<reponame>cjds/PyBioSim<gh_stars>0
'''
(c) 2015 Georgia Tech Research Corporation
This source code is released under the New BSD license.  Please see the LICENSE.txt file included with this software for more information

authors: <NAME> (<EMAIL>), <NAME> (<EMAIL>)

author: <NAME> (<EMAIL>)

Genetic Algorithm 

This is how the genetic algorithm will be run. The two teams will be genetic algorithms facing off against each other

At each level we may start with a population of 30 algorithms.
Each algorithm will play 3 games against the next 3 opponents in the population
The top 10 algorithms are matched with each other to create the next generation of algorithms
The fitness of the algorithms will be tested by 3 factors 
    1. The number of goals scored
    2. -0.5 the number of goals scored against your
    3. -0.01 times the algorithm makes a random choice (this is to discourage random choices in the system)

The box of 16 locations will start from the left corner closest to the goal for each individual team so that the algorithm is 
side agnostic

The statistics tracker will track:
'''


import numpy as np
import matplotlib.pyplot as plt
from numpy import *
from World import *
from Agent import Agent, RestrictedAgent
from Obstacle import *
from pylab import *
from Ball import Ball
from LinearAlegebraUtils import distBetween
from PreyBrain import PreyBrain
from GeneticAlgo import GeneticAlgo
from SoccerBrain import SoccerBrain
from Team import Team
from SimTime import SimTime
from StatsTracker import StatsTracker
from DatabaseAccess import DatabaseAccess, Gene, Individual

import Constants
#Called once for initialization
'''
Usage guidelines:
1. Define globals required for the simulation in the __init__ constructor, here we define a bunch of waypoints for the ball
2. Initialize the globals in the setup() method. 
'''

class Simulator(object):
    def __init__(self, world, simTime, fps, imageDirName,individual1,individual2):
        self.world = world
        self.simTime = simTime
        self.fps = fps
        self.imageDirName = imageDirName
        self.currWP = 0
        self.scoreTeam1=0
        self.scoreTeam2=0
        self.finalScore=Constants.MAX_SCORE
        self.teamSize=4
        self.ballInitialPosition=array([0,0,-150])
        self.teamPosition= [array([-50,50,-150]),array([-50,-50,-150]),array([-100,100,-150]),array([-100,-100,-150]),array([50,50,-150]),array([50,-50,-150]),array([100,100,-150]),array([100,-100,-150])]
        self.individual1=individual1
        self.individual2=individual2
        #self.ballWPs = [array([50.0, -100.0, 0.0]), array([0.0, 100.0, -70.0]), array([50.0, 20.0, 100.0]),array([-30.0, 50.0, -100.0]), array([80.0, -50.0, 50.0]), array([80.0, -50.0, -50.0]), array([-65.0, 20.0, 50.0]), array([-50.0, 20.0, -60.0])]

    def setup(self):    
        #setup directory to save the images
        try:
            os.mkdir(self.imageDirName)
        except:
            print self.imageDirName + " subdirectory already exists. OK."

  
         #define teams which the agents can be a part of
        predator = Team("Predator", '#ff99ff',GoalObstacle([-150,-50,-150],50,100))
        prey = Team("Prey", '#ffcc99',GoalObstacle([150,-50,-150],50,100))
        #Defining a couple of agents 

        self.world.teams=[predator,prey]

        #initialize Team 1
        #TODO change this back
        for i in range(0,self.teamSize):
            brain = GeneticAlgo(self.individual1)
            agent = Agent(predator,  self.teamPosition[i], array([0,0,0]),brain, 0.5, 5, 5,i)
            self.world.addAgent(agent)

        #initialize Team 2
        for i in range(0, self.teamSize):
            brain = GeneticAlgo(self.individual2)
            agent = Agent(prey, self.teamPosition[i+self.teamSize],array([0,0,0]), brain, 1, 5, 5,i)
            self.world.addAgent(agent)

        ball=Ball (self.ballInitialPosition)
        ball.isDynamic = True
        self.world.addBall(ball)
  
        #define a bunch of obstacles
        #ob1Pos = array([-50,-50,-50])
        #ob1 = Obstacle(ob1Pos, 30)
         
        #ob2Pos = array([80,-50,-50])
        #ob2 = Obstacle(ob2Pos, 20)

        # originRef = Obstacle(array([0.1, 0.1, 0.1]), 10)
         
        #add obstacles to the world
        # self.world.addObstacle(ob1)
        # self.world.addObstacle(originRef)
        
