def plot_paths(self, serializer, best_paths=False):
config = serializer.read_config('config.yaml', path="stats")
dim = config['num_links']
kinematics = Kinematics(dim)
if best_paths:
paths = serializer.load_paths("best_paths.yaml", path="stats")
filename = "best_paths.png"
else:
paths = serializer.load_paths("paths.yaml", path="stats")
filename = "paths.png"
sets = []
for path in paths:
path_coords = []
for elem in path:
state = [elem[i] for i in xrange(dim)]
path_coords.append(kinematics.get_end_effector_position(state))
sets.append(np.array(path_coords))
Plot.plot_2d_n_sets(sets,
xlabel='x',
ylabel='y',
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
save=self.save,
path="stats",
filename=filename)
def plot_average_dist_to_goal(self, serializer, cart_coords):
config = serializer.read_config("config.yaml", path="stats")
stats = serializer.load_stats('stats.yaml', path="stats")
m_cov = stats['m_cov']
data = []
max_avg_distance = 0.0
for k in xrange(len(m_cov)):
dists = []
for coords in cart_coords[k]:
dists.append(
np.linalg.norm(
np.array(coords) - np.array(config['goal_position'])))
avg_distance = 0.0
for d in dists:
avg_distance += d
avg_distance /= len(dists)
if avg_distance > max_avg_distance:
max_avg_distance = avg_distance
data.append(np.array([m_cov[k], avg_distance]))
Plot.plot_2d_n_sets([np.array(data)],
xlabel="joint covariance",
ylabel="average distance to goal",
x_range=[m_cov[0], m_cov[-1]],
y_range=[0, max_avg_distance],
show_legend=False,
save=self.save,
filename="stats/avg_distance.png")
def plot_average_dist_to_goal(self, serializer, cart_coords):
config = serializer.read_config("config.yaml", path="stats")
stats = serializer.load_stats('stats.yaml', path="stats")
m_cov = stats['m_cov']
data = []
max_avg_distance = 0.0
for k in xrange(len(m_cov)):
dists = []
for coords in cart_coords[k]:
dists.append(np.linalg.norm(np.array(coords) - np.array(config['goal_position'])))
avg_distance = 0.0
for d in dists:
avg_distance += d
avg_distance /= len(dists)
if avg_distance > max_avg_distance:
max_avg_distance = avg_distance
data.append(np.array([m_cov[k], avg_distance]))
Plot.plot_2d_n_sets([np.array(data)],
xlabel="joint covariance",
ylabel="average distance to goal",
x_range=[m_cov[0], m_cov[-1]],
y_range=[0, max_avg_distance],
show_legend=False,
save=self.save,
filename="stats/avg_distance.png")
def plot_paths(self, serializer, best_paths=False):
config = serializer.read_config('config.yaml', path="stats")
dim = config['num_links']
kinematics = Kinematics(dim)
if best_paths:
paths = serializer.load_paths("best_paths.yaml", path="stats")
filename = "best_paths.png"
else:
paths = serializer.load_paths("paths.yaml", path="stats")
filename = "paths.png"
sets = []
for path in paths:
path_coords = []
for elem in path:
state = [elem[i] for i in xrange(dim)]
path_coords.append(kinematics.get_end_effector_position(state))
sets.append(np.array(path_coords))
Plot.plot_2d_n_sets(sets,
xlabel='x',
ylabel='y',
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
save=self.save,
path="stats",
filename=filename)
def plot_mean_planning_times(self,
serializer,
dir="stats",
filename="",
output=""):
if filename == "":
filename = "mean_planning_times_per_step*.yaml"
if output == "":
output = "mean_planning_times_per_step.pdf"
stats = serializer.load_stats('stats.yaml', path=dir)
m_cov = stats['m_cov']
sets = []
labels = []
mean_planning_times = []
for file in glob.glob(os.path.join(os.path.join(dir, filename))):
file_str = file
try:
file_str = file.split("/")[1].split(".")[0].split("_")[
5] + "_" + file.split("/")[1].split(".")[0].split("_")[6]
except:
pass
#mean_rewards = serializer.load_stats('rewards.yaml', path="stats")
mean_planning_times.append(serializer.load_stats(file))
data = []
for k in xrange(len(m_cov)):
data.append(np.array([m_cov[k], mean_planning_times[-1][k]]))
sets.append(np.array(data))
labels.append(file_str)
if not len(mean_planning_times) == 0:
min_m = [min(m) for m in mean_planning_times]
max_m = [max(m) for m in mean_planning_times]
Plot.plot_2d_n_sets(sets,
labels=labels,
xlabel="joint covariance",
ylabel="mean planning times (seconds)",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min(min_m),
max(max_m) * 1.05],
show_legend=True,
save=self.save,
filename=dir + "/" + output)
