def plot_visitation(traj, mdp, args, filename='visitation.pdf'):
# TODO: it only works for Pinball.
if args.tasktype == 'pinball':
xs = [s.data[0] for s in traj]
ys = [s.data[1] for s in traj]
plt.plot(xs, ys, 'b.', alpha=0.5)
for obs in mdp.domain.environment.obstacles:
point_list = obs.points
xlist = []
ylist = []
for p in point_list:
xlist.append(p[0])
ylist.append(p[1])
plt.fill(xlist, ylist, 'k')
elif args.task == 'PointMaze-v0' or args.task == 'AntMaze-v0':
xs = []
ys = []
for s in traj:
if -4.0 <= s.data[0] and s.data[0] <= -4.0 + 8.0 * 3.0 and \
-4.0 <= s.data[1] and s.data[1] <= -4.0 + 8.0 * 3.0:
xs.append(s.data[0])
ys.append(s.data[1])
plt.plot(xs, ys, 'b.', alpha=0.5)
# TODO: (x,y) coordinates start at 0, 0.
# How is the coordinates signed?
maze = [[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 0, 1],
[1, 0, 0, 0, 1], [1, 1, 1, 1, 1]]
scale = 8.0
for y in range(5):
for x in range(5):
if maze[y][x] == 1:
# We decrement x and y because the (0, 0)-coordinate is set at (1, 1) position in the maze.
xbase, ybase = scale * (x - 1.5), scale * (y - 1.5)
xlist = [xbase, xbase + scale, xbase + scale, xbase]
ylist = [ybase, ybase, ybase + scale, ybase + scale]
plt.fill(xlist, ylist, 'k')
elif args.task == 'MontezumaRevenge-ram-v0':
# TODO: Show the background of the Monte?
img = imread('./montezuma.jpg')
plt.imshow(img, zorder=0, extent=[0, 160, 0, 210])
feature = Monte()
pairs = [feature.feature(s, 0) for s in traj]
xs_img = [p[0] for p in pairs]
ys_img = [p[1] for p in pairs]
plt.xlim([0, 160])
plt.ylim([0, 210])
plt.plot(xs_img, ys_img, 'r.', alpha=0.5)
plt.savefig(filename)
plt.close()
def setup_networks(self):
if self.f_func == 'nn':
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nnf':
features = Monte()
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
feature=features,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nns':
features = Subset(state_dim=self.obs_dim,
feature_indices=[0, 1]) # TODO: parameterize
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
feature=features,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nnc':
# Convolutions
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
n_units=self.n_units,
conv=True,
name=self.name)
elif self.f_func == 'rand':
self.f_function = None
else:
print('f_func =', self.f_func)
# print('len(ffnc)=', len(self.f_func))
assert (False)
if self.f_function is not None:
self.f_function.initialize()
self.agent = DQNAgent(self.sess,
obs_dim=self.obs_dim,
num_actions=self.num_actions,
gamma=0.99,
name=self.name)
self.agent.reset()
def plot_eigenfunction(op, args, xind=0, yind=1, filename='visualize_ef.pdf'):
# Pinball
mdp, state_dim, state_bound, num_actions, action_dim, action_bound = get_mdp_params(args)
n_samples = 2000
low_bound = state_bound[0]
up_bound = state_bound[1]
if args.task == 'AntMaze-v0' or args.task == 'PointMaze-v0':
low_bound[xind] = 0.0
low_bound[yind] = 0.0
up_bound[xind] = 8.0 * 3.0
up_bound[yind] = 8.0 * 3.0
if args.tasktype == 'atari':
low_bound[xind] = 0.0
low_bound[yind] = 0.0
up_bound[xind] = 160.0
up_bound[yind] = 210.0
xs = []
ys = []
fs = []
