multi-hop swarm redistribution

from __future__ import division
import random

# Set parameters for the simulation

totalNodes = 4 ** 6

connections = 4

totalHops = 5+1

frequency = {}

totalPulses = totalNodes

def initateMapping():
    for node in range(1, totalNodes):
        frequency[node] = {}
        for step in range(totalHops):
            frequency[node][step] = 0

initateMapping()

# Calculate geometric series for connections^totalHops
# to get total nodes in branching tree

treeSize = 0

def calculateTreeSize():
    for step in range(totalHops):
        global treeSize
        treeSize += connections ** step

calculateTreeSize()

# Populate branching trees without any loops (no duplicate random numbers)

def swarmRedistribution():
    branchingTree = random.sample(range(1, totalNodes), treeSize)
    distance = 0
    nextLevel = 0
    for node in range(treeSize):
        frequency[branchingTree[node]][distance] += 1
        if (node == nextLevel):
            distance += 1
            nextLevel += connections ** distance

# Run the simulation and see if the pulse strength decreases with
# same factor as frequency of getting pulse from certain distance
# increases

def runSimulation():
    for pulse in range(totalPulses):
        swarmRedistribution()
    getBalance()

def getBalance():
    balance = {}
    randomAgent = random.randint(1, totalNodes)
    for step in range(totalHops):
        balance[step] = frequency[randomAgent][step] / connections ** step
    print("The ratio of the frequency a person received pulses from a given distance, divided by how much the amount had decreased at that distance.")
    for step in range(len(balance)):
        print("Distance ", step, ". Ratio: ", balance[step])
    print("The frequency of a person selected on random, same person that was used to calculate the ratios.")
    print(frequency[randomAgent])

runSimulation()