def plot_end_effector_paths(self, serializer, plot_scenery=False, plot_manipulator=False):
config = serializer.read_config('config.yaml', path="stats")
ee_paths = serializer.load("ee_paths.yaml", path="stats")
state_paths = serializer.load("state_paths.yaml", path="stats")
kinematics = Kinematics(config['num_links'])
for file in glob.glob(os.path.join("stats", "ee_paths*.png")):
os.remove(file)
for i in xrange(len(ee_paths)):
sets = [np.array(ee_path) for ee_path in ee_paths[i]]
if plot_scenery:
obstacles = serializer.load_obstacles(path="stats/obstacles")
if not obstacles == None:
for obstacle in obstacles:
point1 = [obstacle[0] - obstacle[2] / 2.0, obstacle[1] - obstacle[3] / 2.0]
point2 = [obstacle[0] - obstacle[2] / 2.0, obstacle[1] + obstacle[3] / 2.0]
point3 = [obstacle[0] + obstacle[2] / 2.0, obstacle[1] + obstacle[3] / 2.0]
point4 = [obstacle[0] + obstacle[2] / 2.0, obstacle[1] - obstacle[3] / 2.0]
sets.append(np.array([point1, point2]))
sets.append(np.array([point2, point3]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point4, point1]))
if plot_manipulator:
for j in xrange(len(state_paths[i])):
for k in xrange(len(state_paths[i][j])):
link_1_position = kinematics.get_link_n_position(state_paths[i][j][k], 1)
link_2_position = kinematics.get_link_n_position(state_paths[i][j][k], 2)
link_3_position = kinematics.get_link_n_position(state_paths[i][j][k], 3)
sets.append(np.array([[0.0, 0.0], [link_1_position[0], link_1_position[1]]]))
sets.append(np.array([[link_1_position[0], link_1_position[1]],
[link_2_position[0], link_2_position[1]]]))
sets.append(np.array([[link_2_position[0], link_2_position[1]],
[link_3_position[0], link_3_position[1]]]))
Plot.plot_2d_n_sets(sets,
xlabel="x",
ylabel="y",
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
save=self.save,
path="stats",
filename="ee_paths" + str(i) + ".png")
def __init__(self):
waypoints = self.get_waypoints(25)
obstacles = self.get_bad_points()
print "Get K"
K = self.K(waypoints)
print "Got K "
f_prior, epsi, A = self.get_f_prior(waypoints, K)
f_obst, obst_symb_vec = self.get_f_obst(waypoints)
print "Got f"
trajectory = self.optimize(waypoints, obstacles, f_prior, f_obst, A, epsi, obst_symb_vec)
print trajectory
print obstacles
plot_sets = [np.array(trajectory), np.array(obstacles)]
plot_2d_n_sets(np.array(plot_sets),
x_range=[0.0, 15.0],
y_range=[0.0, 15.0],
plot_type="points")
def plot_emd_graph(self, serializer, cartesian_coords):
stats = serializer.load_stats('stats.yaml', path="stats")
config = serializer.read_config('config.yaml', path="stats")
#emd = stats['emd']
m_cov = stats['m_cov']
emds = []
for k in xrange(len(cartesian_coords)):
#cart_coords.append([cartesian_coords[i] for i in xrange(len(cartesian_coords))])
emds.append(calc_EMD(cartesian_coords[k], config['num_bins']))
arr = np.array([np.array([m_cov[i], emds[i]]) for i in xrange(len(emds))])
Plot.plot_2d_n_sets([arr],
xlabel='joint covariance',
ylabel='Wasserstein distance',
x_range=[m_cov[0], m_cov[-1]],
y_range=[0, max(emds)],
show_legend=False,
save=self.save,
path="stats",
filename="emd.png")
def plot_mean_rewards(self, serializer):
stats = serializer.load_stats('stats.yaml', path="stats")
m_cov = stats['m_cov']
rewards = serializer.load_stats('rewards.yaml', path="stats")
mean_rewards_data = []
variance_data = []
min_mean_reward = float("inf")
max_mean_reward = -float("inf")
min_variance = float("inf")
max_variance = -float("inf")
for k in xrange(len(m_cov)):
n, min_max, mean, var, skew, kurt = scipy.stats.describe(
np.array(rewards[k]))
mean_rewards_data.append(np.array([m_cov[k], mean]))
variance_data.append(np.array([m_cov[k], var]))
if min_max[0] < min_mean_reward:
min_mean_reward = min_max[0]
if min_max[1] > max_mean_reward:
max_mean_reward = min_max[1]
if var > max_variance:
max_variance = var
if var < min_variance:
min_variance = var
Plot.plot_2d_n_sets(
[np.array(mean_rewards_data)],
xlabel="joint covariance",
ylabel="mean reward",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min_mean_reward - 0.1, max_mean_reward + 0.1],
show_legend=False,
save=self.save,
filename="stats/mean_rewards.png")
Plot.plot_2d_n_sets([np.array(variance_data)],
xlabel="joint covariance",
ylabel="reward variance",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min_variance - 0.1, max_variance + 0.1],
show_legend=False,
