info('E count: %d' % g_labeled.ecount())
logger.info('Changed U: %d' % changed_u)
logger.info('Changed D: %d' % changed_d)
logger.info('Changed: %d' % (changed_d + changed_u))
changed_e = [(g_labeled.vs[e.source]['name'],
g_labeled.vs[e.target]['name']) for e in changed_edges]
changed_e = changed_e + [(x[1], x[0]) for x in changed_e]
changed_e = set(changed_e)
vf_g_closeness = vft.convert_to_vf(g,
vfmode=vft.CLOSENESS,
labeled_graph=g_labeled)
# e_colors = []
# for e in vf_g_closeness.es:
# if e.source < N and e.target < N: col = 'grey'
# elif e.source < N and e.target >= N: col = 'blue'
# elif e.source >= N and e.target >= N: col = 'red'
# else: col = 'cyan'
# e_colors.append(col)
# igraph.plot(vf_g_closeness, "/tmp/closeness.pdf",
# vertex_label=vf_g_closeness.vs['name'],
# vertex_size=0.2,
# edge_color=e_colors)
pairs = [(g.vs.find(x[helpers.TRACE][0]).index,
g.vs.find(x[helpers.TRACE][-1]).index, tuple(x[helpers.TRACE]))
for x in meta]
# pairs = list(set(pairs))
# random.shuffle(pairs)
# visited = set()
# pairs2 = []
# for x in pairs:
# k = (x[0], x[1])
# if k in visited: continue
# visited.add(k)
# visited.add((k[1], k[0]))
# pairs2.append(x)
# pairs = pairs2
traces = [x[2] for x in pairs]
stretches = []
syntetic_traces = []
sh_traces = []
base_traces = []
original_traces = []
bad = 0
progress = progressbar1.DummyProgressBar(end=10, width=15)
if show_progress:
progress = progressbar1.AnimatedProgressBar(end=len(pairs), width=15)
for s, t, trace_original in pairs:
progress += 1
progress.show_progress()
trace_original_idx = [g.vs.find(x).index for x in trace_original]
logger.debug('Original trace: %s -- %s -- %s',
[g.vs[x]['name'] for x in trace_original_idx],
vft.trace_to_string(g, trace_original_idx, vft.CLOSENESS),
[g.vs[x]['closeness'] for x in trace_original_idx])
sh_routes = g.get_all_shortest_paths(s, t)
sh_len = len(sh_routes[0])
sh_routes_named = [[g.vs[y]['name'] for y in x] for x in sh_routes]
sh_trace_name = random.choice(sh_routes_named)
base_trace_name = random.choice(sh_routes_named)
candidates = vf_g_closeness.get_all_shortest_paths(s + N, t + N)
candidates = [vft.vf_route_converter(x, N) for x in candidates]
# candidates = []
if len(candidates) == 0:
candidates = vft.get_shortest_vf_route(g_labeled,
s,
t,
mode='vf',
vf_g=vf_g_closeness,
_all=True,
vfmode=vft.CLOSENESS)
if len(candidates) == 0:
s_name, t_name = g.vs[s]['name'], g.vs[t]['name']
logger.debug("!!!No syntetic route from %s to %s" %
(s_name, t_name))
continue
logger.debug('Candidates from %s to %s:' %
(g.vs[s]['name'], g.vs[t]['name']))
for c in candidates:
logger.debug('%s -- %s -- %s' %
([g.vs[x]['name'] for x in c],
vft.trace_to_string(g_labeled, c, vft.PRELABELED),
[g.vs[x]['closeness'] for x in c]))
chosen_one = random.choice(candidates)
chosen_one_name = [g.vs[x]['name'] for x in chosen_one]
# print chosen_one
# print trace_original
# pretty_plotter.pretty_plot(g, trace_original_idx,
# chosen_one, changed_edgess,
# spec_color=(0, 0, 0, 155))
hop_stretch = len(chosen_one) - sh_len
stretches.append(hop_stretch)
trace_original_e = zip(trace_original, trace_original[1:])
chosen_one_e = zip(chosen_one_name, chosen_one_name[1:])
trace_affected = any([x in changed_e for x in trace_original_e])
chosen_affected = any([x in changed_e for x in chosen_one_e])
logger.debug('Trace affected: %s' % trace_affected)
logger.debug('Chosen affected: %s' % chosen_affected)