        
#called at a fixed 30fps always
    def fixedLoop(self):
        for agent in self.world.agents:
            agent.moveAgent(self.world)

        for ball in self.world.balls:  
            #BALL IS IN GOAL       
            if ball.position[0] <= -world.width +0.5 and ball.position[1]>-50 and ball.position[1]<50:
                #team 1 scored
                self.scoreTeam1=self.scoreTeam1+1
                self.individual1.goalScored()
                self.individual2.goalScoredAgainst()
                #reset everything
                ball.resetBall(self.ballInitialPosition)
                for i in range(0, self.teamSize*2):
                    self.world.agents[i].position=self.teamPosition[i]
            if ball.position[0] >= world.width -0.5 and ball.position[1]>-50 and ball.position[1]<50:
                self.scoreTeam2=self.scoreTeam2+1
                ball.resetBall(self.ballInitialPosition)
                for i in range(0, self.teamSize*2):
                    self.world.agents[i].position=self.teamPosition[i]
            ball.updatePhysics(self.world)                

    
#Called at specifed fps
    def loop(self, ax):       
        self.world.draw(ax)
       
                
    def run(self):
        #Run setup once
        self.setup()
        
        #Setup loop
        timeStep = 1/double(30)
        frameProb = double(self.fps) / 30
        currTime = double(0)
        SimTime.fixedDeltaTime = timeStep
        SimTime.deltaTime = double(1/ self.fps)
        drawIndex = 0
        physicsIndex = 0
        while(currTime < self.simTime and self.scoreTeam1<self.finalScore and self.scoreTeam1<self.finalScore ):
            self.fixedLoop()
            SimTime.time = currTime
            currProb = double(drawIndex)/double(physicsIndex+1)
            if currProb < frameProb:
                if Constants.DISPLAY_IMAGES:
                    self.drawFrame(drawIndex)  
                drawIndex+=1
            physicsIndex+=1
            currTime+=double(timeStep)

     
        #print "Physics ran for "+str(physicsIndex)+" steps"
        print "Score was "+str(self.scoreTeam1)+" - "+str(self.scoreTeam2)+". Game ran for "+str(physicsIndex)
        if Constants.DISPLAY_IMAGES:
            print "Drawing ran for "+str(drawIndex)+" steps"
       # print "Agents were stunned for"+str(StatsTracker.stunTimeDict)
            
    def drawFrame(self, loopIndex):
        fig = plt.figure(figsize=(16,12))
        ax = fig.add_subplot(111, projection='3d') 
        ax.view_init(elev = 30)
        ax.set_xlabel(self.world.teams[0].name +'  '+str(self.scoreTeam1) + ' - ' + str(self.scoreTeam2)+'  '+self.world.teams[1].name )
        ax.set_ylabel("Y")
        ax.set_zlabel("Z")    
        fname = self.imageDirName + '/' + str(int(100000000+loopIndex)) + '.png' # name the file 
        self.loop(ax)
        plt.gca().set_ylim(ax.get_ylim()[::-1])
        savefig(fname, format='png', bbox_inches='tight')
        print 'Written Frame No.'+ str(loopIndex)+' to '+ fname
        plt.close()

#Simulation runs here
#set the size of the world

#specify which world to simulate, total simulation time, and frammerate for video
database=DatabaseAccess()
individuals=[]

for level in range(Constants.START_LEVEL,Constants.FINAL_LEVEL):
    if level==Constants.START_LEVEL:
        if Constants.START_LEVEL==0:
            individuals=database.createRandomPopulation(0,Constants.POPULATION_SIZE)
        else:
            individuals=database.loadGeneticAlgorithms(Constants.START_LEVEL)
    else: 
        individuals=database.createNewPopulation(individuals,level)
    size=len(individuals)
    print "Generation "+str(level)
    for i in range(0,size):
        goalScored=0
        goalsAgainst=0
        world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # if level==Constants.START_LEVEL:
        #     for gene in individuals[i].genes:
        #         print str(gene.team_areas)

        individuals[i].fitness=0
        individuals[i].randomTime=0
        sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+1)%size])
        sim.run()
        goalsScored=sim.scoreTeam1
        goalsAgainst=sim.scoreTeam2
        print str(individuals[i].fitness)+ " 1 "+str(individuals[i].id)
        # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+2)%size])
        # sim.run()
        # goalsScored+=sim.scoreTeam1
        # goalsAgainst+=sim.scoreTeam2
        # print str(individuals[i].fitness)+ " 2 "
        # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+3)%size])
        # sim.run()
        # goalsScored+=sim.scoreTeam1
        # goalsAgainst+=sim.scoreTeam2
        # print str(individuals[i].fitness)+ " 3 "
        database.saveStatistics( individuals[i].id, individuals[i].randomTime, individuals[i].fitness,goalsScored,goalsAgainst)
'''
world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
individual1=Individual()    
individual2=Individual()
sim = Simulator(world, 60, 1, "images1",individual1,individual2)
#run the simulation
sim.run()
'''
'''
To create a video using the image sequence, execute the following command in command line.
>ffmpeg -framerate 30 -i "1%08d.png" -r 30 outPut.mp4
                    ^                    ^
                Framerate mtached with simulator
Make sure to set your current working directory to /images and have ffmpeg in your path.
'''