save=self.save,
filename="stats/reward_variance.png")
def plot_emd_graph(self, serializer, cartesian_coords):
stats = serializer.load_stats('stats.yaml', path="stats")
config = serializer.read_config('config.yaml', path="stats")
#emd = stats['emd']
m_cov = stats['m_cov']
emds = []
for k in xrange(len(cartesian_coords)):
#cart_coords.append([cartesian_coords[i] for i in xrange(len(cartesian_coords))])
emds.append(calc_EMD(cartesian_coords[k], config['num_bins']))
arr = np.array(
[np.array([m_cov[i], emds[i]]) for i in xrange(len(emds))])
Plot.plot_2d_n_sets([arr],
xlabel='joint covariance',
ylabel='Wasserstein distance',
x_range=[m_cov[0], m_cov[-1]],
y_range=[0, max(emds)],
show_legend=False,
save=self.save,
path="stats",
filename="emd.png")
def plot_mean_rewards(self, serializer):
stats = serializer.load_stats('stats.yaml', path="stats")
m_cov = stats['m_cov']
rewards = serializer.load_stats('rewards.yaml', path="stats")
mean_rewards_data = []
variance_data = []
min_mean_reward = float("inf")
max_mean_reward = -float("inf")
min_variance = float("inf")
max_variance = -float("inf")
for k in xrange(len(m_cov)):
n, min_max, mean, var, skew, kurt = scipy.stats.describe(np.array(rewards[k]))
mean_rewards_data.append(np.array([m_cov[k], mean]))
variance_data.append(np.array([m_cov[k], var]))
if min_max[0] < min_mean_reward:
min_mean_reward = min_max[0]
if min_max[1] > max_mean_reward:
max_mean_reward = min_max[1]
if var > max_variance:
max_variance = var
if var < min_variance:
min_variance = var
Plot.plot_2d_n_sets([np.array(mean_rewards_data)],
xlabel="joint covariance",
ylabel="mean reward",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min_mean_reward - 0.1, max_mean_reward + 0.1],
show_legend=False,
save=self.save,
filename="stats/mean_rewards.png")
Plot.plot_2d_n_sets([np.array(variance_data)],
xlabel="joint covariance",
ylabel="reward variance",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min_variance - 0.1, max_variance + 0.1],
show_legend=False,
save=self.save,
filename="stats/reward_variance.png")
def plot_mean_planning_times(self, serializer, dir="stats", filename="", output=""):
if filename == "":
filename = "mean_planning_times_per_step*.yaml"
if output == "":
output = "mean_planning_times_per_step.pdf"
stats = serializer.load_stats('stats.yaml', path=dir)
m_cov = stats['m_cov']
sets = []
labels = []
mean_planning_times = []
for file in glob.glob(os.path.join(os.path.join(dir, filename))):
file_str = file
try:
file_str = file.split("/")[1].split(".")[0].split("_")[5] + "_" + file.split("/")[1].split(".")[0].split("_")[6]
except:
pass
#mean_rewards = serializer.load_stats('rewards.yaml', path="stats")
mean_planning_times.append(serializer.load_stats(file))
data = []
for k in xrange(len(m_cov)):
data.append(np.array([m_cov[k], mean_planning_times[-1][k]]))
sets.append(np.array(data))
labels.append(file_str)
if not len(mean_planning_times) == 0:
min_m = [min(m) for m in mean_planning_times]
max_m = [max(m) for m in mean_planning_times]
Plot.plot_2d_n_sets(sets,
labels=labels,
xlabel="joint covariance",
ylabel="mean planning times (seconds)",
x_range=[m_cov[0], m_cov[-1]],
y_range=[min(min_m), max(max_m) * 1.05],
show_legend=True,
save=self.save,
filename=dir + "/" + output)
def plot_end_effector_paths(self,
serializer,
plot_scenery=False,
plot_manipulator=False):
config = serializer.read_config('config.yaml', path="stats")
ee_paths = serializer.load("ee_paths.yaml", path="stats")
state_paths = serializer.load("state_paths.yaml", path="stats")
kinematics = Kinematics(config['num_links'])
for file in glob.glob(os.path.join("stats", "ee_paths*.png")):
os.remove(file)
for i in xrange(len(ee_paths)):
sets = [np.array(ee_path) for ee_path in ee_paths[i]]
if plot_scenery:
obstacles = serializer.load_obstacles(path="stats/obstacles")
if not obstacles == None:
for obstacle in obstacles:
point1 = [
obstacle[0] - obstacle[2] / 2.0,
obstacle[1] - obstacle[3] / 2.0
]
point2 = [
obstacle[0] - obstacle[2] / 2.0,
obstacle[1] + obstacle[3] / 2.0
]
point3 = [
obstacle[0] + obstacle[2] / 2.0,
obstacle[1] + obstacle[3] / 2.0
]
point4 = [
obstacle[0] + obstacle[2] / 2.0,
obstacle[1] - obstacle[3] / 2.0
]
sets.append(np.array([point1, point2]))
sets.append(np.array([point2, point3]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point4, point1]))
