import scenario
-def boxplot(w={}, t=""):
+def boxplot(w={}, t="", yl=None, lx="Elapsed time [s]", ly=""):
"""Plot boxplot graph.
Keyword arguments:
w -- What to plot.
t -- Graph title.
+ yl -- Y axis limit [min, max].
"""
f, ax = plt.subplots()
- ax.set_title(t)
+
+ if False: # is the y axis log scale?
+ ax.set_title("{} -- {} (log yscale)".format(scenario.SNAME, t))
+ ax.set_yscale("log")
+ else:
+ ax.set_title("{} -- {}".format(scenario.SNAME, t))
+
+ ax.set_xlabel(lx)
+ ax.set_ylabel(ly)
+
ax.boxplot([v for k, v in w.items()], labels=[k for k, v in w.items()])
ax.minorticks_on()
ax.grid(
linewidth="0.5",
color="gray",
)
- plt.xticks(rotation="vertical")
- plt.savefig("out.eps", bbox_inches="tight")
+ if yl:
+ ax.set_ylim(yl)
+ plt.xticks(rotation=45)
+ plt.savefig("out.pdf", bbox_inches="tight")
plt.close()
-def barplot(wl=[], t=""):
+def boxplots(w2=[]):
+ """Plot boxplot for multiple times.
+
+ Keyword arguments:
+ w -- What to plot. It is extracted from `w2`.
+ """
+ f, ax = plt.subplots()
+ eax = ax.twinx()
+
+ [w, no_opt, iterations] = w2
+
+ key = tuple(w.keys())[0]
+ title = ""
+ try:
+ title = (scenario
+ .TITLE
+ .split("/")[-2]
+ .split(".json")[0]
+ .split("sc")[1]
+ .replace("_", "-")
+ )
+ title = "scenario {}".format(title)
+ title = "Final path cost, {}, ({} avg. iters.)".format(
+ title,
+ np.average([i for i in iterations[key].values()]),
+ )
+ except:
+ pass
+ ax.set_title(title)
+ ax.set_xlabel("Number of iterations [-]")
+ ax.set_ylabel("Cost [m]")
+ eax.set_ylabel("Finished with no path [%]")
+
+ M = len(w[key][0])
+ err_hist = [0 for i in range(M)]
+ val_list = [[] for i in range(M)]
+ mi = 999999999999
+ ma = -1
+ N = len(w[key])
+ for i in range(len(w[key])):
+ for j in range(M):
+ if w[key][i][j] == 0:
+ err_hist[j] += 1
+ else:
+ v = w[key][i][j]
+ val_list[j].append(v)
+ if v < mi: mi = v
+ if v > ma: ma = v
+ for i in range(len(err_hist)):
+ err_hist[i] *= 100 / N
+
+ maxes = [max(val_list[i]) if len(val_list[i]) > 0 else 0 for i in range(M)]
+ MAX = max(maxes)
+ mins = [min(val_list[i]) if len(val_list[i]) > 0 else 0 for i in range(M)]
+ MIN = min(mins)
+ EMAX = max(err_hist)
+ EMIN = 0
+
+ ax.set_ylim([0, 100])
+ eax.set_ylim([0, 100])
+
+ average = [np.average(val_list[i]) for i in range(M)]
+ # 95 and 5 are not used
+ perct_95 = [np.percentile(val_list[i], [95])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_5 = [np.percentile(val_list[i], [5])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ # percentiles by 10 %
+ perct_10 = [np.percentile(val_list[i], [10])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_20 = [np.percentile(val_list[i], [20])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_30 = [np.percentile(val_list[i], [30])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_40 = [np.percentile(val_list[i], [40])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_50 = [np.percentile(val_list[i], [50])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_60 = [np.percentile(val_list[i], [60])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_70 = [np.percentile(val_list[i], [70])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_80 = [np.percentile(val_list[i], [80])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+ perct_90 = [np.percentile(val_list[i], [90])[0] if len(val_list[i]) > 0 else 0 for i in range(M)]
+
+ # find index of the closest to average path
+ average_i = [
+ (i, w[key][i][-1])
+ for i in range(len(w[key]))
+ ]
+ average_i = sorted(average_i, key=lambda i: i[1])
+ # this is for figures to the paper -- min/max path cost
+ n0i = 0
+ for i in average_i:
+ if i[1] > 0:
+ n0i = i[0]
+ break
+ print("{} {}".format(n0i, average_i[-1][0]))
+
+ lhs = []
+ # err hist
+ lhs.append(
+ eax.fill_between(
+ range(1, M+1),
+ [i for i in err_hist],
+ [0 for i in range(1, M+1)],
+ color="tab:red",
+ alpha=0.2,
+ label="No path found",
+ )
+ )
+ # plot percentiles
+ lhs.append(
+ ax.fill_between(
+ range(1, M+1),
+ mins,
+ maxes,
+ color="tab:orange",
+ alpha=0.2,
+ label="Minimum and maximum",
+ )
+ )
+ lhs.append(
+ ax.fill_between(
+ range(1, M+1),
+ perct_10,
+ perct_90,
+ color="tab:orange",
+ alpha=0.25,
+ label="10 % and 90 % percentile",
+ )
+ )
+ lhs.append(
+ ax.fill_between(
+ range(1, M+1),
+ perct_20,
+ perct_80,
+ color="tab:orange",
+ alpha=0.3,
+ label="20 % and 80 % percentile",
+ )
+ )
+ lhs.append(
+ ax.fill_between(
+ range(1, M+1),
+ perct_30,
+ perct_70,
+ color="tab:orange",
+ alpha=0.35,
+ label="30 % and 70 % percentile",
+ )
+ )
+ lhs.append(
+ ax.fill_between(
+ range(1, M+1),
+ perct_40,
+ perct_60,
+ color="tab:orange",
+ alpha=0.4,
+ label="40 % and 60 % percentile",
+ )
+ )
+ # plot median and average
+ lhs.append(
+ ax.plot(
+ range(1, M+1),
+ perct_50,
+ color="tab:orange",
+ alpha=1,
+ label="50 % percentile (median)",
+ )
+ )
+ lhs.append(
+ ax.plot(
+ range(1, M+1),
+ average,
+ color="tab:red",
+ alpha=1,
+ label="Average cost",
+ )
+ )
+ # plot average before path optimization
+ #ax.plot(
+ # range(1, M+1),
+ # [np.average([i for i in no_opt[key].values()]) for i in range(1, M+1)],
+ # label="Average cost before optimization",
+ # color="tab:blue",
+ # linestyle="--",
+ #)
+
+ ax.minorticks_on()
+
+ ax.tick_params(axis='x', labelrotation=45)
+ ax.legend(loc="upper left")
+ eax.legend(loc="upper right")
+ plt.savefig("out.pdf", bbox_inches="tight")
+ plt.close()
+
+def barplot(wl=[], t="", yl=None, lx="Entry Point Planner variant [-]", ly=""):
"""Plot barplot graph.