# if np.isinf(low_bound).any() or np.isinf(up_bound).any():
# bfr = sample_option_trajectories(mdp, args, noptions=0)
#
# ss, _, _, _, _ = bfr.sample(n_samples)
#
# max_x = float('-inf')
# min_x = float('inf')
# max_y = float('-inf')
# min_y = float('inf')
# for i in range(n_samples):
# x = ss[i].data[xind]
# y = ss[i].data[yind]
# max_x = max(x, max_x)
# min_x = min(x, min_x)
# max_y = max(y, max_y)
# min_y = min(y, min_y)
# low_bound[xind] = min_x
# up_bound[xind] = max_x
# low_bound[yind] = min_y
# up_bound[yind] = max_y
# TODO: Implement a script to plot the f-value of the states
# visited by the agent instead of sampling uniform randomly.
if args.restoretraj:
print('restoring buffer from ' + './vis/' + args.task + 'option' + str(args.noptions - 1) + '_' + str(args.ffuncnunit) + '_' + str(args.rseed) + '/' + 'traj')
bfr = ExperienceBuffer()
bfr.restore(args.basedir + '/vis/' + args.task + 'option' + str(args.noptions - 1) + '_' + str(args.ffuncnunit) + '_' + str(args.rseed) + '/' + 'traj')
bfr_size = bfr.size()
print('bfr_size=', bfr_size) # TODO: parameter?
samples, _, _, _, _ = bfr.sample(n_samples)
# samples = [bfr.buffer[i][0] for i in range(min(bfr.size(), n_samples))]
if args.task == 'MontezumaRevenge-ram-v0':
feature = Monte()
xs = [feature.feature(s, 0)[0] for s in samples]
ys = [feature.feature(s, 0)[1] for s in samples]
elif args.ffunction == 'nns':
feature = Subset(state_dim, [0, 1])
xs = [feature.feature(s, 0)[0] for s in samples]
ys = [feature.feature(s, 0)[1] for s in samples]
else:
xs = [s.data[xind] for s in samples]
ys = [s.data[yind] for s in samples]
else:
xs = [random.uniform(low_bound[xind], up_bound[xind]) for _ in range(n_samples)]
ys = [random.uniform(low_bound[yind], up_bound[yind]) for _ in range(n_samples)]
fs = []
for i in range(len(xs)):
if args.task == 'MontezumaRevenge-ram-v0':
obs = np.array([xs[i], ys[i]])
obs = np.reshape(obs, (1, 2))
f_value = op.f_function.f_from_features(obs)[0][0]
elif args.ffunction == 'nns':
obs = np.array([xs[i], ys[i]])
obs = np.reshape(obs, (1, 2))
f_value = op.f_function.f_from_features(obs)[0][0]
else:
s = mdp.get_init_state()
s.data[xind] = xs[i]
s.data[yind] = ys[i]
f_value = op.f_function(s)[0][0]
fs.append(f_value)
# TODO: What is the best colormap for all people (including color blinds?) but still appealing for majority?
# bwr looks useful, but may be misleading?.
cmap = matplotlib.cm.get_cmap('plasma')
normalize = matplotlib.colors.Normalize(vmin=min(fs), vmax=max(fs))
colors = [cmap(normalize(value)) for value in fs]
# colors_np = np.asarray(colors)
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(x=xs, y=ys, c=colors)
cax, _ = matplotlib.colorbar.make_axes(ax)
cbar = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=normalize)
term_th = op.lower_th
cax.plot([0, 1], [term_th] * 2, 'k')
term, nonterm = 0, 0
for f in fs:
if f < term_th:
term += 1
else:
nonterm += 1
print(term, 'terms', nonterm, 'nonterms')
# TODO: Only for pinball domains. What to do for MuJoCo?
# Obstacles
if args.tasktype == 'pinball':
for obs in mdp.domain.environment.obstacles:
point_list = obs.points
xlist = []
ylist = []
for p in point_list:
xlist.append(p[0])
ylist.append(p[1])
ax.fill(xlist, ylist, 'k')
elif args.task == 'PointMaze-v0' or args.task == 'AntMaze-v0':