if plot_manipulator:
for j in xrange(len(state_paths[i])):
for k in xrange(len(state_paths[i][j])):
link_1_position = kinematics.get_link_n_position(
state_paths[i][j][k], 1)
link_2_position = kinematics.get_link_n_position(
state_paths[i][j][k], 2)
link_3_position = kinematics.get_link_n_position(
state_paths[i][j][k], 3)
sets.append(
np.array([[0.0, 0.0],
[link_1_position[0],
link_1_position[1]]]))
sets.append(
np.array([[link_1_position[0], link_1_position[1]],
[link_2_position[0],
link_2_position[1]]]))
sets.append(
np.array([[link_2_position[0], link_2_position[1]],
[link_3_position[0],
link_3_position[1]]]))
Plot.plot_2d_n_sets(sets,
xlabel="x",
ylabel="y",
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
save=self.save,
path="stats",
filename="ee_paths" + str(i) + ".png")
def plot_particles(self, serializer, particle_limit=0):
config = serializer.read_config('config.yaml', path="stats")
for file in glob.glob(os.path.join("stats", "particles*.png")):
os.remove(file)
if config['plot_particles']:
particles = serializer.load("particles.yaml", path="stats")
state_paths = serializer.load("state_paths.yaml", path="stats")
map_size = sum(
[link_dimension[0] for link_dimension in self.link_dimensions])
axis = v2_int()
ax1 = v_int()
ax2 = v_int()
ax1[:] = [0, 0, 1]
if config['workspace_dimensions'] == 2:
ax2[:] = [0, 0, 1]
elif config['workspace_dimensions'] == 3:
ax2[:] = [0, 1, 0]
axis[:] = [ax1, ax2, ax1]
kinematics = Kinematics()
kinematics.setLinksAndAxis(self.link_dimensions, axis)
obstacles = serializer.load_obstacles(path="stats/obstacles")
environment = serializer.load_environment()
for file in glob.glob(os.path.join("stats", "particles*.png")):
os.remove(file)
sets = []
color_map = []
for obstacle in environment:
point1 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point2 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point3 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point4 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point5 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point6 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point7 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point8 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
for i in xrange(len(state_paths)):
for j in xrange(len(state_paths[i])):
for k in xrange(len(state_paths[i][j])):
temp_sets = []
color_map_temp = []
if config['plot_particles'] and not k == 0:
for l in xrange(len(particles[i][j][k - 1])):
if not particle_limit == 0:
if l > particle_limit + 1:
continue
angles = v_double()
angles[:] = particles[i][j][k - 1][l]
link_1_position = kinematics.getPositionOfLinkN(
angles, 1)
link_2_position = kinematics.getPositionOfLinkN(
angles, 2)
link_3_position = kinematics.getPositionOfLinkN(
angles, 3)
temp_sets.append(
np.array([[0.0, 0.0, 0.0],
[
link_1_position[0],
link_1_position[1],
link_1_position[2]
]]))
temp_sets.append(
np.array([[
link_1_position[0], link_1_position[1],
link_1_position[2]
],
[
link_2_position[0],
link_2_position[1],
link_2_position[2]
]]))
temp_sets.append(
np.array([[
link_2_position[0], link_2_position[1],
link_2_position[2]
],
[
link_3_position[0],
link_3_position[1],
link_3_position[2]
]]))
color_map_temp.extend(
['#aaabbb' for t in xrange(3)])
angles = v_double()
angles[:] = state_paths[i][j][k]
link_1_position = kinematics.getPositionOfLinkN(
angles, 1)
link_2_position = kinematics.getPositionOfLinkN(
angles, 2)
link_3_position = kinematics.getPositionOfLinkN(
angles, 3)
temp_sets.append(
np.array([[0.0, 0.0, 0.0],
[
link_1_position[0],
link_1_position[1],
link_1_position[2]
]]))
temp_sets.append(
np.array([[
link_1_position[0], link_1_position[1],
link_1_position[2]
],
[
link_2_position[0],
link_2_position[1],
link_2_position[2]
]]))
temp_sets.append(
np.array([[
link_2_position[0], link_2_position[1],
link_2_position[2]
],
[
link_3_position[0],
link_3_position[1],
link_3_position[2]
]]))
color_map_temp.extend(['#0000ff' for t in xrange(3)])
"""
Plot the goal area
"""
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append(
[ball[0][0], ball[0][1], ball[1]])
Plot.plot_2d_n_sets(
temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[-map_size * 1.1, map_size * 1.1],
y_range=[-map_size * 1.1, map_size * 1.1],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path="stats",
