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ERAESMultiProcessingDD.py
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import multiprocessing
from src.Graphs.Objects.MultipleEdge import DynamicGraph
from src.StastModules.Snapshot import get_snapshot,get_snapshot_dd
from src.FileOperations.WriteOnFile import create_file, create_folder, write_on_file_contents
import math as mt
import logging
import pandas as pd
import networkx as nx
#from src.tail_estimation_degree_distribution.tail-estimation.py import *
from networkx.algorithms.graphical import is_graphical
from networkx.utils.random_sequence import powerlaw_sequence
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
def worker(data, return_dict):
def flood():
flood_dictionary = {}
if (G.get_converged()):
if (G.flooding.get_initiator() == -1):
G.set_flooding()
G.flooding.set_initiator()
G.flooding.update_flooding(G)
logging.info("Flooding Simulation : STARTED")
# print("----- Flooding Simulation: STARTED -----\n")
else:
if (G.flooding.get_started() == True):
G.flooding.update_flooding(G)
G.flooding.is_informed()
if (G.flooding.get_converged()):
logging.info("ALL NODES IN THE NETWORK ARE INFORMED")
# print("--- ALL NODES IN THE NETWORK ARE INFORMED ---\n\n")
flood_dictionary['informed_nodes'] = G.flooding.get_informed_nodes()
flood_dictionary['uninformed_nodes'] = G.flooding.get_uninformed_nodes()
flood_dictionary['t_flood'] = G.flooding.get_t_flood()
flood_dictionary['process_status'] = G.get_converged()
flood_dictionary['flood_status'] = G.flooding.get_converged()
flood_dictionary['initiator'] = G.flooding.get_initiator()
else:
flood_dictionary['informed_nodes'] = 0
flood_dictionary['uninformed_nodes'] = len(G.get_list_of_nodes())
flood_dictionary['t_flood'] = 0
flood_dictionary['process_status'] = G.get_converged()
flood_dictionary['flood_status'] = G.flooding.get_converged()
flood_dictionary['initiator'] = G.flooding.get_initiator()
return (flood_dictionary)
"""worker function"""
final_stats = []
d = data["d"]
c = data["c"]
edge_falling_rate = data["edge_falling_rate"]
dd = data["dd"]
# sim = data["sim"]
max_iter = data["max_iter"]
G = DynamicGraph(n, d, c,falling_probability= edge_falling_rate,degree_sequence=dd)
t = 0
achieved = False
repeat = True
sim = {
"simulation": data["sim"],
"pl_exponend":data["gamma"]
}
while (repeat):
G.add_phase_dd()
G.del_phase_dd()
if (edge_falling_rate != 0):
G.random_fall()
if (G.get_converged() == False):
stats = get_snapshot_dd(G, edge_falling_rate, G.get_c(),dd, t)
if (t == max_iter):
G.set_converged(True)
G.flooding.set_converged(False)
if (G.get_converged()):
if (nx.is_connected(G.get_G()) == False and edge_falling_rate == 0):
flood_dictionary = {
'informed_nodes': 0,
'uninformed_nodes': len(G.get_list_of_nodes()),
't_flood': 0,
'process_status': G.get_converged(),
'flood_status': False,
'initiator': G.flooding.get_initiator()
}
repeat = False
final_stats.append({**sim, **stats, **flood_dictionary})
logging.info("The graph is not connected, flooding will always fail")
logging.info("Exiting")
if (repeat):
if (G.flooding.get_converged() == False):
flood_dictionary = flood()
final_stats.append({**sim, **stats, **flood_dictionary})
else:
logging.info("Flooding protocol simulation %r: CONVERGED" % data["sim"])
repeat = False
t += 1
# print(G.flooding.get_list_of_informed_ndoes())
# print(str(sim) + " represent!")
return_dict[sim['simulation']] = final_stats
if __name__ == "__main__":
d_list = [3]
c_list = [1.5,3,4]
n_list = [512, 1024, 2048, 4096, 8192, 16384, 32768]
exponent = [2,2.3,2.5,2.7,3]
probs_list = [0.0,0.1,0.5,0.7,0.9]
outPath = "./tmp/"
for d in d_list:
for c in c_list:
for n in n_list:
for probs in probs_list:
for ex in exponent:
iterate = True
dd = []
while iterate: # Continue generating sequences until one of them is graphical
dd = sorted([int(round(d)) for d in powerlaw_sequence(n, ex)], reverse=True) # Round to nearest integer to obtain DISCRETE degree sequence
if is_graphical(dd):
iterate = False
data = {
"d": d,
"c": c,
"dd":dd,
"edge_falling_rate": probs,
"max_iter": 100,
"gamma":ex
}
name = "ERAESDD_c_" + str(c) +"_n_"+str(n)+ "_p_" + str(probs)+"_g_"+str(ex)
outpath = create_folder(outPath, name)
logging.info("EXECUTING: %r " % name)
manager = multiprocessing.Manager()
return_dict = manager.dict()
jobs = []
for i in range(10):
data["sim"] = i
p = multiprocessing.Process(target=worker, args=(data, return_dict))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
reduced = []
for key in return_dict:
reduced.extend(return_dict[key])
df = pd.DataFrame(reduced)
df.to_csv(outpath + "results.csv")