from os import listdir
from sys import argv, exit
import numpy as np
+import scipy.stats as ss
-LOGF="log"
-LOGSF=["T2","T3", "Klemm2015"]
-LEG={
- LOGSF[0]: "T2",
- LOGSF[1]: "T3",
- LOGSF[2]: "Klemm2015"}
-COLS={
- LEG[LOGSF[0]]: "orange",
- LEG[LOGSF[1]]: "red",
- LEG[LOGSF[2]]: "blue"}
+# ed - euclidean distance
+# rs - reeds and shepp path length
+# sh - reeds and shepp, same heading
+
+# ad - optimize with dijkstra, all nodes
+# as - optimize with smart, all nodes
+# ar - optimize with remove redundant points, all nodes
+# cd - optimize with dijkstra, cusp nodes
+# cs - optimize with smart, cusp nodes
+# cr - optimize with remove redundant points, cusp nodes
+
+LOGF = "log_wo"
+LOG = [
+ {"f": "T2", "c": "orange", "l": "T2"},
+]
+
+r = {}
def load_trajectory(fname):
"""Load trajectory from file.
return trajectories
def gplot():
- """Plot graph."""
+ """Plot graph. Template."""
plt.rcParams["font.size"] = 24
fig = plt.figure()
ax = fig.add_subplot(111)
plt.show()
#plt.savefig("WHATEVER")
+def mean_conf_int(data, conf=0.95):
+ """Return (mean, lower, uppper) of data.
+
+ see https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data
+
+ Keyword arguments:
+ data -- A list of data.
+ conf -- Confidence interval.
+ """
+ a = np.array(data)
+ n = len(a)
+ m = np.mean(a)
+ se = ss.sem(a)
+ h = se * ss.t.ppf((1 + conf) / 2, n - 1)
+ return (m, m - h, m + h)
+
def count_if_exist(trajectories, what):
"""From multiple trajectories compute the number of occurences.
pass
return val
-def plot_successrate():
- """Plot success rate of single/multi-core implementations."""
-
- r={}
- for sf in LOGSF:
- r["{}".format(LEG[sf])] = load_trajectories("{}/{}".format(LOGF, sf))
-
- v={}
- for a in r.keys():
- v[a] = (100 * count_if_exist(r[a], "traj") /
- count_if_exist(r[a], "elap"))
- vs = sorted(v.items(), key=lambda kv: kv[1])
-
- plt.rcParams["font.size"] = 24
- fig = plt.figure()
- ax = fig.add_subplot(111)
- ax.set_title("Success rate of trajectories found within 10 seconds limit")
-
- ax.set_ylabel("Framework tested [-]")
- ax.set_xlabel("Found successfully [%]")
- ax.set_xlim(0, 100)
- ax.set_yticklabels([])
- j = 0
- for (k, i) in vs:
- print("{}: {}%".format(k, i))
- ax.barh(j, float(i), 4, label=k)
- j += -5
- ax.legend()
-
- plt.show()
-
-def plot_mintrajcost():
- """Plot minimum trajectory cost found."""
-
- r={}
- for a in ALG:
- for st in ST:
- for co in CO:
- r["{}, {}, {}".format(ALGT[a], STT[st], COT[co])] = (
- load_trajectories("{}/{}st{}co{}_lpar".format(
- LOGF, a, st, co)))
-
- v={}
- for a in r.keys():
- v[a] = get_lasts_if_exist(r[a], "cost")
-
- #plt.rcParams["font.size"] = 24
- fig = plt.figure()
- for i in range(len(r.keys())):
- ax = fig.add_subplot(3, 1, i + 1)
- ax.set_title("Minimum trajectory cost found in 10 seconds (sequential)")
-
- ax.set_ylabel("Framework tested [-]")
- ax.set_xlabel("Minimum trajectory cost [m]")
- #ax.set_yticklabels([])
+def get_val_if_exist(trajectories, what):
+ """From m ultiple trajectories get value.
- plt.bar(range(len(v[r.keys()[i]])), v[r.keys()[i]], label=r.keys()[i])
-
- ax.legend()
- plt.show()
- return
+ Keyword arguments:
+ trajectories -- The list of trajectories.
+ what -- What to take.
+ """
+ val = []
+ for t in trajectories:
+ try:
+ val.append(t[what])
+ except:
+ pass
+ return val
def plot_costdist():
"""Plot distribution of last costs across measured tests."""
