def plotfunc(self, pylab, frame):
turn_all_axes_off(pylab)
frame = int(np.floor(frame / self.upsample))
if frame >= len(self.beliefs):
frame = len(self.beliefs) - 1
belief = self.beliefs[frame]
self.pomdp.display_state_dist(pylab, belief)
agent_state = self.agent_states[frame]
keys = sorted(agent_state.keys())
values = [agent_state[k] for k in keys]
state_string = ' '.join(map(str, values))
# pylab.figtext(0.1, 0.95 , state_string,
# fontproperties=FontProperties(size=25))
max_len = 16
size0 = 24 * 1.5
if len(state_string) > max_len:
use_len = size0 * max_len * 1.0 / len(state_string)
else:
use_len = size0
shape = self.pomdp.get_grid_shape()
dx = 0
dy = shape[1] + 0.5
pylab.text(dx, dy , state_string,
fontproperties=FontProperties(size=use_len))
pylab.axis(self.get_data_bounds())
pylab.tight_layout()
def report_maze_policy(r, mdp, policy):
f = r.figure()
with f.plot('policy') as pylab:
mdp.display_policy(pylab, policy)
turn_all_axes_off(pylab)
state_dist = run_trajectories(mdp, start=mdp.get_start_dist(), policy=policy,
nsteps=1000, ntraj=100,
goal=mdp.get_goal())
if state_dist:
with f.plot('state_dist') as pylab:
mdp.display_state_dist(pylab, state_dist)
turn_all_axes_off(pylab)
def report_maze_policy(r, mdp, policy):
f = r.figure()
with f.plot('policy') as pylab:
mdp.display_policy(pylab, policy)
turn_all_axes_off(pylab)
state_dist = run_trajectories(mdp,
start=mdp.get_start_dist(),
policy=policy,
nsteps=1000,
ntraj=100,
goal=mdp.get_goal())
if state_dist:
with f.plot('state_dist') as pylab:
mdp.display_state_dist(pylab, state_dist)
turn_all_axes_off(pylab)
def plot_2d_graph(pylab, G, plan2point, plan2color, edges2color=None, s=120):
if edges2color is None:
edges2color = lambda n1, n2: [0, 0, 0] #@UnusedVariable
cm = matplotlib.cm.get_cmap('RdYlBu')
for pi, pj, ec in get_edges_points_and_color(G, plan2point, edges2color):
coords = np.vstack((pi, pj)).T
pylab.plot(coords[0], coords[1], linestyle='-', color=ec, zorder=1)
n = len(G)
P = np.array(map(plan2point, G)).T
assert_allclose(P.shape, (2, n))
color = map(plan2color, G)
pylab.scatter(P[0], P[1], s=s, c=color, cmap=cm, zorder=3)
pylab.axis('equal')
turn_all_axes_off(pylab)
pylab.colorbar()
def plot_2d_graph(pylab, G, plan2point, plan2color, edges2color=None, s=120):
if edges2color is None:
edges2color = lambda n1, n2: [0, 0, 0] #@UnusedVariable
cm = matplotlib.cm.get_cmap('RdYlBu')
for pi, pj, ec in get_edges_points_and_color(G, plan2point, edges2color):
coords = np.vstack((pi, pj)).T
pylab.plot(coords[0], coords[1], linestyle='-', color=ec, zorder=1)
n = len(G)
P = np.array(map(plan2point, G)).T
assert_allclose(P.shape, (2, n))
color = map(plan2color, G)
pylab.scatter(P[0], P[1], s=s, c=color, cmap=cm, zorder=3)
pylab.axis('equal')
turn_all_axes_off(pylab)
pylab.colorbar()
def plot_style_servo_field_xy(pylab, area_graphs):
centroid = [0, 0]
pylab.plot(centroid[0], centroid[1], 'go')
# M = 2.0
M = area_graphs
b = 0.03
if False:
pylab.plot([-M + b, -M + b], [-M + b, -M + 1 + b], 'k-')
pylab.plot([-M + b + 1, -M + b], [-M + b, -M + b], 'k-')
pylab.axis('equal')
if False:
N = M - b * 2
pylab.plot([-N, N, N, -N, -N], [-N, -N, N, N, -N], 'k--')
pylab.axis((-M, +M, -M, +M))
turn_all_axes_off(pylab)
def plotfunc(self, pylab, frame):
turn_all_axes_off(pylab)
frame = int(np.floor(frame / self.upsample))
if frame >= len(self.beliefs):
frame = len(self.beliefs) - 1
belief = self.beliefs[frame]
self.pomdp.display_state_dist(pylab, belief)
agent_state = self.agent_states[frame]
keys = sorted(agent_state.keys())
values = [agent_state[k] for k in keys]
state_string = ' '.join(map(str, values))
# pylab.figtext(0.1, 0.95 , state_string,
# fontproperties=FontProperties(size=25))
max_len = 16
size0 = 24 * 1.5
if len(state_string) > max_len:
use_len = size0 * max_len * 1.0 / len(state_string)
else:
use_len = size0
shape = self.pomdp.get_grid_shape()
dx = 0
dy = shape[1] + 0.5
pylab.text(dx,
dy,
state_string,
fontproperties=FontProperties(size=use_len))
pylab.axis(self.get_data_bounds())
pylab.tight_layout()
def plan_report(self, report, result, tc):
"""
We pass the testcase structure, so we can use the ground
truth (if available) to make a nicer visualization.