filename="particles" + str(i) + "_" + str(j) +
"_" + str(k) + ".png")
elif config['workspace_dimensions'] == 3:
Plot.plot_3d_n_sets(sets=temp_sets,
balls=balls,
colormap=color_map_temp,
show_legend=False,
save=self.save,
path="stats",
filename="particles" + str(i) +
"_" + str(j) + "_" + str(k) +
".png")
def create_video(self, serializer, dir='stats'):
try:
os.makedirs(dir + "/mov")
except Exception as e:
print e
config = serializer.read_config("config.yaml", path=dir)
utils = Utils()
map_size = sum(
[link_dimension[0] for link_dimension in self.link_dimensions])
environment = serializer.load_environment(path=dir + "/environment")
sets = []
color_map = []
plot_particles = True
for obstacle in environment:
point1 = [
obstacle[0][0] - obstacle[1][0],
obstacle[0][1] - obstacle[1][1],
obstacle[0][2] - obstacle[1][2]
]
point2 = [
obstacle[0][0] - obstacle[1][0],
obstacle[0][1] - obstacle[1][1],
obstacle[0][2] + obstacle[1][2]
]
point3 = [
obstacle[0][0] - obstacle[1][0],
obstacle[0][1] + obstacle[1][1],
obstacle[0][2] - obstacle[1][2]
]
point4 = [
obstacle[0][0] - obstacle[1][0],
obstacle[0][1] + obstacle[1][1],
obstacle[0][2] + obstacle[1][2]
]
point5 = [
obstacle[0][0] + obstacle[1][0],
obstacle[0][1] - obstacle[1][1],
obstacle[0][2] - obstacle[1][2]
]
point6 = [
obstacle[0][0] + obstacle[1][0],
obstacle[0][1] - obstacle[1][1],
obstacle[0][2] + obstacle[1][2]
]
point7 = [
obstacle[0][0] + obstacle[1][0],
obstacle[0][1] + obstacle[1][1],
obstacle[0][2] - obstacle[1][2]
]
point8 = [
obstacle[0][0] + obstacle[1][0],
obstacle[0][1] + obstacle[1][1],
obstacle[0][2] + obstacle[1][2]
]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
state = None
particles_begin = False
s_first = False
particles = []
cov_num = -1
for process_covariance in self.process_covariances:
for file in glob.glob(dir + "/*.log"):
if str(process_covariance) in file:
cov_num += 1
run_num = -1
step_num = -1
with open(file, 'r') as f:
for line in f:
if "RUN #" in line or "Run #" in line:
run_num += 1
step_num = -1
elif "S_ESTIMATED:" in line:
line_arr = line.split(":")[1].strip().split(
" ")
estimated_state = [
float(line_arr[j])
for j in xrange(len(line_arr))
]
elif "PARTICLES BEGIN" in line:
particles_begin = True
particles = []
elif "p:" in line and particles_begin and not "step" in line:
if len(particles
) < config['particle_plot_limit']:
particle = line.split(
":")[1].strip().split(" ")
particle = [float(p) for p in particle]
particles.append(particle)
elif "PARTICLES END" in line:
particles_begin = False
elif "S:" in line:
line_arr = line.split(":")[1].strip().split(
" ")
state = [
float(line_arr[j])
for j in xrange(len(line_arr) / 2)
]
if not len(particles) == 0:
temp_sets = []
color_map_temp = []
step_num += 1
if plot_particles:
for particle in particles:
angles = v_double()
angles[:] = particle
link_1_position = self.kinematics.getPositionOfLinkN(
angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(
angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(
angles, 3)
temp_sets.append(
np.array(
[[0.0, 0.0, 0.0],
[
link_1_position[0],
link_1_position[1],
link_1_position[2]
]]))
temp_sets.append(
np.array(
[[
link_1_position[0],
link_1_position[1],
link_1_position[2]
],
[
link_2_position[0],
link_2_position[1],
link_2_position[2]
]]))
temp_sets.append(
np.array(
[[
link_2_position[0],
link_2_position[1],
link_2_position[2]
],
[
link_3_position[0],
link_3_position[1],
link_3_position[2]
]]))
color_map_temp.extend(
['#aaabbb' for t in xrange(3)])
angles = v_double()
angles[:] = state
img_filename = "img_" + str(
cov_num) + "_" + str(
run_num) + "_" + "0000"
if step_num < 10:
img_filename += "0"
img_filename += str(step_num) + ".png"
#img_filename = "img_" + str(cov_num) + "_" + str(run_num) + "_" + "0000" + str(step_num) + ".png"
link_1_position = self.kinematics.getPositionOfLinkN(
angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(
angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(
angles, 3)
temp_sets.append(
np.array([[0.0, 0.0, 0.0],
[
link_1_position[0],
link_1_position[1],
link_1_position[2]
]]))
temp_sets.append(
np.array([[
link_1_position[0],
link_1_position[1],
link_1_position[2]
],
[
link_2_position[0],
link_2_position[1],
link_2_position[2]
]]))
temp_sets.append(
np.array([[
link_2_position[0],
link_2_position[1],