    
'''
(c) 2015 Georgia Tech Research Corporation
This source code is released under the New BSD license.  Please see the LICENSE.txt file included with this software for more information

authors: <NAME> (<EMAIL>), <NAME> (<EMAIL>)

author: <NAME> (<EMAIL>)

Genetic Algorithm 

This is how the genetic algorithm will be run. The two teams will be genetic algorithms facing off against each other

At each level we may start with a population of 30 algorithms.
Each algorithm will play 3 games against the next 3 opponents in the population
The top 10 algorithms are matched with each other to create the next generation of algorithms
The fitness of the algorithms will be tested by 3 factors 
    1. The number of goals scored
    2. -0.5 the number of goals scored against your
    3. -0.01 times the algorithm makes a random choice (this is to discourage random choices in the system)

The box of 16 locations will start from the left corner closest to the goal for each individual team so that the algorithm is 
side agnostic

The statistics tracker will track:
'''


import numpy as np
import matplotlib.pyplot as plt
from numpy import *
from World import *
from Agent import Agent, RestrictedAgent
from Obstacle import *
from pylab import *
from Ball import Ball
from LinearAlegebraUtils import distBetween
from PreyBrain import PreyBrain
from GeneticAlgo import GeneticAlgo
from SoccerBrain import SoccerBrain
from Team import Team
from SimTime import SimTime
from StatsTracker import StatsTracker
from DatabaseAccess import DatabaseAccess, Gene, Individual

import Constants
#Called once for initialization
'''
Usage guidelines:
1. Define globals required for the simulation in the __init__ constructor, here we define a bunch of waypoints for the ball
2. Initialize the globals in the setup() method. 
'''

class Simulator(object):
    def __init__(self, world, simTime, fps, imageDirName,individual1,individual2):
        self.world = world
        self.simTime = simTime
        self.fps = fps
        self.imageDirName = imageDirName
        self.currWP = 0
        self.scoreTeam1=0
        self.scoreTeam2=0
        self.finalScore=Constants.MAX_SCORE
        self.teamSize=4
        self.ballInitialPosition=array([0,0,-150])
        self.teamPosition= [array([-50,50,-150]),array([-50,-50,-150]),array([-100,100,-150]),array([-100,-100,-150]),array([50,50,-150]),array([50,-50,-150]),array([100,100,-150]),array([100,-100,-150])]
        self.individual1=individual1
        self.individual2=individual2
        #self.ballWPs = [array([50.0, -100.0, 0.0]), array([0.0, 100.0, -70.0]), array([50.0, 20.0, 100.0]),array([-30.0, 50.0, -100.0]), array([80.0, -50.0, 50.0]), array([80.0, -50.0, -50.0]), array([-65.0, 20.0, 50.0]), array([-50.0, 20.0, -60.0])]

    def setup(self):    
        #setup directory to save the images
        try:
            os.mkdir(self.imageDirName)
        except:
            print self.imageDirName + " subdirectory already exists. OK."

  
         #define teams which the agents can be a part of
        predator = Team("Predator", '#ff99ff',GoalObstacle([-150,-50,-150],50,100))
        prey = Team("Prey", '#ffcc99',GoalObstacle([150,-50,-150],50,100))
        #Defining a couple of agents 

        self.world.teams=[predator,prey]

        #initialize Team 1
        #TODO change this back
        for i in range(0,self.teamSize):
            brain = GeneticAlgo(self.individual1)
            agent = Agent(predator,  self.teamPosition[i], array([0,0,0]),brain, 0.5, 5, 5,i)
            self.world.addAgent(agent)

        #initialize Team 2
        for i in range(0, self.teamSize):
            brain = GeneticAlgo(self.individual2)
            agent = Agent(prey, self.teamPosition[i+self.teamSize],array([0,0,0]), brain, 1, 5, 5,i)
            self.world.addAgent(agent)

        ball=Ball (self.ballInitialPosition)
        ball.isDynamic = True
        self.world.addBall(ball)
  
