from os import listdir
from sys import argv, exit
import numpy as np
+import scipy.stats as ss
# ed - euclidean distance
# rs - reeds and shepp path length
LOGF = "log_wo"
LOG = [
- {"f": "rs", "c": "orange", "l": "Reeds and Shepp path length"},
- {"f": "sh", "c": "blue", "l": "Reeds and Shepp same heading"},
- {"f": "ed", "c": "red", "l": "Euclidean distance"},
-# {"f": "ed", "c": "orange", "l": "No optimization"},
-# {"f": "ar", "c": "green", "l": "Remove redundant points"},
-# {"f": "as", "c": "blue", "l": "Smart"},
-# {"f": "cd", "c": "red", "l": "Dijkstra on cusp nodes"},
+ {"f": "T2", "c": "orange", "l": "T2"},
]
r = {}
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 get_val_if_exist(trajectories, what):
+ """From m ultiple trajectories get value.
+
+ 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."""
v = {}
if __name__ == "__main__":
plt.rcParams["font.size"] = 29
- r = {}
- for sf in [i["f"] for i in LOG]:
- r[sf] = load_trajectories("{}/{}".format(LOGF, sf))
- print_successrate()
- plot_maxtime()
- plot_costdist()
+ res = []
+ LOGF = "log-slotplanner"
+ for d in listdir(LOGF):
+ r = {}
+ for sf in [i["f"] for i in LOG]:
+ r[sf] = load_trajectories("{}/{}/{}".format(LOGF, d, sf))
+ res.append({
+ "f": d,
+ "elap": get_val_if_exist(r["T2"], "elap"),
+ "rrte": get_val_if_exist(r["T2"], "rrte"),
+ "ppse": get_val_if_exist(r["T2"], "ppse"),
+ "succ": (
+ count_if_exist(r["T2"], "traj") /
+ count_if_exist(r["T2"], "elap")
+ ),
+ })
+ res2 = []
+ LOGF = "log-rrt"
+ for d in listdir(LOGF):
+ r = {}
+ for sf in [i["f"] for i in LOG]:
+ r[sf] = load_trajectories("{}/{}/{}".format(LOGF, d, sf))
+ res2.append({
+ "f": d,
+ "elap": get_val_if_exist(r["T2"], "elap"),
+ "rrte": get_val_if_exist(r["T2"], "rrte"),
+ "ppse": get_val_if_exist(r["T2"], "ppse"),
+ "succ": (
+ count_if_exist(r["T2"], "traj") /
+ count_if_exist(r["T2"], "elap")
+ ),
+ })
+
+ fig = plt.figure()
+ # For color scheme
+ # see https://github.com/vega/vega/wiki/Scales#scale-range-literals
+ ax = fig.add_subplot(111)
+ ax.set_title("""Elapsed time for different lengths
+ of parallel parking slot""")
+
+ ax.set_ylabel("Time [s]")
+ ax.set_xlabel("Parking slot length [m]")
+
+ # res Slot Planner
+ coord = [float(r["f"].split("_")[1]) for r in res]
+
+ #val = [sum(r["ppse"])/len(r["ppse"]) for r in res]
+ #fin = [(x, y) for (x, y) in zip(coord, val)]
+ #fin.sort()
+ #plt.plot(
+ # [x for (x, y) in fin],
+ # [y for (x, y) in fin],
+ # color = "g",
+ # label = "Slot Planner",
+ #)
+
+ val = [max(r["elap"]) for r in res]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ color = "#e6550d",
+ linestyle = "--",
+ label = "Elapsed worst",
+ )
+
+ #val = [sum(r["rrte"])/len(r["rrte"]) for r in res]
+ #fin = [(x, y) for (x, y) in zip(coord, val)]
+ #fin.sort()
+ #plt.plot(
+ # [x for (x, y) in fin],
+ # [y for (x, y) in fin],
+ # color = "#fd8d3c",
+ # label = "RRT",
+ #)
+
+ val = [sum(r["elap"])/len(r["elap"]) for r in res]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ color = "#e6550d",
+ label = "Elapsed average",
+ )
+
+ val = [r["succ"] for r in res]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ #color = "#fd8d3c",
+ color = "#fdae6b",
+ label = "Success rate",
+ )
+
+ # res2 RRT
+ coord = [float(r["f"].split("_")[1]) for r in res2]
+
+ val = [max(r["elap"]) for r in res2]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ color = "#3182bd",
+ linestyle = "--",
+ label = "Elapsed worst",
+ )
+
+ val = [sum(r["elap"])/len(r["elap"]) for r in res2]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ color = "#3182bd",
+ label = "Elapsed average",
+ )
+
+ val = [r["succ"] for r in res2]
+ fin = [(x, y) for (x, y) in zip(coord, val)]
+ fin.sort()
+ plt.plot(
+ [x for (x, y) in fin],
+ [y for (x, y) in fin],
+ #color = "#6baed6",
+ color = "#9ecae1",
+ label = "Success rate",
+ )
+
+ plt.legend(bbox_to_anchor=(1, 1), loc=1, borderaxespad=0)
+ plt.show()
projects / hubacji1 / iamcar.git / blobdiff