# TODO: (x,y) coordinates start at 0, 0.
# How is the coordinates signed?
maze = [[1, 1, 1, 1, 1],
[1, 0, 0, 0, 1],
[1, 1, 1, 0, 1],
[1, 0, 0, 0, 1],
[1, 1, 1, 1, 1]]
scale = 8.0
for y in range(5):
for x in range(5):
if maze[y][x] == 1:
# We decrement x and y because the (0, 0)-coordinate is set at (1, 1) position in the maze.
xbase, ybase = scale * (x - 1), scale * (y - 1)
xlist = [xbase, xbase + scale, xbase + scale, xbase]
ylist = [ybase, ybase, ybase + scale, ybase + scale]
ax.fill(xlist, ylist, 'k')
elif args.task == 'MontezumaRevenge-ram-v0':
# TODO: Show the background of the Monte?
img = imread('./montezuma.jpg')
ax.imshow(img, zorder=0, extent=[0, 160, 0, 210])
plt.savefig(filename)
plt.close()
def plot_trajectory(traj, mdp, args, filename='trajectory.pdf'):
if args.tasktype == 'pinball':
xs = [s.x for s in traj]
ys = [s.y for s in traj]
plt.plot(xs, ys, 'g')
plt.plot(xs[0], ys[0], 'bo')
plt.plot(xs[-1], ys[-1], 'ro')
for obs in mdp.domain.environment.obstacles:
point_list = obs.points
xlist = []
ylist = []
for p in point_list:
xlist.append(p[0])
ylist.append(p[1])
plt.fill(xlist, ylist, 'k')
elif args.task == 'PointMaze-v0' or args.task == 'AntMaze-v0':
xs = [s.data[0] for s in traj]
ys = [s.data[1] for s in traj]
print('x =', min(xs), ' to ', max(xs))
print('y =', min(ys), ' to ', max(ys))
plt.plot(xs, ys, 'g')
plt.plot(xs[0], ys[0], 'bo')
plt.plot(xs[-1], ys[-1], 'ro')
# TODO: (x,y) coordinates start at 0, 0.
# How is the coordinates signed?
maze = [[1, 1, 1, 1, 1], [1, 0, 0, 0, 1], [1, 1, 1, 0, 1],
[1, 0, 0, 0, 1], [1, 1, 1, 1, 1]]
scale = 8.0
for y in range(5):
for x in range(5):
if maze[y][x] == 1:
# We decrement x and y because the (0, 0)-coordinate is set at (1, 1) position in the maze.
xbase, ybase = scale * (x - 1.5), scale * (y - 1.5)
xlist = [xbase, xbase + scale, xbase + scale, xbase]
ylist = [ybase, ybase, ybase + scale, ybase + scale]
plt.fill(xlist, ylist, 'k')
elif args.task == 'MontezumaRevenge-ram-v0':
feature = Monte()
xs = [feature.feature(s, 0)[0] for s in traj]
ys = [feature.feature(s, 0)[1] for s in traj]
# Duplicate detection
# print('initpos=', xs[0], ys[0])
dup = 0
for i in range(1, len(xs)):
# print('pos', i, '=', int(xs[i]), int(ys[i]))
# if int(ys[i]) < 50: # do we have non-suicidal options?
# dup = i
if int(xs[i]) == int(xs[0]) and int(ys[i]) == int(ys[0]):
dup = i
print('dup=', dup)
xs = xs[min(dup + 1, len(xs) - 1):]
ys = ys[min(dup + 1, len(ys) - 1):]
plt.plot(xs, ys, 'g')
plt.plot(xs[0], ys[0], 'bo')
plt.plot(xs[-1], ys[-1], 'ro')