"""
from matplotlib.cm import get_cmap
f = report.figure(cols=3)
plan2length = lambda x: len(x)
cmap_start = get_cmap('bones')
cmap_goal = get_cmap('jet')
def distance_to(tree, y, node):
image = tree.plan2image(node)
return self.metric_goal.distance(image, y)
from functools import partial
start_dist_y0 = partial(distance_to, self.start_tree, self.y0)
goal_dist_y0 = partial(distance_to, self.goal_tree, self.y0)
start_dist_y1 = partial(distance_to, self.start_tree, self.y1)
goal_dist_y1 = partial(distance_to, self.goal_tree, self.y1)
if result.success:
# Plot the distance as a function of step
steps = plan_steps(result.plan) # (), (0,) (0, 1), etc.
distance_to_y0 = map(start_dist_y0, steps)
distance_to_y1 = map(start_dist_y1, steps)
with f.plot('distances_along_path') as pylab:
lenghts = map(len, steps)
pylab.plot(lenghts, distance_to_y0, 'r-', label='to $y_0$')
pylab.plot(lenghts, distance_to_y1, 'b-', label='to $y_1$')
pylab.xlabel('subplan length')
pylab.legend()
with f.plot('start_tree') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab,
plan2color=plan2length,
cmap=cmap_start,
show_plan=tc.true_plan)
pylab.title('plan length')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_goal)
pylab.title('plan length')
turn_all_axes_off(pylab)
pylab.colorbar()
true_plan = tc.true_plan
ss = guess_state_space(self.id_dds, self._dds)
u0 = self.get_dds().indices_to_commands(true_plan)
origin = ss.state_from_commands(u0)
with f.plot('joint') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('start_tree_y0') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y0, cmap=cmap_start)
pylab.title('distance to $y_0$')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree_y0') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y0, cmap=cmap_goal, origin=origin)
pylab.title('distance to y_0')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('joint_y0') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y0, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y0, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('start_tree_y1') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y1, cmap=cmap_start)
pylab.title('distance to $y_1$')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree_y1') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y1, cmap=cmap_goal)
pylab.title('distance to y_1')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('joint_y1') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y1, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y1, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
def common_settings(pylab):
pylab.axis('equal')
pylab.axis((-M, M, -M, M))
turn_all_axes_off(pylab)
def common_settings(pylab):
pylab.axis('equal')
pylab.axis((-M, M, -M, M))
turn_all_axes_off(pylab)
def plan_report(self, report, result, tc):
"""
We pass the testcase structure, so we can use the ground
truth (if available) to make a nicer visualization.
"""
f = report.figure(cols=3)
plan2length = lambda x: len(x)
cmap_start = get_cmap('bones')
cmap_goal = get_cmap('jet')
def distance_to(tree, y, node):
image = tree.plan2image(node)
return self.metric_goal.distance(image, y)
from functools import partial
start_dist_y0 = partial(distance_to, self.start_tree, self.y0)
goal_dist_y0 = partial(distance_to, self.goal_tree, self.y0)
start_dist_y1 = partial(distance_to, self.start_tree, self.y1)
goal_dist_y1 = partial(distance_to, self.goal_tree, self.y1)
if result.success:
# Plot the distance as a function of step
steps = plan_steps(result.plan) # (), (0,) (0, 1), etc.
distance_to_y0 = map(start_dist_y0, steps)
distance_to_y1 = map(start_dist_y1, steps)
with f.plot('distances_along_path') as pylab:
lenghts = map(len, steps)
pylab.plot(lenghts, distance_to_y0, 'r-', label='to $y_0$')
pylab.plot(lenghts, distance_to_y1, 'b-', label='to $y_1$')
pylab.xlabel('subplan length')
pylab.legend()
with f.plot('start_tree') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_start,
show_plan=tc.true_plan)
pylab.title('plan length')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_goal)
pylab.title('plan length')
turn_all_axes_off(pylab)
pylab.colorbar()
true_plan = tc.true_plan
ss = guess_state_space(self.id_dds, self._dds)
u0 = self.get_dds().indices_to_commands(true_plan)
origin = ss.state_from_commands(u0)
with f.plot('joint') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=plan2length, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('start_tree_y0') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y0, cmap=cmap_start)
pylab.title('distance to $y_0$')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree_y0') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y0, cmap=cmap_goal, origin=origin)
pylab.title('distance to y_0')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('joint_y0') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y0, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y0, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('start_tree_y1') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y1, cmap=cmap_start)
pylab.title('distance to $y_1$')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('goal_tree_y1') as pylab:
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y1, cmap=cmap_goal)
pylab.title('distance to y_1')
turn_all_axes_off(pylab)
pylab.colorbar()
with f.plot('joint_y1') as pylab:
self.start_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=start_dist_y1, cmap=cmap_start)
self.goal_tree.plot_graph_using_guessed_statespace(
pylab, plan2color=goal_dist_y1, cmap=cmap_goal, origin=origin)
turn_all_axes_off(pylab)
pylab.colorbar()