-
- r={}
- for sf in LOGSF:
- r["{}".format(LEG[sf])] = load_trajectories("{}/{}".format(LOGF, sf))
-
- v={}
+ v = {}
for a in r.keys():
v[a] = get_lasts_if_exist(r[a], "cost")
- plt.rcParams["font.size"] = 24
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title("Path cost histogram")
ax.set_ylabel("Number of paths with given cost [-]")
ax.set_xlabel("Path cost [m]")
ax.set_yscale("log")
- ax.set_aspect("equal")
- for a in r.keys():
- plt.hist(v[a], alpha=0.5, label=a, bins=100, histtype="step",
- color=COLS[a])
+ for a in LOG:
+ plt.hist(
+ v[a["f"]],
+ alpha = 0.5,
+ label = a["l"],
+ bins = 100,
+ histtype = "step",
+ color = a["c"])
try:
- X_WHERE = np.percentile(v[a], [95])
- plt.axvline(X_WHERE, lw=1, color=COLS[a], linestyle="--")
+ X_WHERE = np.percentile(v[a["f"]], [95])
+ plt.axvline(X_WHERE, lw=1, color=a["c"], linestyle="--")
except:
pass
def plot_maxtime():
"""Plot time of the last traj (the maximum time)."""
-
- r={}
- for sf in LOGSF:
- r["{}".format(LEG[sf])] = load_trajectories("{}/{}".format(LOGF, sf))
-
- v={}
+ v = {}
for a in r.keys():
v[a] = get_lasts_if_exist(r[a], "secs")
- plt.rcParams["font.size"] = 24
fig = plt.figure()
ax = fig.add_subplot(111)
- ax.set_title("Path found time histogram")
+ ax.set_title("Histogram of time to find the path")
ax.set_ylabel("Number of paths found [-]")
- ax.set_xlabel("Algorithm elapsed time [s]")
+ ax.set_xlabel("Algorithm computation time [s]")
ax.set_yscale("log")
- ax.set_aspect("equal")
+ for a in LOG:
+ plt.hist(
+ v[a["f"]],
+ alpha = 0.5,
+ label = a["l"],
+ bins = np.arange(0, 10, 0.1),
+ histtype = "step",
+ color = a["c"])
+ try:
+ X_WHERE = np.percentile(v[a["f"]], [95])
+ plt.axvline(X_WHERE, lw=1, color=a["c"], linestyle="--")
+ except:
+ pass
+
+ plt.legend()
+ plt.show()
+
+def plot_nothingdone():
+ """Plot *nothing done* time of ``overlaptrees`` procedure."""
+ v = {}
for a in r.keys():
- plt.hist(v[a], alpha=0.5, label=a, bins=np.arange(0, 10, 0.1),
- histtype="step", color=COLS[a])
+ v[a] = get_lasts_if_exist(r[a], "nodo")
+
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ ax.set_title("Histogram of nothing-done-time")
+
+ ax.set_ylabel("Occurences [-]")
+ ax.set_xlabel("Nothing-done-time percentage [-]")
+
+ for a in LOG:
+ plt.hist(
+ v[a["f"]],
+ alpha = 0.5,
+ label = a["l"],
+ bins = np.arange(0, 1, 0.1),
+ histtype = "step",
+ color = a["c"])
try:
- X_WHERE = np.percentile(v[a], [95])
- plt.axvline(X_WHERE, lw=1, color=COLS[a], linestyle="--")
+ X_WHERE = np.percentile(v[a["f"]], [95])
+ plt.axvline(X_WHERE, lw=1, color=a["c"], linestyle="--")
except:
pass
def print_nofnodes():
"""Print average number of nodes."""
-
- r={}
- for sf in LOGSF:
- r["{}".format(LEG[sf])] = load_trajectories("{}/{}".format(LOGF, sf))
-
v={}
for a in r.keys():
lasts = get_lasts_if_exist(r[a], "node")
v[a] = np.average(lasts)
- print("Average number of points:")
- for a in r.keys():
- print("{}: {}".format(a, v[a]))
+ print("Average number of nodes:")
+ for a in LOG:
+ print("{}: {}".format(a["f"], v[a["f"]]))
def print_successrate():
"""Print success rate of implementations."""
- r={}
- for sf in LOGSF:
- r["{}".format(LEG[sf])] = load_trajectories("{}/{}".format(LOGF, sf))
-
v={}
for a in r.keys():
v[a] = (100.0 * count_if_exist(r[a], "traj") /
count_if_exist(r[a], "elap"))
print("Success rate:")
- for a in r.keys():
- print("{}: {}".format(a, v[a]))
+ for a in LOG:
+ print("{}: {}".format(a["f"], v[a["f"]]))
if __name__ == "__main__":
+ r = {}
+ for sf in [i["f"] for i in LOG]:
+ r[sf] = load_trajectories("{}/{}".format(LOGF, sf))
+ print_successrate()
+ plot_maxtime()
plot_costdist()
projects / hubacji1 / iamcar.git / blobdiff