# TODO: Show the background of the Monte?
img = imread('./montezuma.jpg')
plt.imshow(img, zorder=0, extent=[0, 160, 0, 210])
plt.savefig(filename)
plt.close()
def setup_networks(self):
print('f_func=', self.f_func)
if self.f_func == 'fourier':
# low_bound = np.asarray([0.0, 0.0, -2.0, -2.0])
# up_bound = np.asarray([1.0, 1.0, 2.0, 2.0])
features = Fourier(state_dim=self.obs_dim,
bound=self.obs_bound,
order=4)
self.f_function = SpectrumFourier(obs_dim=self.obs_dim,
feature=features,
name=self.name)
elif self.f_func == 'nn':
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nnf':
features = Monte()
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
feature=features,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nns':
features = Subset(state_dim=self.obs_dim,
feature_indices=[0, 1]) # TODO: parameterize
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
feature=features,
n_units=self.n_units,
name=self.name)
elif self.f_func == 'nnc':
# Convolutions
self.f_function = SpectrumNetwork(self.sess,
obs_dim=self.obs_dim,
n_units=self.n_units,
conv=True,
name=self.name)
elif self.f_func == 'rand':
self.f_function = None
elif self.f_func == 'agent':
features = AgentPos(game='Freeway')
self.f_function = SpectrumFourier(obs_dim=self.obs_dim,
feature=features,
name=self.name)
else:
print('f_func =', self.f_func)
# print('len(ffnc)=', len(self.f_func))
assert (False)
if self.f_function is not None:
self.f_function.initialize()
if self.low_method == 'linear':
# low_bound = np.asarray([0.0, 0.0, -2.0, -2.0])
# up_bound = np.asarray([1.0, 1.0, 2.0, 2.0])
features = Fourier(state_dim=self.obs_dim,
bound=self.obs_bound,
order=3)
self.agent = LinearQAgent(actions=range(self.num_actions),
feature=features,
name=self.name)
elif self.low_method == 'ddpg':
# TODO: Using on-policy method is not good for options? is DDPG off-policy?
self.agent = DDPGAgent(self.sess,
obs_dim=self.obs_dim,
action_dim=self.action_dim,
action_bound=self.action_bound,
name=self.name)
elif self.low_method == 'dqn':
self.agent = DQNAgent(self.sess,
obs_dim=self.obs_dim,
num_actions=self.num_actions,
gamma=0.99,
name=self.name)
elif self.low_method == 'rand':
if self.num_actions is None:
self.agent = RandomContAgent(action_dim=self.action_dim,
action_bound=self.action_bound,
name=self.name)
else:
self.agent = RandomAgent(range(self.num_actions),
name=self.name)
else:
print('low_method=', self.low_method)
assert (False)
self.agent.reset()
def plot_op(op, args, mdp, state_bound, filename):
print('visop')
n_samples = 2000
# TODO: Visualize the options according to the direction
# direction = args.reverse
if args.restoretraj:
sample, _, _, _, _ = bfr.sample(n_samples)
if args.task == 'MontezumaRevenge-ram-v0':
feature = Monte()
xs = [feature.feature(s, 0)[0] for s in samples]
ys = [feature.feature(s, 0)[1] for s in samples]
elif args.ffunction == 'nns':
feature = Subset(state_dim, [0, 1])
xs = [feature.feature(s, 0)[0] for s in samples]
ys = [feature.feature(s, 0)[1] for s in samples]
else:
xs = [s.data[0] for s in samples]
ys = [s.data[1] for s in samples]
else:
# TODO: bounds should be implemented inside the tasks.
if 'Ant' in args.task or 'Point' in args.task:
up_bound_x, low_bound_x, up_bound_y, low_bound_y = util.bounds(mdp)
# low_bound_x = - 0.5
# low_bound_y = -4.0
# up_bound_x = -4.0 + 8.0 * 3.0
# up_bound_y = -4.0 + 8.0 * 3.0
elif args.tasktype == 'atari':
low_bound_x = 0.0
low_bound_y = 0.0
up_bound_x = 160.0
up_bound_y = 210.0
else:
low_bound_x = state_bound[0][0]
low_bound_y = state_bound[0][1]
up_bound_x = state_bound[1][0]
up_bound_y = state_bound[1][1]
xs = [
random.uniform(low_bound_x, up_bound_x) for _ in range(n_samples)
]
ys = [
random.uniform(low_bound_y, up_bound_y) for _ in range(n_samples)
]
fs = []
for i in range(len(xs)):
if args.task == 'MontezumaRevenge-ram-v0':
obs = np.array([xs[i], ys[i]])
obs = np.reshape(obs, (1, 2))
f_value = op.f_function.f_from_features(obs)[0][0]
elif args.ffunction == 'nns':
obs = np.array([xs[i], ys[i]])
obs = np.reshape(obs, (1, 2))
f_value = op.f_function.f_from_features(obs)[0][0]
else:
s = mdp.get_init_state()
s.data[0] = xs[i]
s.data[1] = ys[i]
f_value = op.f_function(s)[0][0]
fs.append(f_value)
