mymap = vplot.get_vmap(2)
extent = [
grids['states'][1][0], grids['states'][1][-1], grids['states'][0][0],
grids['states'][0][-1]
]
fig, axs = plt.subplots(1, figsize=(6, 5))
X_value = vibly.project_Q2S(data["Q_value"], grids, proj_opt=np.max)
# lvls = [0., 0.15]
print(X_value.max())
pc1 = vplot.value_function(axs,
X_value,
grids,
XV=XV,
cont_lvls=None,
mymap=mymap,
mynorm=mynorm)
# fig.colorbar(pc1, ax=axs, orientation='horizontal', extend='both')
fig.colorbar(pc1,
ax=axs,
orientation='horizontal',
extend='max',
ticks=[0., 1, 2.])
# * Reward function
# pc0 = vplot.reward_function(axs, RX_value, grids, XV=XV,
# mymap="PRGn", mynorm=mynorm) #,
# viability_threshold=0)
# fig.colorbar(pc0, ax=axs)
ymin=v_min,
ymax=v_max,
where=~XV_slice,
facecolor='red',
alpha=0.2)
axs[0].add_collection(collection)
axs[0].grid(True)
# * plot value functions
lvls = [-100., -75., -50., -25., alpha, 1.]
for pdx, idx in enumerate(these_values):
data = data_list[idx]
X_value = vibly.project_Q2S(data["Q_value"], grids, proj_opt=np.max)
pc3 = vplot.value_function(axs[pdx + 1],
X_value,
grids,
XV=XV,
mymap=mymap,
mynorm=mynorm)
for idx in range(num_plots - 1):
plt.setp(axs[idx].get_xticklabels(), visible=False)
fig.colorbar(pc1,
ax=axs,
orientation='horizontal',
extend='both',
spacing='proportional',
ticks=lvls)
# plt.savefig("Test up to 1e9"+'.pdf', format='pdf')
plt.show()
plt.close()
# viability_threshold = X_value[XV].min()
# mynorm = colors.CenteredNorm()
# mymap = plt.get_cmap('bwr_r')
# shrunk_cmap = vplot.shiftedColorMap(mymap, start=0.2, midpoint=0.5, stop=0.8, name='shrunk')
extent = [
grids['states'][1][0], grids['states'][1][-1], grids['states'][0][0],
grids['states'][0][-1]
]
fig, axs = plt.subplots(2, 1)
# * Reward function
RX_value = vibly.project_Q2S(R_value, grids, proj_opt=np.mean)
pc0 = vplot.reward_function(axs[0], RX_value, grids, XV=XV, mymap="PRGn")
fig.colorbar(pc0, ax=axs[0])
# * Value Function
pc1 = vplot.value_function(axs[1], X_value, grids, XV=XV, mymap=mymap)
fig.colorbar(pc1, ax=axs[1])
plt.savefig(savename + str(failure_penalty) + '.pdf', format='pdf')
print("******************")
print("******************")
# plt.imshow(np.transpose(S_M), origin='lower') # visualize the S-safety measure
# plt.show()
# plt.imshow(Q_V) # visualize the viable set
# plt.show()
plt.savefig('negproxy_R.pdf', format='pdf')
plt.close()
# * plot value functions
# lvls = [-0.2, -0.1, -0.001, 0., 0.001, 0.1, 0.2, 1.]
# lvls = [-100., -200., -50., -10., 0., 0.5, 1., 1.5, 2., 2.5, 3. ].sort()
# lvls = [-100., -200., -50., 0., 1., 2., 3.].sort()
# lvls=10
for pdx, data in enumerate(data_list):
# data = data_list[ndx]
fig, ax = plt.subplots(figsize=(colwide, 3 / 6 * colwide),
constrained_layout=True)
X_value = vibly.project_Q2S(data["Q_value"], grids, proj_opt=np.max)
pc1 = vplot.value_function(
ax,
X_value,
grids,
XV=XV,
# cont_lvls=lvls,
mymap=mymap,
mynorm=mynorm)
print("max viable: ", X_value[XV].max())
print("min viable: ", X_value[XV].min())
print("max unviable: ", X_value[~XV].max())
print("min unviable: ", X_value[~XV].min())
print("-----")
fig.colorbar(pc1,
ax=ax,
orientation='horizontal',
extend='both',
spacing='proportional',
shrink=0.8)
# fig.colorbar(pc1, ax=ax, orientation='horizontal', extend='min',