def main(args):
ttc_logfile = load_log_csv(args.ttc_log_path)
freefall_logfile = load_log_csv(args.freefall_log_path)
ttc_logfile['legend'] = 'TTC'
freefall_logfile['legend'] = 'Freefall'
ax = plot_distance(pd.concat([ttc_logfile, freefall_logfile]),
hue='legend')
plt.show()
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Oracle"]
labels = ["Estimated TTC Control", "Oracle TTC Control"]
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, axes = plt.subplots(1, 2, figsize=(11, 4.8))
for logfile, label in zip(logfiles, labels):
for i, ax in enumerate(axes):
ax.set_xlabel("Timestep", fontsize=16)
if i == 0:
sns.lineplot(x='Timestep', y='distance', data=logfile, ax=ax, label=label)
ax.set_ylabel("Distance", fontsize=16)
ax.yaxis.set_label_coords(-0.08, 0.5)
else:
logfile['actual_velocity_y'] *= -1
sns.lineplot(x='Timestep', y='actual_velocity_y', data=logfile, ax=ax, label=label)
ax.set_ylabel("Vertical Velocity", fontsize=16)
ax.set_ylim(0, 2)
ax.yaxis.set_label_coords(-0.12, 0.5)
ax.xaxis.set_label_coords(0.5, -0.12)
ax.legend(prop={"size": 14})
ax.tick_params(axis='both', which='major', labelsize=12)
plt.tight_layout()
fig.subplots_adjust(left=0.057, wspace=0.193)
plt.show(block=False)
fig.savefig(os.path.join(root_dir, "compare_oracle.png"))
def main(args):
Exs = load_pickle(os.path.join(args.logdir, 'Exs.npy'))
Eys = load_pickle(os.path.join(args.logdir, 'Eys.npy'))
Ets = load_pickle(os.path.join(args.logdir, 'Ets.npy'))
logfile = load_log_csv(os.path.join(args.logdir, 'log.csv'))
# Plot distance
fig_distance, ax_distance = plt.subplots()
plot_distance(logfile, ax=ax_distance)
fig_distance.savefig(os.path.join(args.logdir, 'distance.jpg'))
# Plot velocity
fig_vel, ax_vel = plt.subplots()
plot_vel(logfile, ax=ax_vel)
fig_vel.savefig(os.path.join(args.logdir, 'actual_vel.jpg'))
# Plot desired velocity
fig_desired_vel, ax_desired_vel = plt.subplots()
plot_desired_vel(logfile, ax=ax_desired_vel)
fig_desired_vel.savefig(os.path.join(args.logdir,
'actual_desired_vel.jpg'))
# Plot ttc
fig_ttc, ax_ttc = plt.subplots()
plot_ttc(logfile, ax=ax_ttc, ylim=(0, 1000))
fig_ttc.savefig(os.path.join(args.logdir, 'ttc.jpg'))
# Plot derivatives
if args.derivative_gif:
plot_derivative_animation(Exs,
Eys,
Ets,
output_path=os.path.join(
args.logdir, 'derivatives.gif'))
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Dust", "Cloudy", "Windy", "RotationalLight"]
labels = ["Basic", "Duststorm", "Cloudy", "Windy", "Rotational Light"]
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, ax = plt.subplots()
for i, (logfile, label) in enumerate(zip(logfiles, labels)):
logfile['actual_velocity_y'] *= -1
sns.lineplot(x='Timestep', y='actual_velocity_y', data=logfile, ax=ax, label=label, zorder=len(labels)-i)
ax.set_yscale("log")
#ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_xlabel("Timestep", fontsize=16)
ax.set_ylabel("Vertical Velocity (log-scale)", fontsize=16)
ax.xaxis.set_label_coords(0.5, -0.12)
ax.yaxis.set_label_coords(-0.08, 0.5)
ax.legend(prop={"size": 14})
ax.tick_params(axis='both', which='major', labelsize=12)
plt.tight_layout()
plt.show(block=False)
fig.savefig(os.path.join(root_dir, "compare_velocity.pdf"))
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Dust", "Cloudy", "Windy", "RotationalLight"]
labels = ["Basic", "Duststorm", "Cloudy", "Windy", "Rotational Light"]
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, axes = plt.subplots(1, 2, figsize=(11, 4.8))
for i, (logfile, label) in enumerate(zip(logfiles, labels)):
for j, ax in enumerate(axes):
if j == 0:
sns.lineplot(x='Timestep',
y='distance',
data=logfile,
ax=ax,
label=label)
else:
logfile['actual_velocity_y'] *= -1
sns.lineplot(x='Timestep',
y='actual_velocity_y',
data=logfile,
ax=ax,
label=label,
zorder=len(labels) - i)
for i, ax in enumerate(axes):
if i == 0:
ax.set_ylabel("Distance", fontsize=16)
ax.set_xlabel("Timestep", fontsize=16)
ax.legend(prop={"size": 14})
ax.yaxis.set_label_coords(-0.08, 0.5)
else:
ax.set_yscale("log")
ax.set_xlabel("Timestep", fontsize=16)
