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functions_motion.py
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functions_motion.py
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import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from random import uniform
import matplotlib.animation as animation
from copy import deepcopy
class Agent:
def __init__(self,ini_pos, ini_velocity,ini_direction,ini_angle,cell,agentId):
self.position = ini_pos
self.velocity = ini_velocity
self.direction = ini_direction
self.angle = ini_angle
self.ID = agentId
self.cell = cell
def findDirection(angle):
return np.array([np.cos(angle), np.sin(angle)])
def InitialiseParticles(N,Lx,Ly, velocity = 5):
agents = []
for i in range(N):
pos = np.asarray([uniform(0,Lx),uniform(0, Ly)])
angle = uniform(0,2*np.pi)
direction = findDirection( angle)
agents.append(Agent(pos,velocity,direction,angle,0,i+1))
return agents
def getValue(ix,iy,leny):
return int(ix*(leny-1) + iy)
def makeCellsGrid(agents,radius, occupants,Lx,Ly):
x = np.arange(0, Lx+radius, radius)
y = np.arange(0, Ly+radius, radius)
lenx = len(x)
leny = len(y)
make_map = np.arange((lenx-1)*(leny-1))
make_map = make_map.reshape((lenx-1),(leny-1))
grid = np.zeros(((lenx-1),(leny-1),occupants))
grid_next = np.zeros((lenx-1)*(leny-1))
for i,a in enumerate(agents):
index_x = a.position[0]//radius
index_y = a.position[1]//radius
val = getValue(index_x,index_y,leny)
a.cell = val
grid[int(index_x)][int(index_y)][int(grid_next[val])] = a.ID
grid_next[int(val)]+=1
return grid.astype(int), make_map.astype(int)
def relativeAngle(agent1, agent2):
vec1 = agent1.direction
vec2 = agent2.position - agent1.position
if(np.linalg.norm(vec2)!=0):
vec2 = vec2/np.linalg.norm(vec2)
else:
vec2 = 0
return np.arccos(np.clip(np.dot(vec1, vec2), -1.0, 1.0))
def ifNeighbour(agent1, agent2,Rr, Ral,Rattr, fieldOfView):
# if(np.sqrt(np.sum(np.square(agent1.position - agent2.position)))<=Rr and relativeAngle(agent1,agent2)<=fieldOfView):
# return 1
if(np.sqrt(np.sum(np.square(agent1.position - agent2.position)))<=Ral and relativeAngle(agent1,agent2)*(180/np.pi)<=fieldOfView):
return 2
else:
return 0
# if(relativeAngle(agent1,agent2)<=fieldOfView):
# return 2
# elif(Ral<np.sqrt(np.sum(np.square(agent1.position - agent2.position)))<=Rattr and relativeAngle(agent1,agent2)<=fieldOfView):
# return 3
def findNearest(agents, grid, map, max_occupants,Rr, Ral, Rattr,fieldOfView,Lx,Ly):
# neigh_listR = np.zeros((len(agents)+1,max_occupants))
neigh_listAl = np.zeros((len(agents)+1,max_occupants))
# neigh_listAttr = np.zeros((len(agents)+1,max_occupants))
last_x = grid.shape[0]
last_y = grid.shape[1]
for a in agents:
# print(a.position)
# flag1 = 0
flag2 = 0
# flag3 = 0
cell = a.cell
ix = np.where(map==cell)[0][0]
iy = np.where(map==cell)[1][0]
for i in range(max_occupants):
if(grid[ix][iy][i]!=0 and grid[ix][iy][i]!=a.ID):
# if(ifNeighbour(a,agents[grid[ix][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix][iy][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix][iy][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix][iy][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix][iy][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(ix-1>=0):
if(grid[ix-1][iy][i]!=0):
# if(ifNeighbour(a,agents[grid[ix-1][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix-1][iy][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix-1][iy][i]-1],Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix-1][iy][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix-1][iy][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix-1][iy][i]
# flag3+=1
else:
break
else:
if(grid[last_x-1][iy][i]!=0):
agent_ = deepcopy(agents[grid[last_x-1][iy][i]-1])
agent_.position[0] = agent_.position[0] - Lx
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[last_x-1][iy][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[last_x-1][iy][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[last_x-1][iy][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(ix+1<grid.shape[0]):
if(grid[ix+1][iy][i]!=0):