link_2_position[2]
],
[
link_3_position[0],
link_3_position[1],
link_3_position[2]
]]))
color_map_temp.extend(
['#0000ff' for n in xrange(3)])
'''angles = v_double()
angles[:] = estimated_state
link_1_position = self.kinematics.getPositionOfLinkN(angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#ff0000' for n in xrange(3)])'''
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append([
ball[0][0], ball[0][1], ball[1]
])
Plot.plot_2d_n_sets(
temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[
-map_size * 1.1, map_size * 1.1
],
y_range=[
-map_size * 1.1, map_size * 1.1
],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path=dir + "/mov",
filename=img_filename)
sleep
def plot_state_path(self, serializer, state_path):
config = serializer.read_config('config.yaml', path="stats")
print "current dir " + os.getcwd()
kinematics = Kinematics()
kinematics.setLinksAndAxis(self.link_dimensions, axis)
kinematics = Kinematics(config['num_links'],
config['workspace_dimensions'])
obstacles = serializer.load_obstacles(path="stats/obstacles")
environment = serializer.load_environment()
for file in glob.glob(os.path.join("stats", "state_paths*.png")):
os.remove(file)
sets = []
color_map = []
for obstacle in environment:
point1 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point2 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point3 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point4 = [
obstacle[0][0] - obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point5 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point6 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] - obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
point7 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] - obstacle[1][2] / 2.0
]
point8 = [
obstacle[0][0] + obstacle[1][0] / 2.0,
obstacle[0][1] + obstacle[1][1] / 2.0,
obstacle[0][2] + obstacle[1][2] / 2.0
]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
for i in xrange(len(state_path)):
temp_sets = []
color_map_temp = []
link_1_position = kinematics.get_link_n_position(state_path[i], 0)
link_2_position = kinematics.get_link_n_position(state_path[i], 1)
link_3_position = kinematics.get_link_n_position(state_path[i], 2)
temp_sets.append(
np.array([[0.0, 0.0], [link_1_position[0],
link_1_position[1]]]))
temp_sets.append(
np.array([[link_1_position[0], link_1_position[1]],
[link_2_position[0], link_2_position[1]]]))
temp_sets.append(
np.array([[link_2_position[0], link_2_position[1]],
[link_3_position[0], link_3_position[1]]]))
color_map_temp.extend(['#0000ff' for t in xrange(3)])
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append([ball[0][0], ball[0][1], ball[1]])
Plot.plot_2d_n_sets(temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path="stats",
filename="state_path" + str(i) + ".png")
def plot_state_path(self, serializer, state_path):
config = serializer.read_config('config.yaml', path="stats")
print "current dir " + os.getcwd()
kinematics = Kinematics()
kinematics.setLinksAndAxis(self.link_dimensions, axis)
kinematics = Kinematics(config['num_links'], config['workspace_dimensions'])
obstacles = serializer.load_obstacles(path="stats/obstacles")
environment = serializer.load_environment()
for file in glob.glob(os.path.join("stats", "state_paths*.png")):
os.remove(file)
sets = []
color_map = []
for obstacle in environment:
point1 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point2 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point3 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point4 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point5 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point6 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point7 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point8 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
for i in xrange(len(state_path)):
temp_sets = []
color_map_temp = []
link_1_position = kinematics.get_link_n_position(state_path[i], 0)
link_2_position = kinematics.get_link_n_position(state_path[i], 1)
link_3_position = kinematics.get_link_n_position(state_path[i], 2)
temp_sets.append(np.array([[0.0, 0.0], [link_1_position[0], link_1_position[1]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1]],
[link_2_position[0], link_2_position[1]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1]],
