def simulateKernel(swarmPlot, a, b):
# move drones according to current velocity
nSwarmPlot = sw.moveSwarm(swarmPlot, simAx, swarm)
# find neighbours of target drone
neighbours = est.nearestNeighbours(swarm, targDrone, 1.0)
# use off the shelf gaussian kernel to estimate distribtuion
distribution = est.GaussianKde(neighbours, X, Y, isSwarm=True)
# show heatmap
simAx.imshow(np.flipud(distribution),
cmap=plt.cm.gist_earth_r,
extent=[0, xMax, 0, yMax])
return nSwarmPlot, a, b
def simulateKernel(swarmPlot, velField,heatMap):
heatMap.remove();
nSwarmPlot = sw.moveSwarm(swarmPlot, simAx, swarm);
nVelField = vf.updateVel(velField, simAx, X,Y,swarm);
points = sw.extractPos(swarm);
distribution = est.GaussianKde(points,X,Y)
nheatMap = simAx.imshow(np.fliplr(distribution), cmap=plt.cm.gist_earth_r, #np.flipud()
extent=[0, xMax, 0, yMax]);
return nSwarmPlot, nVelField, nheatMap;
def showVel(swarm, simAx, X, Y, visualize):
XX, XY = X.shape
U = np.zeros((XX, XY))
V = np.zeros((XX, XY))
desiredRho = est.desiredDensity()
for drone in swarm:
localGauss = est.localGaussian(swarm, drone, 3.0)
#last parameter is radius
vel = df.velocity(localGauss, drone, 0.3, desiredRho)
if drone.yInd() >= 32 or drone.xInd() >= 32:
print "Stop"
U[drone.yInd(), drone.xInd()] = vel[0]
V[drone.yInd(), drone.xInd()] = vel[1]
# print vel;
drone.vx = vel[0]
drone.vy = vel[1]
if visualize:
M = np.hypot(U, V)
Q = simAx.quiver(X,
Y,
U,
V,
M,
units='x',
pivot='mid',
width=0.022,
scale=0.75)
qk = simAx.quiverkey(Q,
0.9,
0.9,
1,
r'$1 \frac{m}{s}$',
labelpos='E',
coordinates='figure')
plt.show()
return Q
else:
return None
#U = np.cos(X)/speedFactor;
#V = np.sin(Y)/speedFactor;
#-------------- add drones ------------------#
swarm = sw.initSwarm(xMax, yMax, xIndMax, yIndMax, resolution, num=20)
#---------------------per scene logistics ----------------------------#
# Generate velocity visualization
fig, simAx = plt.subplots()
simAx.set_title("Velocity field visualization")
# initialize velocity and position
velField = vf.showVel(swarm, simAx, X, Y, False)
swarmPlot = sw.showSwarm(swarm, simAx)
points = sw.extractPos(swarm)
distribution = est.GaussianKde(points, X, Y)
heatMap = simAx.imshow(np.flipud(distribution),
cmap=plt.cm.gist_earth_r,
extent=[0, xMax, 0, yMax])
fig.colorbar(heatMap)
simAx.set_xlim([0, xMax])
simAx.set_ylim([0, yMax])
# simulateKernel is called per scene
def simulateKernel(swarmPlot, velField, heatMap):
heatMap.remove()
nSwarmPlot = sw.moveSwarm(swarmPlot, simAx, swarm)
nVelField = vf.updateVel(velField, simAx, X, Y, swarm)
points = sw.extractPos(swarm)
distribution = est.GaussianKde(points, X, Y)
U = np.cos(X) / speedFactor
V = np.sin(Y) / speedFactor
xIndMax, yIndMax = X.shape
#-------------- add drones ------------------#
swarm = sw.initSwarm(xMax,
yMax,
xIndMax,
yIndMax,
resolution,
num=40,
verbose=False)
#-------------- gaussian estimation ------------------#
targDrone = swarm[0]
neighbours = est.nearestNeighbours(swarm, targDrone, 1.0)
distribution = est.GaussianKde(neighbours, X, Y, isSwarm=True)
#---------------------per scene logistics ----------------------------#
fig, simAx = vf.showField(X, Y, U, V, returnHandle=True)
swarmPlot = sw.showSwarm(swarm, simAx)
def simulateKernel(swarmPlot, a, b):
# move drones according to current velocity
nSwarmPlot = sw.moveSwarm(swarmPlot, simAx, swarm)
# find neighbours of target drone
neighbours = est.nearestNeighbours(swarm, targDrone, 1.0)
# use off the shelf gaussian kernel to estimate distribtuion
distribution = est.GaussianKde(neighbours, X, Y, isSwarm=True)
# show heatmap