main.py

import environment
import config
import behaviour
import gi
import stats

from config import g

count_iterations = 0

def wakeup_nodes(nodes):
    """
    wakes up the affected nodes for action. this will call 'behaviour'
    """
    for node in nodes:
        behaviour.node_method(node) # the action is defined there...

def iteration(): # what happens in each cycle. Main calls happen here.
    print "iteration!"
    global count_iterations
    print "count_iterations "+str(count_iterations)
    nodes = environment.step()
    wakeup_nodes(nodes) # calls their behaviour, which calls the policy (what to do), and finally applies actions
    print "killed edges: "+str(stats.collector.num_kills)
    stats.snapshot()
    print "total graph weight:"+str(stats.collector.total_weight)
    print "average path length, see note1:"+ str(stats.collector.path_length)
    print "mean edge importance:"+str(stats.collector.mean_edges_importance)
    print "std edge importance:"+str(stats.collector.std_edges_importance)
    count_iterations +=1
    stats.new_collector()

def termination_condition3():
    if gi.num_alive_edges() <= 0:
        print("all edges have been killed! THE END!")

        return True
    return False

def termination_condition():
    global count_iterations
    if count_iterations >= config.max_iterations:
        print("max number iterations reached.")

        return True
    return False

def initialize_run():
    """
    initialization procedures. For example: attaching methods to nodes.
    """
    global count_iterations
    count_iterations = 0
    stats.collector = None
    stats.new_collector()
    gi.save_weights()

def end_run():
    print "end run!"
    # FIXME: move following descriptions in a more appropriate place
    # note1: avg path length reports harmonic mean of all shortest path
    # shor.path are chosen favouring high weights
    # mathematically the distance minimized between 2nodes is the sum of the inverse of the weights the edge crossed
    # avg. path length reports now the sum of the real weights. the bigger, the better
    gi.restore_weights()

def simulation_run():
    print "simulation_run!"
    initialize_run()
    while not (termination_condition() or termination_condition3()):
        iteration()
    end_run()

def series(gephi_stuff): # the Highest function (hehe)
    print "series!"
    config.set_g(gephi_stuff['g'])
    config.set_gephi(gephi_stuff)
    gi.save_weights()
    for run_num in range(0, config.runs_per_series):
        stats.run_num = run_num
        simulation_run()
        stats.write_history()