ax.set_ylabel("Vertical Velocity (log-scale)", fontsize=16)
ax.yaxis.set_label_coords(-0.12, 0.5)
ax.get_legend().remove()
ax.tick_params(axis='both', which='major', labelsize=12)
ax.xaxis.set_label_coords(0.5, -0.12)
plt.tight_layout()
plt.show(block=False)
fig.savefig(os.path.join(root_dir, "compare_distance_and_velocity.pdf"))
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Dust", "Cloudy", "Windy", "RotationalLight"]
labels = ["Basic", "Duststorm", "Cloudy", "Windy", "Rotational Light"]
running_window_size = 50
time_interval = 50
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, axes = plt.subplots(1, 5, figsize=(20,4), sharey=True)
running_window_low = running_window_size // 2
running_window_high = running_window_size - running_window_low
for i, (logfile, label, ax) in enumerate(zip(logfiles, labels, axes)):
ttc = logfile['TTC'].to_numpy() / time_interval
N = len(logfile['Timestep'])
running_mean, running_std = [], []
for n in range(N):
low = max(0, n-running_window_low)
high = min(N, n+running_window_high)
running_mean.append(np.mean(ttc[low:high]))
running_std.append(np.std(ttc[low:high]))
running_mean, running_std = np.array(running_mean), np.array(running_std)
gt_ttc = logfile['distance'].to_numpy() / -logfile['actual_velocity_y'].to_numpy()
ax.plot(logfile['Timestep'], gt_ttc, color='gray', label='Ground Truth')
ax.plot(logfile['Timestep'], running_mean, color=sns.color_palette()[i])
ax.fill_between(logfile['Timestep'], running_mean - running_std, running_mean + running_std,
alpha=.3, color=sns.color_palette()[i])
ax.set_title(label, fontsize=16)
ax.set_xlabel("Timestep", fontsize=16)
if i == 0:
ax.set_ylabel("TTC", fontsize=16)
ax.yaxis.set_label_coords(-0.12, 0.5)
ax.legend(prop={"size": 12})
ax.xaxis.set_label_coords(0.5, -0.12)
ax.tick_params(axis='both', which='major', labelsize=12)
ax.set_ylim(0, 30)
plt.tight_layout()
fig.subplots_adjust(left=0.043)
plt.show(block=False)
fig.savefig(os.path.join(root_dir, "compare_ttc.pdf"))
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Dust", "Cloudy", "Windy", "RotationalLight"]
labels = ["Basic", "Duststorm", "Cloudy", "Windy", "Rotational Light"]
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, ax = plt.subplots()
for logfile, label in zip(logfiles, labels):
sns.lineplot(x='Timestep', y='distance', data=logfile, ax=ax, label=label)
ax.set_xlabel("Timestep", fontsize=16)
ax.set_ylabel("Distance", fontsize=16)
ax.xaxis.set_label_coords(0.5, -0.12)
ax.yaxis.set_label_coords(-0.08, 0.5)
ax.legend(prop={"size": 14})
ax.tick_params(axis='both', which='major', labelsize=12)
plt.tight_layout()
plt.show(block=False)
fig.savefig(os.path.join(root_dir, "compare_distance.pdf"))
def main():
root_dir = "../results_ver2"
variants = ["Basic", "Dust", "Cloudy", "Windy", "RotationalLight"]
labels = ["Basic", "Duststorm", "Cloudy", "Windy", "Rotational Light"]
avg_window_size = 20
dt = 1. / 50
logfiles = []
for var in variants:
fpath = os.path.join(root_dir, var, "log.csv")
logfiles.append(load_log_csv(fpath))
fig, ax = plt.subplots()
for logfile, label in zip(logfiles, labels):
N = len(logfile['Timestep']) - 1
reach_time = logfile['Timestep'][N]
velo = logfile['actual_velocity_y'][N - avg_window_size -
1:N].to_numpy()
acc = (velo[1:] - velo[:-1]) / dt
#acc_mean, acc_std = acc.mean(), acc.std()
#ax.scatter(reach_time, acc_mean, s=np.sqrt(acc_std*1E5), label=label)
velo_mean, velo_std = velo.mean(), velo.std()
ax.scatter(reach_time,
velo_mean,
s=np.sqrt(velo_std * 1E5),
label=label)
ax.set_xlabel("Landing Time", fontsize=16)
ax.set_ylabel("Landing Strength", fontsize=16)
ax.xaxis.set_label_coords(0.5, -0.12)
ax.yaxis.set_label_coords(-0.08, 0.5)
ax.legend(prop={"size": 14})
ax.tick_params(axis='both', which='major', labelsize=12)
plt.tight_layout()
plt.show(block=False)
import pdb
pdb.set_trace()
def main(args):
logfile = load_log_csv(args.log_path)
ax = plot_vel(logfile)
plt.show()
def main(args):
logfile = load_log_csv(args.log_path)
ax = plot_ttc(logfile, ylim=(0, 200))
plt.show()