# if hop_stretch > 2:
# logger.debug('Base: %s' % trace_to_string(g_labeled, base_trace_name))
# logger.debug('SH: %s' % trace_to_string(g_labeled, sh_trace_name))
# logger.debug('Trace: %s' % trace_to_string(g_labeled, trace_original))
# logger.debug('Syntetic: %s' % trace_to_string(g_labeled, chosen_one_name))
if trace_affected or chosen_affected or hop_stretch > 2:
# pretty_plotter.pretty_plot_all(g, traces,
# chosen_one, changed_edgess,
# spec_color=(0, 0, 0, 255))
bad += 1
syntetic_traces.append(chosen_one_name)
sh_traces.append(sh_trace_name)
base_traces.append(base_trace_name)
original_traces.append(trace_original)
logger.debug('From %s to %s chosen one %s' %
(g.vs[s]['name'], g.vs[t]['name'], chosen_one_name))
result = zip(base_traces, sh_traces, original_traces, syntetic_traces)
helpers.save_to_json(arguments.all_trace_out, result)
helpers.save_to_json(arguments.syntetic_out, syntetic_traces)
print 'Bad: %d' % bad
c = collections.Counter(stretches)
trace_count = len(syntetic_traces)
logger.info('Stretch dist:')
for k in c:
logger.info('\t%d: %5.2f%%[%d]' %
(k, 100 * c[k] / float(trace_count), c[k]))
logger.info('Valid route count: %d' % trace_count)
logger.info('Route count parameter: %d' % arguments.trace_count)
logger.info('Generated valid pair count: %d' % len(pairs))
def main():
parser = argparse.ArgumentParser(description='Display statistical informations',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('network')
parser.add_argument('metadata')
parser.add_argument('out', type=str, help='folder path for results')
parser.add_argument('--stretch-stat',
dest='stretch_stat',
action='store_true')
parser.add_argument('--eye-stat',
dest='eye_stat',
action='store_true')
parser.add_argument('--eye-stat-basic',
dest='eye_stat_basic',
action='store_true')
parser.add_argument('--ba-stat',
dest='ba_stat',
nargs='+')
parser.add_argument('--degree-dist',
dest='degree_dist',
action='store_true')
parser.add_argument('--simple-load',
dest='simple_load',
action='store_true')
parser.add_argument('--load2d')
parser.add_argument('--stats',
dest='stats',
action='store_true')
parser.add_argument('--upwalk')
parser.add_argument('--verbose', '-v', action='count', default=0)
arguments = parser.parse_args()
arguments.verbose = min(len(helpers.LEVELS), arguments.verbose)
logging.getLogger('compnet').setLevel(helpers.LEVELS[arguments.verbose])
g = helpers.load_network(arguments.network)
meta = helpers.load_from_json(arguments.metadata)
out_folder_path = arguments.out
if out_folder_path.endswith('/'):
out_folder_path = out_folder_path[:-1]
# print 'WIKI MODE!!!!!'
# meta = [x for x in meta if x[helpers.SH_LEN] == 4 and x[helpers.TRACE_LEN] < 10]
# print 'ONLY WITH RANDOM NONVF WALK'
# meta = [x for x in meta if helpers.RANDOM_NONVF_WALK_RUN_COUNT in x]
if arguments.stretch_stat:
logger.info('Generate stretch statistics')
stretch_stat(meta, out_folder_path)
if arguments.eye_stat:
logger.info('Generate Eye statistics')
eye_stat(meta, out_folder_path)
if arguments.eye_stat_basic:
logger.info('Generate Basic Eye statistics')
eye_stat_basic(meta, out_folder_path)
if arguments.ba_stat:
logger.info('Generate Barabasi-Albert statistics')
ba_stat(meta, arguments.ba_stat, out_folder_path)
if arguments.degree_dist:
logger.info('Generate degree distributions')
degree_distribution_stat(g, out_folder_path)
if arguments.load2d:
logger.info('Generate load stat')
tr = helpers.load_from_json(arguments.load2d)
load2d(g, tr, out_folder_path)
if arguments.simple_load:
logger.info('Generate simple load based on meta')
simple_load(g, meta, out_folder_path)
if arguments.stats:
logger.info('Stat gen')
stats(g, meta )
# stat_printer(statistic)
if arguments.upwalk:
logger.info('Upwalk')
upwalk(arguments.upwalk, out_folder_path)