        #define a bunch of obstacles
        #ob1Pos = array([-50,-50,-50])
        #ob1 = Obstacle(ob1Pos, 30)
         
        #ob2Pos = array([80,-50,-50])
        #ob2 = Obstacle(ob2Pos, 20)

        # originRef = Obstacle(array([0.1, 0.1, 0.1]), 10)
         
        #add obstacles to the world
        # self.world.addObstacle(ob1)
        # self.world.addObstacle(originRef)
        
        
#called at a fixed 30fps always
    def fixedLoop(self):
        for agent in self.world.agents:
            agent.moveAgent(self.world)

        for ball in self.world.balls:  
            #BALL IS IN GOAL       
            if ball.position[0] <= -world.width +0.5 and ball.position[1]>-50 and ball.position[1]<50:
                #team 1 scored
                self.scoreTeam1=self.scoreTeam1+1
                self.individual1.goalScored()
                self.individual2.goalScoredAgainst()
                #reset everything
                ball.resetBall(self.ballInitialPosition)
                for i in range(0, self.teamSize*2):
                    self.world.agents[i].position=self.teamPosition[i]
            if ball.position[0] >= world.width -0.5 and ball.position[1]>-50 and ball.position[1]<50:
                self.scoreTeam2=self.scoreTeam2+1
                ball.resetBall(self.ballInitialPosition)
                for i in range(0, self.teamSize*2):
                    self.world.agents[i].position=self.teamPosition[i]
            ball.updatePhysics(self.world)                

    
#Called at specifed fps
    def loop(self, ax):       
        self.world.draw(ax)
       
                
    def run(self):
        #Run setup once
        self.setup()
        
        #Setup loop
        timeStep = 1/double(30)
        frameProb = double(self.fps) / 30
        currTime = double(0)
        SimTime.fixedDeltaTime = timeStep
        SimTime.deltaTime = double(1/ self.fps)
        drawIndex = 0
        physicsIndex = 0
        while(currTime < self.simTime and self.scoreTeam1<self.finalScore and self.scoreTeam1<self.finalScore ):
            self.fixedLoop()
            SimTime.time = currTime
            currProb = double(drawIndex)/double(physicsIndex+1)
            if currProb < frameProb:
                if Constants.DISPLAY_IMAGES:
                    self.drawFrame(drawIndex)  
                drawIndex+=1
            physicsIndex+=1
            currTime+=double(timeStep)

     
        #print "Physics ran for "+str(physicsIndex)+" steps"
        print "Score was "+str(self.scoreTeam1)+" - "+str(self.scoreTeam2)+". Game ran for "+str(physicsIndex)
        if Constants.DISPLAY_IMAGES:
            print "Drawing ran for "+str(drawIndex)+" steps"
       # print "Agents were stunned for"+str(StatsTracker.stunTimeDict)
            
    def drawFrame(self, loopIndex):
        fig = plt.figure(figsize=(16,12))
        ax = fig.add_subplot(111, projection='3d') 
        ax.view_init(elev = 30)
        ax.set_xlabel(self.world.teams[0].name +'  '+str(self.scoreTeam1) + ' - ' + str(self.scoreTeam2)+'  '+self.world.teams[1].name )
        ax.set_ylabel("Y")
        ax.set_zlabel("Z")    
        fname = self.imageDirName + '/' + str(int(100000000+loopIndex)) + '.png' # name the file 
        self.loop(ax)
        plt.gca().set_ylim(ax.get_ylim()[::-1])
        savefig(fname, format='png', bbox_inches='tight')
        print 'Written Frame No.'+ str(loopIndex)+' to '+ fname
        plt.close()

#Simulation runs here
#set the size of the world

#specify which world to simulate, total simulation time, and frammerate for video
database=DatabaseAccess()
individuals=[]

for level in range(Constants.START_LEVEL,Constants.FINAL_LEVEL):
    if level==Constants.START_LEVEL:
        if Constants.START_LEVEL==0:
            individuals=database.createRandomPopulation(0,Constants.POPULATION_SIZE)
        else:
            individuals=database.loadGeneticAlgorithms(Constants.START_LEVEL)
    else: 
        individuals=database.createNewPopulation(individuals,level)
    size=len(individuals)
    print "Generation "+str(level)
    for i in range(0,size):
        goalScored=0
        goalsAgainst=0
        world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # if level==Constants.START_LEVEL:
        #     for gene in individuals[i].genes:
        #         print str(gene.team_areas)