# if(ifNeighbour(a,agents[grid[ix+1][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix+1][iy][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix+1][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix+1][iy][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix+1][iy][i]-1],Rr,Ral,Rattr,fieldOfView)==3) and flag3<max_occupants-1:
# neigh_listAttr[a.ID][flag3] = grid[ix+1][iy][i]
# flag3+=1
else:
break
else:
if(grid[0][iy][i]!=0):
agent_ = deepcopy(agents[grid[0][iy][i]-1])
agent_.position[0] = agent_.position[0] + Lx
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[0][iy][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[0][iy][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[0][iy][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy+1<grid.shape[1]):
if(grid[ix][iy+1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix][iy+1][i]-1],Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix][iy+1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix][iy+1][i]-1],Rr,Ral,Rattr,fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix][iy+1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix][iy+1][i]-1],Rr,Ral,Rattr,fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix][iy+1][i]
# flag3+=1
else:
break
else:
if(grid[ix][0][i]!=0):
agent_ = deepcopy(agents[grid[ix][0][i]-1])
agent_.position[1] = agent_.position[1]+Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix][0][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix][0][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix][0][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy-1>=0):
if(grid[ix][iy-1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix][iy-1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix][iy-1][i]-1],Rr,Ral,Rattr,fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix][iy-1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix][iy-1][i]
# flag3+=1
else:
break
else:
if(grid[ix][last_y-1][i]!=0):
agent_ = deepcopy(agents[grid[ix][last_y-1][i]-1])
agent_.position[1] = agent_.position[1] - Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix][last_y-1][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix][last_y-1][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix][last_y-1][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy-1>=0 and ix-1>=0):
if(grid[ix-1][iy-1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix-1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix-1][iy-1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix-1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix-1][iy-1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix-1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix-1][iy-1][i]
# flag3+=1
else:
break
else:
if(grid[last_x-1][last_y-1][i]!=0):
agent_ = deepcopy(agents[grid[last_x-1][last_y-1][i]-1])
agent_.position[0] = agent_.position[0]-Lx
agent_.position[1] = agent_.position[1]-Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[last_x-1][last_y-1][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[last_x-1][last_y-1][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[last_x-1][last_y-1][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy+1<grid.shape[1] and ix-1>=0):
if(grid[ix-1][iy+1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix-1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix-1][iy+1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix-1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix-1][iy+1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix-1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix-1][iy+1][i]
# flag3+=1
else:
break
else:
if(grid[last_x-1][0][i]!=0):
agent_ = deepcopy(agents[grid[last_x-1][0][i]-1])
agent_.position[0] = agent_.position[0] - Lx
agent_.position[1] = agent_.position[1] + Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[last_x-1][0][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[last_x-1][0][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[last_x-1][0][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy+1<grid.shape[1] and ix+1<grid.shape[0]):
if(grid[ix+1][iy+1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix+1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix+1][iy+1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix+1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix+1][iy+1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix+1][iy+1][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix+1][iy+1][i]
