How can I make my ant simulation faster?

I was not sure if I should post about 200 lines here. I want to make this ant simulation faster. The bottleneck is at Ant.checkdistancebetweenantsandfood().

It would be great if you have some hints for some cleaner or more efficient code. I hope this is the right place to show this code.

import random import time import datetime # TODO # food - 100 portions okay # bring food to the hill okay # bring food to the hill - direct way # use food over time # die if food is out # bear if enough food is there # trail okay # trail disolves over time # opponent - different properties # new food # inside ant hill ? # make calculations faster ############################# # PLOT - BEGIN ############################# import matplotlib.pyplot as plt import numpy as np fig, ax = plt.subplots() # field size size = 30 # black ants x1 = [0] y1 = [0] points, = ax.plot(x1, y1, marker='o', linestyle='None', alpha=0.3, markersize=5, c='black') # ant hill x2 = [0] y2 = [0] points2, = ax.plot(x2, y2, marker='o', linestyle='None', alpha=0.6, markersize=30, c='brown') # food foodamount = 220 foodposx = [] foodposy = [] for i in range(foodamount): foodposx.append(random.randint(0, 2*size)-size) foodposy.append(random.randint(0, 2*size)-size) points3, = ax.plot(foodposx, foodposy, marker='o', linestyle='None', alpha=0.6, markersize=5, c='green') foundenoughfood = 20 anthillfood = 0 # movement speed = .4 # trail trailx = [0] traily = [0] trail, = ax.plot(trailx, traily, marker='o', linestyle='None', alpha=0.6, markersize=5, c='orange') #trailduration = [] bearingspeed = 100 # higher value = slower bearing # red ants #... ax.set_xlim(-size, size) ax.set_ylim(-size, size) new_x = np.random.randint(10, size=1) new_y = np.random.randint(10, size=1) points.set_data(new_x, new_y) plt.pause(.0000000000001) ############################# # PLOT - END ############################# class Antqueen: counter = 2 def __init__(self, name): """Initializes the data.""" self.name = name print("(Initializing {0})".format(self.name)) def bear(self): # print Antqueen.counter, 'ants born' self.name = 'ant_' + str(Antqueen.counter) Antqueen.counter = Antqueen.counter + 1 ants[self.name] = None globals()[self.name] = Ant(self.name) class Ant: population = 0 def __init__(self, name): """Initializes the data.""" self.name = name self.food = 10 self.posx = 0.0 self.posy = 0.0 #print("(Initializing {0})".format(self.name)) Ant.population += 1 def die(self): """I am dying.""" print("{0} is dead!".format(self.name)) Ant.population -= 1 if Ant.population == 0: print("{0} was the last one.".format(self.name)) else: print("There are still {0:d} ants working.".format(Ant.population)) def sayHi(self): print("Hello, call me {0}.".format(self.name)) # move + trail def move(self): if globals()[name].food < foundenoughfood: self.posx = self.posx + speed*(random.random()-random.random()) self.posy = self.posy + speed*(random.random()-random.random()) # found enough food # go back to home else: # close to the ant hill # lay down some food to the ant hill if abs(self.posx) < .5: if abs(self.posy) < .5: global anthillfood anthillfood = anthillfood + globals()[name].food - 10 #print "anthillfood: ", anthillfood # lay down everything but 10 globals()[name].food = 10 # far away from the ant hill # set trail trailx.append(self.posx) traily.append(self.posy) # draw the trail ### trail.set_data(trailx, traily) # move towards the ant hill vecx = -self.posx vecy = -self.posy len_vec = np.sqrt(vecx**2+vecy**2) self.posx = self.posx + vecx*1.0/len_vec/3 + speed*(random.random()-random.random())/1.5 self.posy = self.posy + vecy*1.0/len_vec/3 + speed*(random.random()-random.random())/1.5 def eat(self, name, foodnumber, foodposx, foodposy): if foodtable[foodnumber][1] > 0: # food on ground decreases foodtable[foodnumber][1] = foodtable[foodnumber][1] - 1 # food in ant increases globals()[name].food = globals()[name].food + 1 #print name, globals()[name].food, "food" # food is empty if foodtable[foodnumber][1] == 0: #print "foodposx: ", foodposx del foodposx[foodnumber] del foodposy[foodnumber] points3.set_data(foodposx, foodposy) @classmethod def howMany(cls): """Prints the current population.""" print("We have {0:d} ants.".format(cls.population)) def checkdistancebetweenantsandfood(self): for name in ants.keys(): for i in range(len(foodposx)-1): # measure distance between ant and food if -1 < globals()[name].posx - foodposx[i] < 1: if -1 < globals()[name].posy - foodposy[i] < 1: globals()[name].eat(name, i, foodposx, foodposy) # slower #distance = np.sqrt((globals()[name].posx - foodposx[i])**2 + (globals()[name].posy - foodposy[i])**2) #if distance < 1: # globals()[name].eat(name, i, foodposx, foodposy) # generate ant queen antqueen = Antqueen("antqueen") # generate some ants ants = {'ant_1': None} for name in ants.keys(): globals()[name] = Ant(name) # amount of food foodtable = [] for i in range(foodamount): foodtable.append([i, 10]) ############################# # start simulation ############################# for i in range(100000): # move all ants for name in ants.keys(): globals()[name].move() # generate more ants if (i % bearingspeed == 0): antqueen.bear() # plot ants allposx = [] allposy = [] for name in ants.keys(): allposx.append(globals()[name].posx) allposy.append(globals()[name].posy) points.set_data(allposx, allposy) plt.draw() #plt.pause(0.000000000001) # draw is faster # ants find food for name in ants.keys(): globals()[name].checkdistancebetweenantsandfood()