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robot_room.py
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import numpy as np
from matplotlib import pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.patches import Arc
def plot_robot_room(N = 21, loc = 0, doors = [], sample_stats = None, figsize=(8,8)):
pos_radius = 0.8
outer_raius = 1
inner_big_radius = 0.95
small_radius = 0.1*21.0/N
angle = 0
delta_angle = 2*np.pi/N
fig, ax = plt.subplots(figsize=figsize)
#plt.figure(figsize=(5,5))
im = plt.imread("./images/robot.png")
oi = OffsetImage(im, zoom = 0.25)
rx = 0
ry = 0
for i in range(N):
x = pos_radius*np.cos(angle)
y = pos_radius*np.sin(angle)
pos_circle = plt.Circle((x, y), radius=small_radius, fill=False, color = 'g')
plt.gca().add_patch(pos_circle)
angle = angle + delta_angle
plt.text(x, y, str(i+1), fontsize = 12, horizontalalignment='center', verticalalignment='center')
circle_out = plt.Circle((0, 0), radius=outer_raius, fill=False)
plt.gca().add_patch(circle_out)
circle_in = plt.Circle((0, 0), radius=inner_big_radius, fill=False)
plt.gca().add_patch(circle_in)
circle_inner = plt.Circle((0, 0), radius=0.65, fill=False)
plt.gca().add_patch(circle_inner)
delta_angle_grad = delta_angle*180/np.pi
for door in doors:
door = Arc([0,0],inner_big_radius*2,inner_big_radius*2,angle=delta_angle_grad*door- delta_angle_grad*3/2,theta1=0, theta2=0.95*delta_angle_grad, color='y', linewidth='10')
plt.gca().add_patch(door)
stats_radius = 1.1
if sample_stats is not None:
max_door = 0
max_hist = 0
max_spread_hist = 0
for sample_stat in sample_stats:
if sample_stat is not None:
if 'door' in sample_stat:
if max_door<sample_stat['door']:
max_door = sample_stat['door']
if not set(range(N+1)).isdisjoint(list(sample_stat.keys())):
maximun = max(list(sample_stat.values()))
spread = max(list(sample_stat.keys())) - min(list(sample_stat.keys()))
if max_hist<maximun:
max_hist = maximun
if max_spread_hist<spread:
max_spread_hist = spread
for i, sample_stat in enumerate(sample_stats):
if sample_stat is not None:
if 'door' in sample_stat:
prediction_arc = Arc([0,0],stats_radius*2,stats_radius*2,angle=(delta_angle_grad*(i+1) - delta_angle_grad*3/2),
theta1=0, theta2=delta_angle_grad, color='g', linewidth=20*sample_stat['door']/max_door)
plt.gca().add_patch(prediction_arc)
if not set(range(N+1)).isdisjoint(list(sample_stat.keys())):
angle_width = delta_angle_grad/(N+2)
angle_base_pos = (delta_angle_grad*(i+1) - delta_angle_grad*3/2)
prediction_arc = Arc([0,0],stats_radius*2,stats_radius*2,angle=angle_base_pos,
theta1=0, theta2=angle_width/4 , color='k', linewidth=40.0)
plt.gca().add_patch(prediction_arc)
for key, value in sample_stat.items():
angle_position = angle_base_pos + (key+1)*angle_width
prediction_arc = Arc([0,0],stats_radius*2,stats_radius*2,angle=0,
theta1=angle_position-angle_width, theta2=angle_position, color='g', linewidth=20.0*value/max_hist)
plt.gca().add_patch(prediction_arc)
# Draw robot
if (type(loc)==int):
x = pos_radius*np.cos(delta_angle*(loc-1))
y = pos_radius*np.sin(delta_angle*(loc-1))
box = AnnotationBbox(oi, (x, y), frameon=False)
ax.add_artist(box)
pos_circle = plt.Circle((x, y), radius=small_radius, fill=True, color = 'y')
plt.gca().add_patch(pos_circle)
else:
for location in loc:
x = pos_radius*np.cos(delta_angle*(location-1))
y = pos_radius*np.sin(delta_angle*(location-1))
box = AnnotationBbox(oi, (x, y), frameon=False)
ax.add_artist(box)
pos_circle = plt.Circle((x, y), radius=small_radius, fill=True, color = 'y')
plt.gca().add_patch(pos_circle)
plt.axis('scaled')
plt.axis('off')
plt.show()