# TODO: Find the best color mapping for visualization.
# TODO: What is the best thing we can do for color blinds? Intensity of the plot?
# TODO:
# if args.reverse:
cmap = matplotlib.cm.get_cmap('Blues')
# cmap = matplotlib.cm.get_cmap('plasma')
if args.reverse:
term_th = op.upper_th
normalize = matplotlib.colors.Normalize(
vmin=min(fs),
vmax=term_th) # TODO: Does this give us an inverse direction?
colors = []
for value in fs:
if value < term_th:
# colors.append(cmap(1.0 - normalize(value)))
colors.append(cmap(normalize(value)))
else:
# TODO: What is gray rgb?
# colors.append((0.15, 0.15, 0.15))
colors.append((0.15, 0.15, 0.15))
# colors.append((0.0 , 0.0 , 1.0))
else:
term_th = op.lower_th
normalize = matplotlib.colors.Normalize(vmin=term_th, vmax=max(fs))
colors = []
for value in fs:
if value > term_th:
colors.append(cmap(1.0 - normalize(value)))
else:
colors.append((0.15, 0.15, 0.15))
# colors.append((0, 0, 0))
# print('colors=', colors)
# colors_np = np.asarray(colors)
fig, ax = plt.subplots(figsize=(8, 6))
ax.scatter(x=xs, y=ys, c=colors)
cax, _ = matplotlib.colorbar.make_axes(ax)
cbar = matplotlib.colorbar.ColorbarBase(cax, cmap=cmap, norm=normalize)
# cax.plot([0, 1], [term_th] * 2, 'k')
term, nonterm = 0, 0
for f in fs:
if f < term_th:
term += 1
else:
nonterm += 1
print(term, 'terms', nonterm, 'nonterms')
plot_bg(mdp, args, fig, ax)
plt.savefig(filename, bbox_inches='tight', pad_inches=0)
plt.close()
def plot_traj(traj, mdp, args, filename='trajectory'):
fig, ax = plt.subplots(figsize=(8, 6))
if args.tasktype == 'pinball':
xs = [s.x for s in traj]
ys = [s.y for s in traj]
elif 'Point' in args.task or 'Ant' in args.task:
# xs = [s.data[0] for s in traj]
# ys = [s.data[1] for s in traj]
xs = [maze_width(mdp) - s.data[1] for s in traj]
ys = [maze_height(mdp) - s.data[0] for s in traj]
print('x =', min(xs), ' to ', max(xs))
print('y =', min(ys), ' to ', max(ys))
import time
for i in range(0, int(len(xs) / 3) + 1):
# Plot every three states in a trajectory (otherwise it gets unreadable)
mdp.env.wrapped_env.set_xy([xs[i * 3], ys[i * 3]])
mdp.env.wrapped_env.set_ori(traj[i * 3].data[2])
mdp.env.step([0, 0])
time.sleep(0.1)
img = mdp.env.render(mode='rgb_array')
if i < 10:
imsave(args.task + '_0' + str(i) + '.png', img)
else:
imsave(args.task + '_' + str(i) + '.png', img)
elif args.task == 'MontezumaRevenge-ram-v0':
feature = Monte()
xs = [feature.feature(s, 0)[0] for s in traj]
ys = [feature.feature(s, 0)[1] for s in traj]
# Duplicate detection
dup = 0
for i in range(1, len(xs)):
if int(xs[i]) == int(xs[0]) and int(ys[i]) == int(ys[0]):
dup = i
print('dup=', dup)
xs = xs[min(dup + 1, len(xs) - 1):]
ys = ys[min(dup + 1, len(ys) - 1):]
elif args.tasktype == 'atari':
init_s = traj[0]
fig = np.reshape(init_s.data, (105, 80, 3))
# fig = np.reshape(init_s.data, (210, 160, 3))
plt.imshow(fig, vmin=0, vmax=255)
plt.savefig(filename + '_init.pdf', bbox_inches='tight', pad_inches=0)
goal_s = traj[-1]
fig = np.reshape(goal_s.data, (105, 80, 3))
plt.imshow(fig, vmin=0, vmax=255)
plt.savefig(filename + '_goal.pdf', bbox_inches='tight', pad_inches=0)
return
elif args.tasktype == 'atariram':
if args.task == "MsPacman-ram-v0":
target = (210, 164, 74) # Color of the pacman.