# flag3+=1
else:
break
else:
if(grid[0][0][i]!=0):
agent_ = deepcopy(agents[grid[0][0][i]-1])
agent_.position[0] = agent_.position[0]+Lx
agent_.position[1] = agent_.position[1] + Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[0][0][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[0][0][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[0][0][i]
# flag3+=1
else:
break
for i in range(max_occupants):
if(iy-1>0 and ix+1<grid.shape[0]):
if(grid[ix+1][iy-1][i]!=0):
# if(ifNeighbour(a,agents[grid[ix+1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[ix+1][iy-1][i]
# flag1+=1
if(ifNeighbour(a,agents[grid[ix+1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[ix+1][iy-1][i]
flag2+=1
# elif(ifNeighbour(a,agents[grid[ix+1][iy-1][i]-1],Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[ix+1][iy-1][i]
# flag3+=1
else:
break
else:
if(grid[0][last_y-1][i]!=0):
agent_ = deepcopy(agents[grid[0][last_y-1][i]-1])
agent_.position[0]=agent_.position[0]+Lx
agent_.position[1] = agent_.position[1]-Ly
# if(ifNeighbour(a,agent_,Rr,Ral,Rattr,fieldOfView)==1 and flag1<max_occupants-1):
# neigh_listR[a.ID][flag1] = grid[0][last_y-1][i]
# flag1+=1
if(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==2 and flag2<max_occupants-1):
neigh_listAl[a.ID][flag2] = grid[0][last_y-1][i]
flag2+=1
# elif(ifNeighbour(a,agent_,Rr,Ral,Rattr, fieldOfView)==3 and flag3<max_occupants-1):
# neigh_listAttr[a.ID][flag3] = grid[0][last_y-1][i]
# flag3+=1
else:
break
return neigh_listAl
def getNeighList(agents,max_occupants,Lx,Ly,Rr,Ral,Rattr, fieldOfView):
grid, map = makeCellsGrid(agents,Ral,len(agents),Lx,Ly)
neigh_listAl = findNearest(agents, grid, map,max_occupants,Rr,Ral,Rattr, fieldOfView, Lx, Ly)
return neigh_listAl.astype(int)
def getAngle(direction):
if(direction[1]>=0):
vec1 = np.array([1,0])
vec2 = direction
if(np.linalg.norm(vec2)!=0):
vec2 = vec2/np.linalg.norm(vec2)
else:
vec2 = 0
return np.arccos(np.clip(np.dot(vec1, vec2), -1.0, 1.0))
elif(direction[1]<0):
vec1 = np.array([1,0])
vec2 = direction
if(np.linalg.norm(vec2)!=0):
vec2 = vec2/np.linalg.norm(vec2)
else:
vec2 = 0
return 2*np.pi - np.arccos(np.clip(np.dot(vec1, vec2), -1.0, 1.0))
def calculateOrder(agents):
ave = 0
for a in agents:
ave+=a.direction
ave = np.sqrt(np.sum(np.square(ave)))
ave = ave/len(agents)
return ave
def cumulativeOrder(order, time_step):
return np.trapz(order, x=None, dx=time_step, axis=- 1)/(len(order)*time_step)
def windowRMSD(cumulative_order,window_size = 10):
temp = np.asarray(cumulative_order[-window_size:])
return np.sqrt(np.sum((temp-temp.mean())**2)/(window_size-1))
def update(agents, max_occupants , max_iterations, Lx, Ly, Rr, Ral, Rattr,eta = 0, fieldOfView = 180, MI = 0.4*0.5**2, torque_parameter=0.01, alpha_r=0.2, alpha_align=0.7, alpha_attr=0.1, time_step=0.1):
neighListalign = getNeighList(agents,max_occupants,Lx,Ly,Rr,Ral,Rattr, fieldOfView)
angles = []
directions = []
positions = []
velocities = []
order = []
cumulative = []
order.append(calculateOrder(agents))
cumulative.append(cumulativeOrder(order,time_step = time_step))
for i in range(2):
temp = []
for a in agents:
temp.append(a.angle)
angles.append(temp)
for i in range(1):
temp1 = []
temp2 = []
temp3 = []
for a in agents:
temp1.append(a.direction)
temp2.append(a.position)
temp3.append(a.velocity)
directions.append(temp1)
positions.append(temp2)
for iter in range(max_iterations):
temp1 = []
temp2 = []
temp3 = []
temp4 = []
for i,a in enumerate(agents):
noise1 = uniform(-eta/2, eta/2)
# noise2 = uniform(-eta/2, eta/2)
# w_r= np.asarray([0 ,0]).astype(np.float32)
# w_attr = np.asarray([0 ,0]).astype(np.float32)
w_align = np.asarray([0 ,0]).astype(np.float32)
# angle_ini = a.angle
# for j in range(max_occupants):
# if(neighListR[i+1][j]!=0):
# w_r+=a.position - agents[neighListR[i+1][j]-1].position
# else:
# break
count = 0
for j in range(max_occupants):
if(neighListalign[i+1][j]!=0):
count+=1
w_align+=agents[neighListalign[i+1][j]-1].direction
# print(agents[neighListalign[i+1][j]-1].direction)
else:
break
if(count==0):
w_align = np.asarray([0 ,0]).astype(np.float32) # np.array([noise1, noise2])
# w_align = a.direction
else:
# print(w_align/count)
w_align = (w_align/count) #+ np.array([noise1, noise2])
