def loadTagList(self,fname="tags"):
f=open(fname+".csv",'r')
taglist=[]
from AcousticTag import AcousticTag
for line in f.readlines():
last_ping,posx,posy,posz,delay,ID,bin=line.split(',')
tag = AcousticTag(ID,last_ping=float(last_ping),ping_delay=float(delay))
tag.pos=np.array([float(posx),float(posy),float(posz)])
tag.bin=bin
taglist.append(tag)
f.close()
self.taglist=taglist
return taglist
plt.xlim([0, grid.x_range])
plt.ylim([0, grid.y_range])
plt.xticks(np.arange(0, grid.x_range, grid.x_range / 5.0))
plt.yticks(np.arange(0, grid.y_range, grid.y_range / 5.0))
plt.draw()
taglist = []
N = 100 # number of tags to be generated in field
#generate N tags and append to taglist
for i in range(N):
delay = max(1, 30 * np.random.rand())
#each tag object has an ID, an offset (last_ping), and a delay between pings (ping_delay)
taglist.append(
AcousticTag(i, last_ping=-delay * np.random.rand(), ping_delay=delay))
# create grid world object
grid = Grid(taglist, x_range=100, y_range=100)
#make map of densities for each cell
density_map = np.array([
0.1, 0.1, 0.4, 0.3, 0.2, 0.1, 0.3, 0.3, 0.1, 0.3, 0.2, 0.3, 0.3, 0.2, 0.1,
0.3, 0.9, 0.3, 0.2, 0.1, 0.2, 0.3, 0.2, 0.1, 0.1
])
grid.setMap(density_map) #set map with no arguement sets the default map
#saveTaglist to .csv file
grid.saveTagList(fname="testField")
############################################### Plotting ############################
#get all tag position to plot
tagx = np.zeros(N)
tagy = np.zeros(N)
t = 0
last_meas = t
run = False
running = False
searchComplete = False
updateGP = False
latestMeas = 0
taglist = []
agentList = []
tagx = np.zeros(N)
tagy = np.zeros(N)
for i in range(N):
# taglist.append(AcousticTag(i,last_ping=np.random.randn()),ping_delay=max(2,30*np.random.randn())) # most realistic
taglist.append(AcousticTag(
i, last_ping=17 *
np.random.randn())) # more realistic (pings are not aligned in time)
# taglist.append(AcousticTag(i)) #better for understanding because pings are aligned in time and all have same ping interval
x, y, _ = taglist[i].pos
tagx[i] = x
tagy[i] = y
E = Grid(taglist, x_range=x_range, y_range=y_range)
if field == fields[0]:
taglist = E.loadTagList() # E.setMap(density_map)
# E.saveTagList()
for i in range(numAgents):
s = AcousticReciever(np.array([0, 0, 0]), sensorRange)
if method == searchMethods[2]:
# agentList.append(Agent(np.array([np.random.rand()*x_range,np.random.rand()*y_range,0,0]),s,E,dim=2))
agentList.append(
N = 1000 #how many tags present
simtime = 100 #max simulation time
numAgents = 1 #number of agents exploring
measurement_time = 1
time_step = .5
running = False
searchComplete = False
taglist = []
agentList = []
tagx = np.zeros(N)
tagy = np.zeros(N)
for i in range(N):
#taglist.append(AcousticTag(i,last_ping=np.random.randn()),ping_delay=max(2,30*np.random.randn())) # most realistic
taglist.append(AcousticTag(i, last_ping=np.random.randn())
) # more realistic (pings are not aligned in time)
#taglist.append(AcousticTag(i)) #better for understanding because pings are aligned in time and all have same ping interval
x, y, _ = taglist[i].pos
tagx[i] = x
tagy[i] = y
E = Grid(taglist)
E.setMap(density_map)
for i in range(numAgents):
s = AcousticReciever(np.array([0, 0, 0]), 50)
agentList.append(Agent(np.array([(i * 2 + 1) * 50.0, 605.0]), s, E, dim=2))
agentList[i].dynamics = m1_step
for i in range(N):
def m1_step(x,u):
return 1*np.array([np.cos(u), np.sin(u)])
#settings
N = 1000
simtime=3
numAgents=1
time_step=.5
#np.random.seed(1)
#setup
taglist=[]
agentList=[]
tagx=np.zeros(N)
tagy=np.zeros(N)
for i in range(N):
taglist.append(AcousticTag(i,last_ping=15*np.random.randn())) # more realistic
#taglist.append(AcousticTag(i)) #better for understanding
x,y,_ = taglist[i].pos
tagx[i]=x
tagy[i]=y
E = Grid(taglist)
E.setMap()
for i in range(numAgents):
s= AcousticReciever(np.array([0,0,0]),50)
agentList.append(Agent(np.array([50.0,600.0]),s,E,dim=2))
agentList[i].dynamics=m1_step
for i in range(N):
x,y,_ = taglist[i].pos
self.y_range * 1.0 / len(self.y_bins))
self.taglist[count].pos = np.array([
xr + mpx * np.random.rand(),
yr + mpy * np.random.rand(), zr * np.random.rand()
])
self.taglist[count].bin = i
self.tag_count[i] += 1
count += 1
if __name__ == '__main__':
from AcousticTag import AcousticTag
taglist = []
N = 1000
for i in range(N):
taglist.append(AcousticTag(i))
grid = Grid(taglist)
grid.setMap()
tau = .5
# print('tau=',tau)
# print(np.round(grid.groundTruth(tau),decimals=2))
# print('method 2 tau=',tau)
# print(np.round(grid.approximateField(tau,spacing=(200,200),sensorRange=110),decimals=2))
# #print(np.round(grid.groundTruth(tau,fixed=True),decimals=2))
# tau=2
# print('tau=',tau)
# print(np.round(grid.groundTruth(tau),decimals=2))
# print('method 2 tau=',tau)
# print(np.round(grid.approximateField(tau,spacing=(200,200),sensorRange=110),decimals=2))
# #print(np.round(grid.groundTruth(tau,fixed=True),decimals=2))
# tau=5
