ot.set_weights( weights )
ot.set_masses( np.ones( positions.shape[ 0 ] ) * np.pi * target_radius ** 2 )
for i in range( nb_timesteps ):
print("iteration %d/%d for na=%d" % (i, nb_timesteps,na))
# optimal weights
ot.set_positions( positions )
ot.adjust_weights()
centroids = ot.get_centroids()
# display
if i in display_timesteps:
print(i)
j = display_timesteps.tolist().index(i)
ot.display_asy( directory + "/pd_{:03}.asy".format( j ), values=color_values, linewidth=0.0005, dotwidth=target_radius * 0, closing=domain_asy, avoid_bounds=True )
ot.display_vtk( directory + "/pd_{:03}.vtk".format( j ) )
h_positions.append( positions )
h_weights.append( weights )
# update positions
descent_direction = np.zeros_like(positions)
for n in range( positions.shape[ 0 ] ):
descent_direction[n,:] = g.grad( positions[ n, : ], 4 * target_radius )
positions += timestep*(descent_direction + (centroids-positions)/epsilon)
for n in range( positions.shape[ 0 ] ):
if timeout[ n ] == 0 and positions[ n, 0 ] > 4*alpha/3:
timeout[ n ] = i + 1
ot.set_weights( weights )
ot.set_masses( np.ones( positions.shape[ 0 ] ) * np.pi * target_radius ** 2 )
for i in range( nb_timesteps ):
print("iteration %d/%d for na=%d" % (i, nb_timesteps,na))
# optimal weights
ot.set_positions( positions )
ot.adjust_weights()
centroids = ot.get_centroids()
# display
if i in display_timesteps:
print(i)
j = display_timesteps.tolist().index(i)
ot.display_asy( directory + "/pd_{:03}.asy".format( j ), values=color_values, linewidth=0.0005, dotwidth=target_radius * 0, closing=domain_asy, avoid_bounds=True )
ot.display_vtk( directory + "/pd_{:03}.vtk".format( j ) )
if i in save_timesteps:
h_positions.append( positions.copy() )
# update positions
descent_direction = np.zeros_like(positions)
for n in range( positions.shape[ 0 ] ):
descent_direction[n,:] = g.grad( positions[ n, : ], 4 * target_radius )
positions += timestep*(descent_direction + (centroids-positions)/epsilon)
for n in range( positions.shape[ 0 ] ):
if timeout[ n ] == 0 and positions[ n, 0 ] > 4*alpha/3:
timeout[ n ] = i + 1
min_w = 0
ot = OptimalTransport(domain, RadialFuncEntropy(eps))
ot.set_masses(np.ones(positions.shape[0]))
weights = np.zeros(positions.shape[0])
for i in range(101):
# optimal weights
ot.set_positions(positions)
ot.adjust_weights()
weights = ot.get_weights()
min_w = np.min(weights)
max_w = np.max(weights)
# display
if i % 10 == 0:
ot.display_asy(directory + "/we_{:03}.asy".format(int(i / 10)),
values=(weights - min_w) / (max_w - min_w),
linewidth=0.002)
ot.display_asy(directory + "/pd_{:03}.asy".format(int(i / 10)),
values=(weights - min_w) / (max_w - min_w),
linewidth=0.002,
min_rf=0,
max_rf=0.35)
ot.display_vtk(directory + "/pd_{:03}.vtk".format(i))
# update positions
d = 0.75
positions = d * ot.get_centroids() + (1 - d) * positions
h_positions = []
for i in range(niter):
#print("iteration %d/%d" % (i,niter))
# optimal weights
ot.set_positions(positions)
ot.adjust_weights()
weights = ot.get_weights()
# display
if i in display_timesteps:
print(i)
j = display_timesteps.tolist().index(i)
diffpos = ot.get_centroids() - positions
rho = np.exp(-(diffpos[:, 0]**2 + diffpos[:, 1]**2 - weights) /
eps)
#if max_rho<0:
max_rho = max(rho)
ot.display_asy(directory + "/pd_{:03}.asy".format(int(j)),
values=rho / max_rho,
linewidth=0.00002)
#ot.display_vtk( directory + "/pd_{:03}.vtk".format( i ) )
if i in save_timesteps:
h_positions.append(positions.copy())
# update positions
positions = positions + (tau / eps) * (ot.get_centroids() - positions)
np.save(directory + "/positions.npy", h_positions)