From 1e249fd7b590956f745f34976d2861d5469d6cfd Mon Sep 17 00:00:00 2001
From: markr <marlene.kress@gmx.de>
Date: Wed, 13 Sep 2017 19:20:16 +0000
Subject: [PATCH] Upload New File
---
Final/python files/leadership_simulation.py | 328 ++++++++++++++++++++
1 file changed, 328 insertions(+)
create mode 100644 Final/python files/leadership_simulation.py
diff --git a/Final/python files/leadership_simulation.py b/Final/python files/leadership_simulation.py
new file mode 100644
index 0000000..458f157
--- /dev/null
+++ b/Final/python files/leadership_simulation.py
@@ -0,0 +1,328 @@
+from graphics import * # get here: http://mcsp.wartburg.edu/zelle/python/graphics.py
+import time
+import random
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# imports from our files
+from help_functions import *
+from classes import Agent
+from check_boundarys import *
+
+
+# ------------------------------------------------------------------------
+# global variables
+
+# whether average distance will be ploted at end of simulation
+plot = False
+
+# window dimensions
+winWidth = 500
+winHeight = 500
+
+window = GraphWin("Window", winWidth, winHeight)
+
+# max time for one simulation
+maxTime = 4000
+
+# whether the boundary is "free" or not
+# <->
+# can they swim through the side of the window y/n?
+free = True
+
+# max velocity of the agents
+maxV = 8
+# constant speed of agents
+speed = 4
+
+# number of agents(including leadergroups)
+agentNum = 50
+
+# angle of blindness
+blind = 80
+
+# maximum turning angle
+maxTurn = 50
+radTurn = math.radians(maxTurn)
+negRadTurn = math.radians(360-maxTurn)
+
+# radii of zones
+zor_r = 10
+zoo_r = 80
+zoa_r = 110
+
+# leadergroup 1, red
+
+lgOneNum = 5 # number of agents in group
+prefDirOne = [-1 ,-1] # preferred direction of group
+weightOne = 0.2 # weight of group, determines how much they move in prefDir
+
+# leadergroup 2, blue
+lgTwoNum = 2 # number of agents in group
+prefDirTwo = [1 ,1] # preferred direction of group
+weightTwo = 0.2 # weight of group, determines how much they move in prefDir
+
+
+
+# -------------------------------------------------------------------------------
+# leadership simulation
+
+def wraparound_distance(x1, y1, x2, y2, winWidth, winHeight):
+ xDis = x1-x2
+ yDis = y1-y2
+
+ x=x1
+ y=y1
+ flag = False
+
+ if(xDis > winWidth/2):
+ x -= winWidth
+ flag = True
+ elif (xDis < -winWidth/2):
+ x += winWidth
+ flag = True
+ if(yDis > winHeight/2):
+ y -= winHeight
+ flag = True
+ elif (yDis < -winHeight/2):
+ y += winHeight
+ flag = True
+
+ return [distance(x, y, x2, y2), flag, x-x2, y-y2]
+
+def neigbour_in_zones_free(a, aas, zor_r, zoo_r, zoa_r, blind, winWidth, winHeight):
+ zor = []
+ zoo = []
+ zoa = []
+
+ for agent in aas:
+ wpd = wraparound_distance(a.point.getX(), a.point.getY(), agent.point.getX(), agent.point.getY(), winWidth, winHeight)
+ dis = wpd[0]
+ disVecX = wpd[2]
+ disVecY = wpd[3]
+ alpha = calc_angle(a.x_velocity,a.y_velocity, disVecX, disVecY)
+
+ if (a == agent):
+ True
+ elif alpha < 180 - blind and alpha > 180 + blind:
+ True
+ else:
+ dis = absvec(agent.point.getX() - a.point.getX() , agent.point.getY() - a.point.getY() )
+ if dis <= zor_r:
+ zor.append([agent, wpd[1]])
+ elif dis <= zoo_r:
+ zoo.append([agent, wpd[1]])
+ elif dis <= zoa_r:
+ zoa.append([agent, wpd[1]])
+
+ return [zor, zoo, zoa]
+
+def updateV_leadership_couzin(a, matrix):
+ dx=0
+ dy=0
+
+ #zor
+ if matrix[0] != []:
+ for agent in matrix[0]:
+
+ disX = agent[0].point.getX() - a.point.getX()
+ disY = agent[0].point.getY() - a.point.getY()
+
+ rX = disX/absvec(disX, disY)
+ rY = disY/absvec(disX, disY)
+
+ #flag checkBoundary
+ if not agent[1]:
+ dx += rX / absvec(rX, rY)
+ dy += rY / absvec(rX, rY)
+ else:
+ dx -= rX / absvec(rX, rY)
+ dy -= rY / absvec(rX, rY)
+ dx = -dx
+ dy = -dy
+ # randomness factor / error
+ #dx += random.uniform(-1, 1)
+ #dy += random.uniform(-1, 1)
+ #normalise
+ dx = dx / absvec(dx, dy)
+ dy = dy / absvec(dx, dy)
+
+
+ # zoo ; zoa leer
+ elif matrix[1] != [] and matrix[2] == []:
+ for b in matrix[1]:
+ agent = b[0]
+ if not b[1]:
+ dx += agent.x_velocity / absvec(agent.x_velocity, agent.y_velocity)
+ dy += agent.y_velocity / absvec(agent.x_velocity, agent.y_velocity)
+ else:
+ dx -= agent.x_velocity / absvec(agent.x_velocity, agent.y_velocity)
+ dy -= agent.y_velocity / absvec(agent.x_velocity, agent.y_velocity)
+
+ dx += a.x_velocity / absvec(a.x_velocity, a.y_velocity)
+ dy += a.y_velocity / absvec(a.x_velocity, a.y_velocity)
+
+ # zoo leer ; zoa
+ elif matrix[1] == [] and matrix[2] != []:
+ for b in matrix[2]:
+ agent = b[0]
+ disX = agent.point.getX() - a.point.getX()
+ disY = agent.point.getY() - a.point.getY()
+ if not b[1]:
+ dx += ( disX / absvec(disX, disY) ) / absvec( disX / absvec(disX, disY) , disY/ absvec(disX,disY) )
+ dy += ( disY / absvec(disX, disY) ) / absvec( disX / absvec(disX, disY) , disY/ absvec(disX,disY) )
+ else:
+ dx -= ( disX / absvec(disX, disY) ) / absvec( disX / absvec(disX, disY) , disY/ absvec(disX,disY) )
+ dy -= ( disY / absvec(disX, disY) ) / absvec( disX / absvec(disX, disY) , disY/ absvec(disX,disY) )
+
+ #both zones filled
+ elif matrix[1] != [] and matrix[2] != []:
+
+ for b in matrix[1]:
+ agent = b[0]
+
+ dx += agent.x_velocity / absvec(agent.x_velocity, agent.y_velocity)
+ dy += agent.y_velocity / absvec(agent.x_velocity, agent.y_velocity)
+
+ for b in matrix[2]:
+ agent = b[0]
+ disX = agent.point.getX() - a.point.getX()
+ disY = agent.point.getY() - a.point.getY()
+
+ rX = disX/absvec(disX, disY)
+ rY = disY/absvec(disX, disY)
+
+ if not b[1]:
+ dx += rX / absvec(rX, rY)
+ dy += rY / absvec(rX, rY)
+ else:
+ dx -= rX / absvec(rX, rY)
+ dy -= rY / absvec(rX, rY)
+
+ dx += a.x_velocity / absvec(a.x_velocity, a.y_velocity)
+ dy += a.y_velocity / absvec(a.x_velocity, a.y_velocity)
+
+ # all zones empty
+ else:
+ dx = a.x_velocity
+ dy = a.y_velocity
+
+ # randomness factor / error
+ #dx += random.uniform(-1, 1)
+ #dy += random.uniform(-1, 1)
+ #normalise
+ dx = dx / absvec(dx, dy)
+ dy = dy / absvec(dx, dy)
+
+ #prefered direction
+ dx = (dx + (a.weight * a.prefdir[0]) ) / absvec((dx + (a.weight * a.prefdir[0])) , (dy + (a.weight * a.prefdir[1])))