# print(w_align)
# for j in range(max_occupants):
# if(neighListattr[i+1][j]!=0):
# w_attr+=(agents[neighListattr[i+1][j]-1].position - a.position)
# else:
# break
# if(np.sqrt(np.sum(np.square(w_r)))!=0):
# w_r = w_r/np.sqrt(np.sum(np.square(w_r)))
# if(np.sqrt(np.sum(np.square(w_attr)))!=0):
# w_attr = w_attr/np.sqrt(np.sum(np.square(w_attr)))
if(np.sqrt(np.sum(np.square(w_align)))!=0):
w_align = w_align/np.sqrt(np.sum(np.square(w_align)))
w = w_align
if(np.sqrt(np.sum(np.square(w)))!=0):
w = w/np.sqrt(np.sum(np.square(w)))
else:
w = a.direction
angle_final = getAngle(w)
# print("Initial Direction ", w)
# print("Initial Angle ", angle_final ," ", angle_final*180/np.pi)
# print("Noise ", noise1)
angle_final = angle_final + noise1
angle_final = angle_final%(2*np.pi)
# print("Final Angle ", angle_final , " ", angle_final*180/np.pi)
w = findDirection(angle_final)
# print("Final Direction " , w)
# if(angle_final > angle_ini):
# torque = torque_parameter*(min(np.abs(angle_final - angle_ini),np.abs(2*np.pi - (angle_final -angle_ini))))
# elif(angle_final < angle_ini):
# torque = -torque_parameter*(min(np.abs(angle_final - angle_ini),np.abs(2*np.pi - (angle_final -angle_ini))))
# else:
# torque = 0
# torque = 0
# print(angle_final)
# after_angle = 2*angles[iter+1][a.ID-1] - angles[iter][a.ID-1] + (time_step**2)*(torque)/MI
# if(angle_final > angle_ini):
# if(after_angle > angle_final):
# after_angle = angle_final
# elif(angle_final <= angle_ini):
# if(after_angle < angle_final):
# after_angle = angle_final
# if(after_angle>2*np.pi):
# after_angle = after_angle%(2*np.pi)
# if(after_angle<0):
# after_angle = 0
a.angle = angle_final
a.direction = w
# print(a.direction)
# print(a.position)
temp1.append(a.angle)
temp2.append(a.direction)
# print(a.velocity*a.direction*time_step)
# a.velocity = uniform(1,5)
position = a.position + a.velocity*a.direction*time_step
# position_ = position
# cac = 0
if(position[0]<0):
position[0] = position[0] + Lx
elif(position[0]>Lx):
position[0] = position[0] - Lx
if(position[1]<0):
position[1] = position[1] + Ly
elif(position[1]>Ly):
position[1] = position[1] - Ly
# if(position[0]<0 or position[1]<0):
# print("The position before update was ", position_)
# print("The position after update is ", position)
# print("The trigger was", cac)
# print(position_[0]>Lx)
# print(position_[1]<Ly)
# cac = 0
a.position = position
# print(a.position)
temp3.append(a.position)
temp4.append(a.velocity)
angles.append(temp1)
directions.append(temp2)
positions.append(temp3)
velocities.append(temp4)
order.append(calculateOrder(agents))
cumulative.append(cumulativeOrder(order,time_step = time_step))
# if(iter>10 and windowRMSD(cumulative, window_size=50)<10**(-4)):
# # max_iterations = 100
# # iter = 0
# print("Stopping Simulation ", iter, " Iterations have been completed")
# break
neighListalign = getNeighList(agents,max_occupants,Lx,Ly,Rr,Ral,Rattr,fieldOfView)
if((iter+1)%5==0):
print(iter+1, " Iterations completed")
print("Order of the system is ", order[-1])
return np.asarray(angles),np.asarray(directions),np.asarray(positions),np.asarray(velocities), np.asarray(order), agents, cumulative
def make_animation(agentsPositions,agentsDirections,output_file, Lx, Ly, framesPerSecond=25):
# Settings
video_file = output_file
clear_frames = False # Should it clear the figure between each frame?
fps = framesPerSecond
# Output video writer
FFMpegWriter = animation.writers['ffmpeg']
metadata = dict(title='Collective Motion', artist='Matplotlib', comment='Move')
writer = FFMpegWriter(fps=fps, metadata=metadata)
fig, ax = plt.subplots()
plt.tick_params(
axis='x',
which='both',
bottom=False,
top=False,
labelbottom=False)
plt.title('Collective Motion')
with writer.saving(fig, video_file, 100):
for i in range(len(agentsPositions)):
ax.set_xlim(Lx)
ax.set_ylim(Ly)
ax.scatter(agentsPositions[i][:,0],agentsPositions[i][:,1],color='blue', label = 'Agents', edgecolors = 'black', s=10, zorder=1, alpha = 0.8)
ax.quiver(agentsPositions[i][:,0],agentsPositions[i][:,1],agentsDirections[i][:,0] , agentsDirections[i][:,1], color='black', units='inches' , angles='xy', scale=10,width=0.015,headlength=3,headwidth=2,alpha=0.8)
writer.grab_frame()
ax.clear()
plt.clf()