def update(frame_num):
nonlocal global_step
nonlocal arrows
global_step = frame_num
robot.center = robot_positions[frame_num]
for i, human in enumerate(humans):
human.center = human_positions[frame_num][i]
human_numbers[i].set_position((human.center[0] - x_offset,
human.center[1] - y_offset))
for arrow in arrows:
arrow.remove()
arrows = [
patches.FancyArrowPatch(*orientation[frame_num],
color=arrow_color,
arrowstyle=arrow_style)
for orientation in orientations
]
for arrow in arrows:
ax.add_artist(arrow)
# if self.attention_weights is not None:
# human.set_color(str(self.attention_weights[frame_num][i]))
# attention_scores[i].set_text('human {}: {:.2f}'.format(i, self.attention_weights[frame_num][i]))
time.set_text('Time: {:.2f}'.format(frame_num *
self.time_step))
def animate(i):
# according to number of savings
time.set_text("time={}".format(i+1))
# agent
for enum,ag in enumerate(trial.agents):
# agent body
agent_loc = Point(ag.data.x[i],ag.data.y[i])
agent_body = agent_loc.buffer(ag.data.r)
agents[enum].set_data(*agent_body.exterior.xy)
# trajectory
trajs[enum].set_data(ag.data.x[:i+1],ag.data.y[:i+1])
# ir sensors
ix = [ir for ir in ag.data.irs[i] if ir]
for n_ir,ir in enumerate(ix):
irs[enum][n_ir].set_data(*ir.exterior.xy)
# feeding areas
fx = ag.data.x[i] + (ag.data.r+ag.data.f_range/2)*np.sin(ag.data.o[i])
fy = ag.data.y[i] + (ag.data.r+ag.data.f_range/2)*np.cos(ag.data.o[i])
floc = Point(fx,fy)
f_area = floc.buffer(ag.data.f_range/2)
fas[enum].set_data(*f_area.exterior.xy)
# same tuple for all irs of all agents
all_irs = [ir for irx in irs for ir in irx]
return tuple(agents),tuple(trajs),tuple(all_irs),tuple(fas)
def animate(i):
# i: from number of savings in trial
time.set_text("time={}".format(i))
# at: adjusted i for savings from agents' states
at = int(i / evaluation.dt)
# main centroid and triangle
centroid.set_data(best_trial.triangles[i].centroid.xy)
ags.set_data(*best_trial.triangles[i].exterior.xy)
# remaining centroids (can be shorter)
if i < len(rm_trials[0].triangles):
cent2.set_data(rm_trials[0].triangles[i].centroid.xy)
if i < len(rm_trials[1].triangles):
cent3.set_data(rm_trials[1].triangles[i].centroid.xy)
if i < len(rm_trials[2].triangles):
cent4.set_data(rm_trials[2].triangles[i].centroid.xy)
if i < len(rm_trials[3].triangles):
cent5.set_data(rm_trials[3].triangles[i].centroid.xy)
# ax1 agents
for enum, ag in enumerate(best_trial.agents):
# agent body
agents[enum].set_data(*ag.data.body[at].exterior.xy)
# trajectories
trajs[enum].set_data(ag.data.x[:at], ag.data.y[:at])
# sensors
ag_irs = [ir for ir in ag.data.irs[at] if ir]
for n_ir, ir in enumerate(ag_irs):
irs[enum][n_ir].set_data(*ir.xy)
all_irs = [ir for irx in irs for ir in irx]
# time mark for ax6
tbar.set_data([i, i], [0, 1])
#import pdb; pdb.set_trace()
return centroid, ags, traj, tuple(agents) + tuple(all_irs)
def update(frame_num):
nonlocal global_step
global_step = frame_num
robot.center = robot_positions[frame_num]
for i, obstacle in enumerate(obstacles):
obstacle.center = obstacle_positions[frame_num][i]
obstacle_numbers[i].set_position((obstacle.center[0] - x_offset, obstacle.center[1] - y_offset))
time.set_text('Time: {:.2f}'.format(frame_num * self.time_step))
def update(frame_num):
nonlocal global_step
nonlocal arrows
global_step = frame_num
robot.center = robot_positions[frame_num]
for i, human in enumerate(humans):
human.center = human_positions[frame_num][i]
human_numbers[i].set_position((human.center[0] - x_offset,
human.center[1] - y_offset))
for arrow in arrows:
arrow.remove()
arrows = [
patches.FancyArrowPatch(*orientation[frame_num],
color=arrow_color,
arrowstyle=arrow_style)
for orientation in orientations
]
for arrow in arrows:
ax.add_artist(arrow)
if self.attention_weights is not None:
human.set_color(
str(self.attention_weights[frame_num][i]))
attention_scores[i].set_text('human {}: {:.2f}'.format(
i, self.attention_weights[frame_num][i]))
# # print("agents_future_circles", agents_future_circles)
# for i, circles in enumerate(agents_future_circles):
# print("i", i, " cirle", circles)
# for j, circle in enumerate(circles):
# # print("j", j)
# # print("agents_future_circles", agents_future_positions[0][j])
# # for k in range(len(agents_future_positions[0][j])):
#
# circle.center = [agents_future_positions[0][i][j], agents_future_positions[1][i][j]]
# print("circle", circle.center)
time.set_text('Time: {:.2f}'.format(frame_num *
self.time_step))
def update(i, x, y, scat, d_t, time, Acc_fac):
scat.set_offsets(np.vstack((x[Acc_fac * i, :], y[Acc_fac * i, :])).T)
t = mth.floor(Acc_fac * d_t * i)
time.set_text('Time = {}s'.format(t))
return scat, time,