def plot_one_prefpol(ec_maps, map_index, save=False):
fig, axes = plt.subplots(1, 4, figsize=(10, 3), sharex='col')
cmap = plt.cm.Spectral_r
cNorm = colors.Normalize(vmin=0, vmax=1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
chance_threshold = 0.2
if map_index == 3:
rwd_colrow = (16, 9)
else:
rwd_colrow = (14, 14)
for ind, ax in enumerate(axes.flatten()):
sizes = list(ec_maps[list(ec_maps.keys())[map_index]].keys())
print(sizes)
env_ec_maps = ec_maps[list(ec_maps.keys())[map_index]]
cache_size = list(env_ec_maps.keys())[ind]
policy_array = env_ec_maps[cache_size]
ax.pcolor(grids[map_index], vmin=0, vmax=1, cmap='bone')
ax.add_patch(plt.Rectangle(rwd_colrow, 1, 1, edgecolor='w',
fill=False))
for i in range(20):
for j in range(20):
policy = tuple(policy_array[i, j])
dx, dy = 0.0, 0.0
for pind, k in enumerate(policy):
action = pind
prob = k
if prob < 0.01:
pass
else:
dx1, dy1, head_w, head_l = make_arrows(action, prob)
dx += dx1 * prob
dy += dy1 * prob
if dx == 0.0 and dy == 0.0:
pass
else:
colorVal1 = scalarMap.to_rgba(entropy(policy))
ax.arrow(j + 0.5,
i + 0.5,
dx,
dy,
head_width=0.3,
head_length=0.2,
color=colorVal1)
ax.set_aspect('equal')
ax.set_title(f'{cache_size}')
ax.invert_yaxis()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.tight_layout()
if save:
save_format = 'svg'
plt.savefig(f'../figures/CH1/mem_map_{map_index}.{save_format}',
format=save_format)
plt.show()
def plot_all_maxpol(save=False):
fig, axs = plt.subplots(4, 5, sharex='col')
cmap = plt.cm.Spectral_r
cNorm = colors.Normalize(vmin=0, vmax=1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
chance_threshold = 0.2
for ind, name in enumerate(ec_maps.keys()):
if name[-2:] == '51':
rwd_colrow = (16, 9)
else:
rwd_colrow = (14, 14)
rect = plt.Rectangle(rwd_colrow, 1, 1, color='g', alpha=0.3)
axs[ind, 0].pcolor(grids[ind],
cmap='bone_r',
edgecolors='k',
linewidths=0.1)
axs[ind, 0].axis(xmin=0, xmax=20, ymin=0, ymax=20)
axs[ind, 0].set_aspect('equal')
axs[ind, 0].add_patch(rect)
axs[ind, 0].get_xaxis().set_visible(False)
axs[ind, 0].get_yaxis().set_visible(False)
axs[ind, 0].invert_yaxis()
for jnd, cache_size in enumerate(list(ec_maps[name].keys())):
policy_array = ec_maps[name][cache_size]
axs[ind, jnd + 1].pcolor(grids[ind], vmin=0, vmax=1, cmap='bone')
axs[ind, jnd + 1].add_patch(
plt.Rectangle(rwd_colrow, 1, 1, edgecolor='w', fill=False))
for i in range(20):
for j in range(20):
action = np.argmax(tuple(policy_array[i][j]))
prob = max(policy_array[i][j])
dx1, dy1, head_w, head_l = make_arrows(action, prob)
if prob > chance_threshold:
if (dx1, dy1) == (0, 0):
pass
else:
colorVal1 = scalarMap.to_rgba(prob)
axs[ind, jnd + 1].arrow(j + 0.5,
i + 0.5,
dx1,
dy1,
head_width=0.3,
head_length=0.2,
color=colorVal1)
else:
pass
axs[ind, jnd + 1].set_aspect('equal')
axs[ind, jnd + 1].invert_yaxis()
axs[ind, jnd + 1].get_xaxis().set_visible(False)
axs[ind, jnd + 1].get_yaxis().set_visible(False)
if save:
plt.savefig('../figures/CH1/ec_throttled_maps.svg', format='svg')
plt.show()
def plt_maxpol(env_num, env_grid, maps_dict, plot_name, save=False):
'''
:param env_name: int specifying environment type
:param maps_dict: dictionary containing policy maps
:param save: whether to save generated plot
:return: nothing
'''
fig, axes = plt.subplots(1,
len(list(maps_dict.keys())),
figsize=(2 * len(list(maps_dict.keys())) + 2, 3))
cmap = plt.cm.Spectral_r
cNorm = colors.Normalize(vmin=0, vmax=1)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cmap)
chance_threshold = 0.2
if env_num == 5:
rwd_colrow = (16, 9)
else:
rwd_colrow = (14, 14)
for ind, ax in enumerate(axes.flatten()):
# lay down base map
ax.pcolor(env_grid, vmin=0, vmax=1, cmap='bone')
ax.add_patch(plt.Rectangle(rwd_colrow, 1, 1, edgecolor='w',
fill=False))
#index corresponds to one of the elements of the maps dict
cache_size = list(maps_dict.keys())[ind]
policy_array = maps_dict[cache_size]
rows, columns = env_grid.shape
for i in range(rows):
for j in range(columns):
action = np.argmax(tuple(policy_array[i][j]))
prob = max(policy_array[i][j])
dx1, dy1, head_w, head_l = make_arrows(action, prob)
if prob > chance_threshold:
if (dx1, dy1) == (0, 0):
pass
else:
colorVal1 = scalarMap.to_rgba(prob)
ax.arrow(j + 0.5,
i + 0.5,
dx1,
dy1,
head_width=0.3,
head_length=0.2,
color=colorVal1)
else:
pass
ax.set_aspect('equal')
ax.set_title(f'{cache_size}')
ax.invert_yaxis()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.suptitle(f'{plot_name}')
plt.tight_layout()
if save:
save_format = 'svg'
plt.savefig(f'../figures/CH2/mem_map_{env_num}.{save_format}',
format=save_format)