+ dy = (dy + (a.weight * a.prefdir[1]) ) / absvec((dx + (a.weight * a.prefdir[0])) , (dy + (a.weight * a.prefdir[1])))
+
+ return [dx, dy]
+
+# update function
+def update_leadership(agent, agents):
+ # Velocity update
+ for agent in agents:
+ neigh_matrix = neigbour_in_zones_free(agent, agents, zor_r, zoo_r, zoa_r,blind,winWidth, winHeight)
+ agent.set_temp_velocity(updateV_leadership_couzin(agent, neigh_matrix))
+
+ # move, draw
+ for agent in agents:
+ # check if rotation is in range of maxTurn
+ agent = move_agent_couzin(agent, maxTurn, radTurn, negRadTurn)
+
+ # if agent is in a leadergroup, find angle between
+ # preferred direction and actual direction,
+ # if angle is smaller than 20% increase weight, else decrease
+ if (agent.prefdir != [0,0]):
+ angle_pref = math.atan2(agent.prefdir[0], agent.prefdir[1])
+ angle_new = math.atan2(agent.x_velocity, agent.y_velocity)
+ beta = math.degrees(angle_new - angle_pref)
+
+ if abs(beta) > 180:
+ if beta < 0:
+ beta += 360
+ else:
+ beta -= 360
+
+ if abs(beta) < 20 :
+ agent.set_weight(min(1, agent.weight+0.001))
+ else:
+ agent.set_weight(max(0, agent.weight-0.0001))
+
+
+ # normalise direction vector to 1, and multiply by constant speed
+ x = agent.x_velocity/absvec(agent.x_velocity, agent.y_velocity) * agent.speed
+ agent.y_velocity = agent.y_velocity/absvec(agent.x_velocity, agent.y_velocity) * agent.speed
+ agent.x_velocity = x
+
+ if free:
+ agent = checkBoundary_free(agent, winWidth, winHeight)
+ else:
+ agent = checkBoundary(agent, winWidth, winHeight)
+
+ # draw arrow
+ agent.draw_Line()
+
+ return agents
+
+
+# -----------------------------------------------------------------------------------
+# main
+def main():
+
+ agents = []
+
+ if plot:
+ # data for plotting
+ distancedata = np.array([])
+ distancestd = np.array([])
+ numpycount = np.array([])
+
+ # generate agents
+ for i in range(agentNum):
+
+ agent = Agent(Point(random.uniform(0,winWidth), random.uniform(0,winHeight)),window, maxV)
+ agent.speed = speed
+ # give them a random starting direction
+ agent.set_xvelocity(random.uniform(-2,2))
+ agent.set_yvelocity(random.uniform(-2,2))
+ agent.draw_Line()
+
+ agents.append(agent)
+
+ # set preferred direction/weight/colour of leadergroup one
+ for i in range(lgOneNum):
+ agents[i].set_prefdir(prefDirOne)
+ agents[i].set_weight(weightOne)
+ agents[i].set_colour("red")
+
+ # set preferred direction/weight/colour of leadergroup one
+ for i in range(lgOneNum, lgOneNum + lgTwoNum):
+ agents[i].set_prefdir(prefDirTwo)
+ agents[i].set_weight(weightTwo)
+ agents[i].set_colour("blue")
+
+ # main loop
+ for i in range(maxTime):
+ if plot:
+ rawdata = totalavgdistance(agents,distancedata)
+ #print ("rawdata: "+ str(rawdata))
+ distancedata = np.append(distancedata,np.mean(rawdata))
+ distancestd = np.append(distancestd,np.std(rawdata))
+ numpycount = np.append(numpycount,i)
+
+
+ agents = update_leadership(agent, agents)
+ time.sleep(0.01)
+
+ if plot:
+ plt.errorbar(numpycount,distancedata,yerr=distancestd)
+
+
+ window.getMouse()
+ window.close()
+
+main()
--
GitLab