[link_3_position[0], link_3_position[1]]]))
color_map_temp.extend(['#0000ff' for t in xrange(3)])
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append([ball[0][0], ball[0][1], ball[1]])
Plot.plot_2d_n_sets(temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[-3.5, 3.5],
y_range=[-3.5, 3.5],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path="stats",
filename="state_path" + str(i) + ".png")
def create_video(self, serializer, dir='stats'):
try:
os.makedirs(dir + "/mov")
except Exception as e:
print e
config = serializer.read_config("config.yaml", path=dir)
utils = Utils()
map_size = sum([link_dimension[0] for link_dimension in self.link_dimensions])
environment = serializer.load_environment(path=dir + "/environment")
sets = []
color_map = []
plot_particles = True
for obstacle in environment:
point1 = [obstacle[0][0] - obstacle[1][0], obstacle[0][1] - obstacle[1][1], obstacle[0][2] - obstacle[1][2]]
point2 = [obstacle[0][0] - obstacle[1][0], obstacle[0][1] - obstacle[1][1], obstacle[0][2] + obstacle[1][2]]
point3 = [obstacle[0][0] - obstacle[1][0], obstacle[0][1] + obstacle[1][1], obstacle[0][2] - obstacle[1][2]]
point4 = [obstacle[0][0] - obstacle[1][0], obstacle[0][1] + obstacle[1][1], obstacle[0][2] + obstacle[1][2]]
point5 = [obstacle[0][0] + obstacle[1][0], obstacle[0][1] - obstacle[1][1], obstacle[0][2] - obstacle[1][2]]
point6 = [obstacle[0][0] + obstacle[1][0], obstacle[0][1] - obstacle[1][1], obstacle[0][2] + obstacle[1][2]]
point7 = [obstacle[0][0] + obstacle[1][0], obstacle[0][1] + obstacle[1][1], obstacle[0][2] - obstacle[1][2]]
point8 = [obstacle[0][0] + obstacle[1][0], obstacle[0][1] + obstacle[1][1], obstacle[0][2] + obstacle[1][2]]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
state = None
particles_begin = False
s_first = False
particles = []
cov_num = -1
for process_covariance in self.process_covariances:
for file in glob.glob(dir + "/*.log"):
if str(process_covariance) in file:
cov_num += 1
run_num = -1
step_num = -1
with open(file, 'r') as f:
for line in f:
if "RUN #" in line or "Run #" in line:
run_num += 1
step_num = -1
elif "S_ESTIMATED:" in line:
line_arr = line.split(":")[1].strip().split(" ")
estimated_state = [float(line_arr[j]) for j in xrange(len(line_arr))]
elif "PARTICLES BEGIN" in line:
particles_begin = True
particles = []
elif "p:" in line and particles_begin and not "step" in line:
if len(particles) < config['particle_plot_limit']:
particle = line.split(":")[1].strip().split(" ")
particle = [float(p) for p in particle]
particles.append(particle)
elif "PARTICLES END" in line:
particles_begin = False
elif "S:" in line:
line_arr = line.split(":")[1].strip().split(" ")
state = [float(line_arr[j]) for j in xrange(len(line_arr) / 2)]
if not len(particles) == 0:
temp_sets = []
color_map_temp = []
step_num += 1
if plot_particles:
for particle in particles:
angles = v_double()
angles[:] = particle
link_1_position = self.kinematics.getPositionOfLinkN(angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#aaabbb' for t in xrange(3)])
angles = v_double()
angles[:] = state
img_filename = "img_" + str(cov_num) + "_" + str(run_num) + "_" + "0000"
if step_num < 10:
img_filename += "0"
img_filename += str(step_num) + ".png"
#img_filename = "img_" + str(cov_num) + "_" + str(run_num) + "_" + "0000" + str(step_num) + ".png"
link_1_position = self.kinematics.getPositionOfLinkN(angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#0000ff' for n in xrange(3)])
'''angles = v_double()
angles[:] = estimated_state
link_1_position = self.kinematics.getPositionOfLinkN(angles, 1)
link_2_position = self.kinematics.getPositionOfLinkN(angles, 2)
link_3_position = self.kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#ff0000' for n in xrange(3)])'''
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append([ball[0][0], ball[0][1], ball[1]])
Plot.plot_2d_n_sets(temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[-map_size * 1.1, map_size * 1.1],
y_range=[-map_size * 1.1, map_size * 1.1],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path=dir + "/mov",
filename=img_filename)
sleep
def plot_particles(self, serializer, particle_limit=0):
config = serializer.read_config('config.yaml', path="stats")
for file in glob.glob(os.path.join("stats", "particles*.png")):
