<gh_stars>0
import matplotlib.pyplot as plt
import numpy as np
from statistics import mean, variance, stdev, pstdev

t = np.arange(10., 110., 10)

tab10 = [0.04, 0.03, 0.09, 0.22, 0.06, 0.24, 0.03, 0.12, 0.13, 0.09]
tab20 = [0.13, 0.13, 0.08, 0.18, 0.10, 0.13, 0.39, 0.11, 0.19, 0.15]
tab30 = [0.36, 0.18, 0.28, 0.23, 0.23, 0.25, 0.21, 0.26, 0.44, 0.48]
tab40 = [1.04, 1.10, 0.64, 0.49, 0.82, 0.60, 0.73, 0.56, 0.53, 1.11]
tab50 = [1.22, 2.04, 1.30, 5.11, 1.35, 1.85, 1.94, 1.05, 1.22, 1.12]
tab60 = [7.66, 2.32, 2.33, 4.08, 2.02, 4.99, 5.48, 1.67, 1.40, 5.31]
tab70 = [4.24, 7.18, 4.66, 3.76, 4.26, 3.49, 4.56, 1.91, 13.40, 5.93]
tab80 = [9.51, 9.49, 0.80, 4.35, 9.03, 5.65, 4.63, 3.96, 11.08, 12.64]
tab90 = [6.10, 14.39, 11.08, 7.88, 25.36, 8.41, 7.63, 1.62, 12.73, 7.10]
tab100 = [18.67, 12.99, 32.13, 15.06, 20.40, 9.33, 2.33, 15.31, 10.11, 20.43]

data_to_plot = [tab10,tab20,tab30,tab40,tab50,tab60,tab70,tab80,tab90,tab100]
h = [mean(tab10),mean(tab20),mean(tab30),mean(tab40),mean(tab50),mean(tab60),mean(tab70),mean(tab80),mean(tab90),mean(tab100)]


fig, ax = plt.subplots()
plt.plot(t,h, "o:", markersize=4)
plt.yscale("log")
plt.title("Temps moyen de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps moyen de  calcul (s)")
plt.show()


fig, ax = plt.subplots()
plt.plot(t,h, "o:", markersize=4)
plt.title("Temps moyen de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps moyen de  calcul (s)")
plt.show()

fig, ax = plt.subplots()
bp = ax.boxplot(data_to_plot)
plt.title("Répartition des temps de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps de calcul (s)")
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from statistics import mean, variance, stdev, pstdev

t = np.arange(10., 110., 10)

tab10 = [0.04, 0.03, 0.09, 0.22, 0.06, 0.24, 0.03, 0.12, 0.13, 0.09]
tab20 = [0.13, 0.13, 0.08, 0.18, 0.10, 0.13, 0.39, 0.11, 0.19, 0.15]
tab30 = [0.36, 0.18, 0.28, 0.23, 0.23, 0.25, 0.21, 0.26, 0.44, 0.48]
tab40 = [1.04, 1.10, 0.64, 0.49, 0.82, 0.60, 0.73, 0.56, 0.53, 1.11]
tab50 = [1.22, 2.04, 1.30, 5.11, 1.35, 1.85, 1.94, 1.05, 1.22, 1.12]
tab60 = [7.66, 2.32, 2.33, 4.08, 2.02, 4.99, 5.48, 1.67, 1.40, 5.31]
tab70 = [4.24, 7.18, 4.66, 3.76, 4.26, 3.49, 4.56, 1.91, 13.40, 5.93]
tab80 = [9.51, 9.49, 0.80, 4.35, 9.03, 5.65, 4.63, 3.96, 11.08, 12.64]
tab90 = [6.10, 14.39, 11.08, 7.88, 25.36, 8.41, 7.63, 1.62, 12.73, 7.10]
tab100 = [18.67, 12.99, 32.13, 15.06, 20.40, 9.33, 2.33, 15.31, 10.11, 20.43]

data_to_plot = [tab10,tab20,tab30,tab40,tab50,tab60,tab70,tab80,tab90,tab100]
h = [mean(tab10),mean(tab20),mean(tab30),mean(tab40),mean(tab50),mean(tab60),mean(tab70),mean(tab80),mean(tab90),mean(tab100)]


fig, ax = plt.subplots()
plt.plot(t,h, "o:", markersize=4)
plt.yscale("log")
plt.title("Temps moyen de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps moyen de  calcul (s)")
plt.show()


fig, ax = plt.subplots()
plt.plot(t,h, "o:", markersize=4)
plt.title("Temps moyen de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps moyen de  calcul (s)")
plt.show()

fig, ax = plt.subplots()
bp = ax.boxplot(data_to_plot)
plt.title("Répartition des temps de calcul (s) en fonction de la taille de l'instance")
plt.xlabel("Taille de l'instance")
plt.ylabel("Temps de calcul (s)")
plt.show()