LOGSF[0]: "No path optimization",
LOGSF[1]: "RRT*-Smart optimization",
LOGSF[2]: "Dijkstra optimization"}
+ COLS={
+ LEG[LOGSF[0]]: "orange",
+ LEG[LOGSF[1]]: "blue",
+ LEG[LOGSF[2]]: "red"}
r={}
for sf in LOGSF:
ax.set_ylabel("Number of paths with given cost [-]")
ax.set_xlabel("Path cost [m]")
ax.set_yscale("log")
- #ax.set_yticklabels([])
+ ax.set_aspect("equal")
for a in r.keys():
- plt.hist(v[a], alpha=0.5, label=a, bins=200)
+ plt.hist(v[a], alpha=0.5, label=a, bins=100, histtype="step",
+ color=COLS[a])
+ X_WHERE = np.percentile(v[a], [95])
+ plt.axvline(X_WHERE, lw=1, color=COLS[a], linestyle="--")
plt.legend()
plt.show()
LOGSF[0]: "RRT* nearest neighbour",
LOGSF[1]: "Nearest neighbour with same heading",
LOGSF[2]: "Nearest neighbour Euclidean distance"}
+ COLS={
+ LEG[LOGSF[0]]: "orange",
+ LEG[LOGSF[1]]: "blue",
+ LEG[LOGSF[2]]: "red"}
r={}
for sf in LOGSF:
ax.set_ylabel("Number of paths found [-]")
ax.set_xlabel("Algorithm elapsed time [s]")
ax.set_yscale("log")
- #ax.set_yticklabels([])
+ ax.set_aspect("equal")
for a in r.keys():
- plt.hist(v[a], alpha=0.5, label=a, bins=np.arange(0, 10, 0.05))
+ plt.hist(v[a], alpha=0.5, label=a, bins=np.arange(0, 10, 0.1),
+ histtype="step", color=COLS[a])
+ X_WHERE = np.percentile(v[a], [95])
+ plt.axvline(X_WHERE, lw=1, color=COLS[a], linestyle="--")
plt.legend()
plt.show()