Keyword arguments:
wl -- What to plot list.
t -- Graph title.
+ yl -- Y axis limit [min, max].
"""
if len(wl) < 1:
raise ValueError
wl.append([])
f, ax = plt.subplots()
ax.set_title(t)
+ ax.set_xlabel(lx)
+ ax.set_ylabel(ly)
w = wl[0]
ax.bar(
range(len(w)),
linewidth="0.5",
color="gray",
)
- plt.xticks(rotation="vertical")
- plt.savefig("out.eps", bbox_inches="tight")
+ if yl:
+ ax.set_ylim(yl)
+ plt.xticks(rotation=45)
+ plt.savefig("out.pdf", bbox_inches="tight")
plt.close()
-def histplot(w={}, t=""):
+def histplot(w={}, t="", lx="Algorithm computation time [s]", ly="Number of paths found [-]"):
"""Plot histogram graph.
Keyword arguments:
f, ax = plt.subplots()
ax.set_title("{} (log yscale)".format(t))
ax.set_yscale("log")
+ ax.set_xlim(-10, 500)
+ ax.set_xlabel(lx)
+ ax.set_ylabel(ly)
+
+ COLORS = ["tab:orange", "tab:red", "tab:blue", "tab:green"]
+ i = 0
for ck, cv in w.items():
ax.hist(
[v for k, v in w.items() if k == ck],
#alpha = 0.5,
histtype = "step",
- #bins = 100,
+ #bins = 30,
+ color=COLORS[i]
)
X_WHERE = np.percentile([v for k, v in w.items() if k == ck], [95])
- plt.axvline(X_WHERE, lw=1, linestyle="--")
+ print("percentile is {}".format(X_WHERE))
+ plt.axvline(X_WHERE, lw=1, linestyle="--", color=COLORS[i])
+ i += 1
ax.minorticks_on()
ax.grid(
color="gray",
)
plt.legend([k for k, v in w.items()])
- plt.savefig("out.eps", bbox_inches="tight")
+ plt.xticks(rotation=45)
+ plt.savefig("out.pdf", bbox_inches="tight")
plt.close()
if __name__ == "__main__":
w = sys.argv[1]
else:
w = "time"
+ if len(sys.argv) > 2:
+ scenario.DNAME = sys.argv[2]
+ plt.rcParams["figure.figsize"] = [12, 12]
plt.rcParams["font.size"] = 24
+ plt.rcParams["font.family"] = "cmr10"
+ plt.rcParams["hatch.linewidth"] = 1.0
+ plt.rcParams["lines.linewidth"] = 1.0
+ plt.rc('axes', unicode_minus=False)
if w == "time":
- boxplot(scenario.time(), "Elapsed time")
+ boxplot(scenario.time(), "Elapsed time", yl=[0.0004, 200], ly="Time [s]")
elif w == "htime":
- histplot(scenario.time(), "Elapsed time histogram")
+ histplot(scenario.time(), "Histogram of time to find a path")
elif w == "cost":
- boxplot(scenario.cost(), "Final path cost")
+ boxplot(scenario.cost(), "Final path cost", yl=[0, 80], ly="Cost [m]")
+ elif w == "costs":
+ boxplots(scenario.costs())
elif w == "hcost":
histplot(scenario.cost(), "Final path cost histogram")
+ elif w == "orig_cost":
+ boxplot(scenario.orig_cost(), "Original path cost", yl=[0, 80], ly="Cost [m]")
+ elif w == "orig_hcost":
+ histplot(scenario.orig_cost(), "Original path cost histogram")
elif w == "cusp":
- boxplot(scenario.cusp(), "Changes in direction")
+ boxplot(scenario.cusp(), "Changes in direction", ly="Changes [-]")
elif w == "hcusp":
histplot(scenario.cusp(), "Changes in direction histogram")
elif w == "error":
- barplot(scenario.error_rate(), "Error rate")
+ barplot(
+ scenario.error_rate(),
+ "Path not found rate",
+ ly="Path not found [%]",
+ )
elif w == "iter":
- boxplot(scenario.iter(), "Number of iterations")
+ boxplot(
+ scenario.iter(),
+ "Number of iterations",
+ yl=[1, 1000],
+ ly="Iterations [-]",
+ )
else:
print("""The following arguments are allowed:
projects / hubacji1 / iamcar2.git / blobdiff