        individuals[i].fitness=0
        individuals[i].randomTime=0
        sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+1)%size])
        sim.run()
        goalsScored=sim.scoreTeam1
        goalsAgainst=sim.scoreTeam2
        print str(individuals[i].fitness)+ " 1 "+str(individuals[i].id)
        # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+2)%size])
        # sim.run()
        # goalsScored+=sim.scoreTeam1
        # goalsAgainst+=sim.scoreTeam2
        # print str(individuals[i].fitness)+ " 2 "
        # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
        # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+3)%size])
        # sim.run()
        # goalsScored+=sim.scoreTeam1
        # goalsAgainst+=sim.scoreTeam2
        # print str(individuals[i].fitness)+ " 3 "
        database.saveStatistics( individuals[i].id, individuals[i].randomTime, individuals[i].fitness,goalsScored,goalsAgainst)
'''
world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE)
individual1=Individual()    
individual2=Individual()
sim = Simulator(world, 60, 1, "images1",individual1,individual2)
#run the simulation
sim.run()
'''
'''
To create a video using the image sequence, execute the following command in command line.
>ffmpeg -framerate 30 -i "1%08d.png" -r 30 outPut.mp4
                    ^                    ^
                Framerate mtached with simulator
Make sure to set your current working directory to /images and have ffmpeg in your path.
'''
(c) 2015 Georgia Tech Research Corporation This source code is released under the New BSD license.  Please see the LICENSE.txt file included with this software for more information  authors: <NAME> (<EMAIL>), <NAME> (<EMAIL>)  author: <NAME> (<EMAIL>)  Genetic Algorithm   This is how the genetic algorithm will be run. The two teams will be genetic algorithms facing off against each other  At each level we may start with a population of 30 algorithms. Each algorithm will play 3 games against the next 3 opponents in the population The top 10 algorithms are matched with each other to create the next generation of algorithms The fitness of the algorithms will be tested by 3 factors      1. The number of goals scored     2. -0.5 the number of goals scored against your     3. -0.01 times the algorithm makes a random choice (this is to discourage random choices in the system)  The box of 16 locations will start from the left corner closest to the goal for each individual team so that the algorithm is  side agnostic  The statistics tracker will track: #Called once for initialization Usage guidelines: 1. Define globals required for the simulation in the __init__ constructor, here we define a bunch of waypoints for the ball 2. Initialize the globals in the setup() method. #self.ballWPs = [array([50.0, -100.0, 0.0]), array([0.0, 100.0, -70.0]), array([50.0, 20.0, 100.0]),array([-30.0, 50.0, -100.0]), array([80.0, -50.0, 50.0]), array([80.0, -50.0, -50.0]), array([-65.0, 20.0, 50.0]), array([-50.0, 20.0, -60.0])] #setup directory to save the images #define teams which the agents can be a part of #Defining a couple of agents #initialize Team 1 #TODO change this back #initialize Team 2 #define a bunch of obstacles #ob1Pos = array([-50,-50,-50]) #ob1 = Obstacle(ob1Pos, 30) #ob2Pos = array([80,-50,-50]) #ob2 = Obstacle(ob2Pos, 20) # originRef = Obstacle(array([0.1, 0.1, 0.1]), 10) #add obstacles to the world # self.world.addObstacle(ob1) # self.world.addObstacle(originRef) #called at a fixed 30fps always #BALL IS IN GOAL #team 1 scored #reset everything #Called at specifed fps #Run setup once #Setup loop #print "Physics ran for "+str(physicsIndex)+" steps" # print "Agents were stunned for"+str(StatsTracker.stunTimeDict) # name the file #Simulation runs here #set the size of the world #specify which world to simulate, total simulation time, and frammerate for video # if level==Constants.START_LEVEL: #     for gene in individuals[i].genes: #         print str(gene.team_areas) # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE) # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+2)%size]) # sim.run() # goalsScored+=sim.scoreTeam1 # goalsAgainst+=sim.scoreTeam2 # print str(individuals[i].fitness)+ " 2 " # world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE) # sim = Simulator(world, Constants.WORLD_MAX_TIME, 1, "images1",individuals[i],individuals[(i+3)%size]) # sim.run() # goalsScored+=sim.scoreTeam1 # goalsAgainst+=sim.scoreTeam2 # print str(individuals[i].fitness)+ " 3 " world = World(Constants.WORLD_SIZE,Constants.WORLD_SIZE) individual1=Individual()     individual2=Individual() sim = Simulator(world, 60, 1, "images1",individual1,individual2) #run the simulation sim.run() To create a video using the image sequence, execute the following command in command line. >ffmpeg -framerate 30 -i "1%08d.png" -r 30 outPut.mp4                     ^                    ^                 Framerate mtached with simulator Make sure to set your current working directory to /images and have ffmpeg in your path.