def _plot_valfun(self, VMat, xlim=None, ylim=None):
"""
:returns: handle to the figure
"""
plt.figure("Value Function")
# pl.xticks(self.xTicks,self.xTicksLabels, fontsize=12)
# pl.yticks(self.yTicks,self.yTicksLabels, fontsize=12)
# pl.xlabel(r"$\theta$ (degree)")
# pl.ylabel(r"$\dot{\theta}$ (degree/sec)")
plt.title("Value Function")
if xlim is not None and ylim is not None:
extent = [xlim[0], xlim[1], ylim[0], ylim[1]]
else:
extent = [0, 1, 0, 1]
self.valueFunction_fig = plt.imshow(
VMat,
cmap="ValueFunction",
interpolation="nearest",
origin="lower",
extent=extent,
)
norm = colors.Normalize(vmin=VMat.min(), vmax=VMat.max())
self.valueFunction_fig.set_data(VMat)
self.valueFunction_fig.set_norm(norm)
plt.draw()
def show_domain(self, a=0):
s = self.state
# Draw the environment
if self.domain_fig is None:
self.move_fig = plt.subplot(111)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig = plt.imshow(s,
cmap="FlipBoard",
interpolation="nearest",
vmin=0,
vmax=1)
plt.xticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
plt.yticks(np.arange(self.BOARD_SIZE), fontsize=FONTSIZE)
# pl.tight_layout()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig = self.move_fig.plot(a_col,
a_row,
"kx",
markersize=30.0)
plt.show()
a_row, a_col = id2vec(a, [self.BOARD_SIZE, self.BOARD_SIZE])
self.move_fig.pop(0).remove()
# print a_row,a_col
# Instead of '>' you can use 'D', 'o'
self.move_fig = plt.plot(a_col, a_row, "kx", markersize=30.0)
s = s.reshape((self.BOARD_SIZE, self.BOARD_SIZE))
self.domain_fig.set_data(s)
plt.draw()
def show_domain(self, a=0):
# Draw the environment
s = self.state
world = np.zeros((self.blocks, self.blocks), "uint8")
undrawn_blocks = np.arange(self.blocks)
while len(undrawn_blocks):
A = undrawn_blocks[0]
B = s[A]
undrawn_blocks = undrawn_blocks[1:]
if B == A: # => A is on Table
world[0, A] = A + 1 # 0 is white thats why!
else:
# See if B is already drawn
i, j = findElemArray2D(B + 1, world)
if len(i):
world[i + 1, j] = A + 1 # 0 is white thats why!
else:
# Put it in the back of the list
undrawn_blocks = np.hstack((undrawn_blocks, [A]))
if self.domain_fig is None:
plt.figure("Domain")
self.domain_fig = plt.imshow(
world,
cmap="BlocksWorld",
origin="lower",
interpolation="nearest") # ,vmin=0,vmax=self.blocks)
plt.xticks(np.arange(self.blocks), fontsize=FONTSIZE)
plt.yticks(np.arange(self.blocks), fontsize=FONTSIZE)
# pl.tight_layout()
plt.axis("off")
plt.show()
else:
self.domain_fig.set_data(world)
plt.figure("Domain").canvas.draw()
plt.figure("Domain").canvas.flush_events()
def _plot_policy(self,
piMat,
title="Policy",
var="policy_fig",
xlim=None,
ylim=None):
"""
:returns: handle to the figure
"""
if getattr(self, var, None) is None:
plt.figure(title)
# define the colormap
cmap = plt.cm.jet
# extract all colors from the .jet map
cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be grey
cmaplist[0] = (0.5, 0.5, 0.5, 1.0)
# create the new map
cmap = cmap.from_list("Custom cmap", cmaplist, cmap.N)
# define the bins and normalize
bounds = np.linspace(0, self.num_actions, self.num_actions + 1)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
if xlim is not None and ylim is not None:
extent = [xlim[0], xlim[1], ylim[0], ylim[1]]
else:
extent = [0, 1, 0, 1]
self.__dict__[var] = plt.imshow(
piMat,
interpolation="nearest",
origin="lower",
cmap=cmap,
norm=norm,
extent=extent,
)
# pl.xticks(self.xTicks,self.xTicksLabels, fontsize=12)
# pl.yticks(self.yTicks,self.yTicksLabels, fontsize=12)
# pl.xlabel(r"$\theta$ (degree)")
# pl.ylabel(r"$\dot{\theta}$ (degree/sec)")
plt.title(title)
plt.colorbar()
plt.figure(title)
self.__dict__[var].set_data(piMat)
plt.draw()
def show_domain(self, a):
s = self.state
# Draw the environment
fig = plt.figure("IntruderMonitoring")
if self.domain_fig is None:
self.domain_fig = plt.imshow(
self.map,
cmap="IntruderMonitoring",
interpolation="nearest",
vmin=0,
vmax=3,
)
plt.xticks(np.arange(self.COLS), fontsize=FONTSIZE)
plt.yticks(np.arange(self.ROWS), fontsize=FONTSIZE)
plt.show()
if self.ally_fig is not None:
self.ally_fig.pop(0).remove()
self.intruder_fig.pop(0).remove()
s_ally = s[0:self.NUMBER_OF_AGENTS * 2].reshape((-1, 2))
s_intruder = s[self.NUMBER_OF_AGENTS * 2:].reshape((-1, 2))
self.ally_fig = plt.plot(
s_ally[:, 1],
s_ally[:, 0],
"bo",
markersize=30.0,
alpha=0.7,
markeredgecolor="k",
markeredgewidth=2,
)
self.intruder_fig = plt.plot(
s_intruder[:, 1],
s_intruder[:, 0],
"g>",
color="gray",
markersize=30.0,
alpha=0.7,
markeredgecolor="k",
markeredgewidth=2,
)
fig.canvas.draw()
fig.canvas.flush_events()