elif args.task == "Freeway-ram-v0":
target = (252, 252, 84) # Chicken
else:
for s in traj:
fig = np.asarray(s.data)
shape = fig.shape
colors = set()
pos = None
for x in range(shape[1]):
for y in range(shape[0]):
if tuple(fig[y][x]) not in colors:
colors.add(tuple(fig[y][x]))
pos.append((x, y) + tuple(fig[y][x]))
print('pos=', pos)
assert (False)
xs = []
ys = []
for s in traj:
fig = np.asarray(s.data)
shape = fig.shape
pos = None
for x in range(shape[1]):
for y in range(shape[0]):
if tuple(fig[y][x]) == target:
pos = (x, 210 - y)
break
if pos is not None:
break
if pos is not None:
xs.append(pos[0])
ys.append(pos[1])
else:
print('bg Not implemented')
plot_bg(mdp, args, fig, ax)
plt.plot(xs, ys, 'g')
plt.plot(xs[0], ys[0], 'bo')
plt.plot(xs[-1], ys[-1], 'ro')
plt.savefig(filename + '_' + str(len(traj)) + '.pdf',
bbox_inches='tight',
pad_inches=0)
plt.close()
def plot_vis(traj, args, mdp, filename):
if args.tasktype == 'pinball':
xs = [s.data[0] for s in traj]
ys = [s.data[1] for s in traj]
elif 'Point' in args.task or 'Ant' in args.task:
xs = []
ys = []
up_bound_x, low_bound_x, up_bound_y, low_bound_y = util.bounds(mdp)
for s in traj:
if low_bound_x <= s.data[0] and s.data[0] <= up_bound_x and \
low_bound_y<= s.data[1] and s.data[1] <= up_bound_y:
xs.append(s.data[0])
ys.append(s.data[1])
elif args.task == 'MontezumaRevenge-ram-v0':
feature = Monte()
pairs = [feature.feature(s, 0) for s in traj]
xs = [p[0] for p in pairs]
ys = [p[1] for p in pairs]
plt.xlim([0, 160])
plt.ylim([0, 210])
elif args.tasktype == 'atari':
print('not implemented yet')
assert (False)
elif args.tasktype == 'atariram':
if args.task == 'Freeway-ram-v0' or args.task == 'MsPacman-ram-v0':
if args.task == 'Freeway-ram-v0':
target = (252, 252, 84) # Chicken
elif args.task == 'MsPacman-ram-v0':
target = (210, 164, 74) # Color of the pacman.
else:
assert (False)
xs = []
ys = []
for s in traj:
fig = np.asarray(s.data)
shape = fig.shape
# print('shape=', shape)
pos = None
for x in range(shape[1]):
for y in range(shape[0]):
if tuple(fig[y][x]) == target:
pos = (x, 210 - y)
break
if pos is not None:
break
if pos is not None:
xs.append(pos[0])
ys.append(pos[1])
plt.xlim([0, 160])
plt.ylim([0, 210])
else:
assert (False)
fig, ax = plt.subplots(figsize=(8, 6))
plot_bg(mdp, args, fig, ax)
ax.plot(xs, ys, 'b.', alpha=0.5) # Red is better for visualizing in Monte
# init_s = mdp.domain.s0()[0]
# print('init_s=', init_s,)
# ax.plot(init_s[0], init_s[1], 'bo')
# goal = mdp.domain.environment.target_pos
# ax.plot(goal[0], goal[1], 'rx')
# print('goal=', goal)
plt.savefig(filename + ".pdf", bbox_inches='tight', pad_inches=0)
plt.savefig(filename + ".png", bbox_inches='tight', pad_inches=0)
plt.close()