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 _plot_valfun(self, VMat):
"""
:returns: handle to the figure
.. warning::
The calling function MUST call pl.draw() or the figures
will not be updated.
"""
if self.valueFunction_fig is None or self.valueFunction_img is None:
maxV = VMat.max()
minV = VMat.min()
self.valueFunction_fig = pl.figure("Value Function")
ax = self.valueFunction_fig.add_subplot(111)
self.valueFunction_img = ax.imshow(
VMat,
cmap='ValueFunction',
interpolation='nearest',
origin='lower',
vmin=minV,
vmax=maxV)
pl.xticks(self.xTicks, self.xTicksLabels, fontsize=12)
# Don't need the y labels since we share axes on subplot
pl.xlabel(r"$\theta$ (degree)")
pl.title('Value Function')
norm = colors.Normalize(vmin=VMat.min(), vmax=VMat.max())
self.valueFunction_img.set_data(VMat)
self.valueFunction_img.set_norm(norm)
self.valueFunction_fig.canvas.draw()