os.remove(file)
if config['plot_particles']:
particles = serializer.load("particles.yaml", path="stats")
state_paths = serializer.load("state_paths.yaml", path="stats")
map_size = sum([link_dimension[0] for link_dimension in self.link_dimensions])
axis = v2_int()
ax1 = v_int()
ax2 = v_int()
ax1[:] = [0, 0, 1]
if config['workspace_dimensions'] == 2:
ax2[:] = [0, 0, 1]
elif config['workspace_dimensions'] == 3:
ax2[:] = [0, 1, 0]
axis[:] = [ax1, ax2, ax1]
kinematics = Kinematics()
kinematics.setLinksAndAxis(self.link_dimensions, axis)
obstacles = serializer.load_obstacles(path="stats/obstacles")
environment = serializer.load_environment()
for file in glob.glob(os.path.join("stats", "particles*.png")):
os.remove(file)
sets = []
color_map = []
for obstacle in environment:
point1 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point2 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point3 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point4 = [obstacle[0][0] - obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point5 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point6 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] - obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
point7 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] - obstacle[1][2] / 2.0]
point8 = [obstacle[0][0] + obstacle[1][0] / 2.0, obstacle[0][1] + obstacle[1][1] / 2.0, obstacle[0][2] + obstacle[1][2] / 2.0]
if config['workspace_dimensions'] == 2:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
color_map.extend(['#000000' for t in xrange(4)])
elif config['workspace_dimensions'] == 3:
sets.append(np.array([point1, point3]))
sets.append(np.array([point3, point7]))
sets.append(np.array([point7, point5]))
sets.append(np.array([point5, point1]))
sets.append(np.array([point2, point4]))
sets.append(np.array([point4, point8]))
sets.append(np.array([point8, point6]))
sets.append(np.array([point6, point2]))
sets.append(np.array([point1, point2]))
sets.append(np.array([point3, point4]))
sets.append(np.array([point7, point8]))
sets.append(np.array([point5, point6]))
color_map.extend(['#000000' for t in xrange(12)])
balls = [[config['goal_position'], config["goal_radius"]]]
color_map.extend(['green' for i in xrange(len(balls))])
for i in xrange(len(state_paths)):
for j in xrange(len(state_paths[i])):
for k in xrange(len(state_paths[i][j])):
temp_sets = []
color_map_temp = []
if config['plot_particles'] and not k == 0:
for l in xrange(len(particles[i][j][k - 1])):
if not particle_limit == 0:
if l > particle_limit + 1:
continue
angles = v_double();
angles[:] = particles[i][j][k - 1][l]
link_1_position = kinematics.getPositionOfLinkN(angles, 1)
link_2_position = kinematics.getPositionOfLinkN(angles, 2)
link_3_position = kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#aaabbb' for t in xrange(3)])
angles = v_double();
angles[:] = state_paths[i][j][k]
link_1_position = kinematics.getPositionOfLinkN(angles, 1)
link_2_position = kinematics.getPositionOfLinkN(angles, 2)
link_3_position = kinematics.getPositionOfLinkN(angles, 3)
temp_sets.append(np.array([[0.0, 0.0, 0.0], [link_1_position[0], link_1_position[1], link_1_position[2]]]))
temp_sets.append(np.array([[link_1_position[0], link_1_position[1], link_1_position[2]],
[link_2_position[0], link_2_position[1], link_2_position[2]]]))
temp_sets.append(np.array([[link_2_position[0], link_2_position[1], link_2_position[2]],
[link_3_position[0], link_3_position[1], link_3_position[2]]]))
color_map_temp.extend(['#0000ff' for t in xrange(3)])
"""
Plot the goal area
"""
temp_sets.extend(sets)
color_map_temp.extend(color_map)
if config['workspace_dimensions'] == 2:
circles = []
for ball in balls:
circles.append([ball[0][0], ball[0][1], ball[1]])
Plot.plot_2d_n_sets(temp_sets,
circles=circles,
xlabel="x",
ylabel="y",
x_range=[-map_size * 1.1, map_size * 1.1],
y_range=[-map_size * 1.1, map_size * 1.1],
plot_type="lines",
show_legend=False,
color_map=color_map_temp,
save=self.save,
path="stats",
filename="particles" + str(i) + "_" + str(j) + "_" + str(k) + ".png")
elif config['workspace_dimensions'] == 3:
Plot.plot_3d_n_sets(sets=temp_sets,
balls=balls,
colormap=color_map_temp,
show_legend=False,
save=self.save,
path="stats",
filename="particles" + str(i) + "_" + str(j) + "_" + str(k) + ".png")