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import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull


xdata = np.array([1.50000000e-01,   2.90000000e-01,   4.20000000e-01,   5.10000000e-01,
                  5.80000000e-01,   8.00000000e-01,   1.03000000e+00,   1.40000000e+00,
                  1.59000000e+00,   1.60000000e+00,   2.05000000e+00,   2.40000000e+00,
                  2.70000000e+00,   3.20000000e+00,   3.80000000e+00,   4.10000000e+00,
                  4.90000000e+00,   4.90000000e+00,   5.30000000e+00,   7.50000000e+00,
                  7.60000000e+00,   9.10000000e+00,   9.80000000e+00,   1.00000000e+01,
                  1.06000000e+01,   1.09000000e+01,   1.33000000e+01,   1.42000000e+01,
                  1.42000000e+01,   1.48000000e+01,   1.54000000e+01,   1.55000000e+01,
                  1.78000000e+01,   1.99000000e+01,   1.99000000e+01,   2.16000000e+01,
                  2.20000000e+01,   2.38000000e+01,   2.58000000e+01,   2.60000000e+01,
                  3.00000000e+01,   3.09000000e+01,   3.10000000e+01,   3.48000000e+01,
                  3.48000000e+01,   3.55000000e+01,   4.09000000e+01,   4.39000000e+01,
                  4.77000000e+01,   4.79000000e+01,   5.20000000e+01,   6.00000000e+01,
                  6.20000000e+01,   6.60000000e+01,   6.70000000e+01,   6.90000000e+01,
                  7.80000000e+01,   8.30000000e+01,   9.00000000e+01,   9.00000000e+01,
                  1.04000000e+02,   1.19000000e+02,   1.21000000e+02,   1.32000000e+02,
                  1.36000000e+02,   1.39000000e+02,   1.39000000e+02,   1.56000000e+02,
                  1.58000000e+02,   1.65000000e+02,   1.67000000e+02,   1.80000000e+02,
                  1.90000000e+02,   1.97000000e+02,   2.09000000e+02,   2.25000000e+02,
                  2.40000000e+02,   2.50000000e+02,   2.52000000e+02,   2.57000000e+02,
                  2.63000000e+02,   2.78000000e+02,   2.92000000e+02,   3.13000000e+02,
                  3.19000000e+02,   3.29000000e+02,   3.80000000e+02,   3.90000000e+02,
                  3.96000000e+02,   3.98000000e+02,   4.17000000e+02,   4.61000000e+02,
                  4.81000000e+02,   4.96000000e+02,   5.04000000e+02,   5.27000000e+02,
                  5.48000000e+02,   5.55000000e+02,   5.57000000e+02,   5.69000000e+02,
                  5.77000000e+02,   6.04000000e+02,   6.34000000e+02,   6.41000000e+02,
                  6.93000000e+02,   7.10000000e+02,   7.67000000e+02,   8.30000000e+02,
                  8.78000000e+02,   9.68000000e+02,   9.73000000e+02,   1.00700000e+03,
                  1.09700000e+03,   1.11000000e+03,   1.15500000e+03,   1.20800000e+03,
                  1.23600000e+03,   1.36600000e+03,   1.42100000e+03,   1.49000000e+03,
                  1.52300000e+03,   1.64800000e+03,   1.76100000e+03,   1.76100000e+03,
                  2.00500000e+03,   2.01300000e+03,   2.18100000e+03,   2.24600000e+03,
                  2.74000000e+03,   3.29500000e+03,   3.83700000e+03,   4.36200000e+03])

ydata = np.array([2.72707696,   2.63617439,   2.54527183,   2.45211548,   2.63617439,
                  3.84645565,   2.47615583,   3.05305409,   2.54827687,   3.84645565,
                  2.46413565,   3.84645565,   2.94401644,   3.84645565,   4.73168929,
                  2.98036232,   4.1887902,    4.71190817,   2.88949762,   4.66943022,
                  5.23254529,   7.15293505,   4.71190817,   4.27427572,   4.39964092,
                  5.63676707,   7.03331773,   4.04631435,   4.55163919,   4.60717115,
                  5.4294562,    5.33581921,   6.99572768,   4.25290434,   4.78402922,
                  4.95714817,   5.68848053,   7.03998354,   6.75991664,   6.87467146,
                  5.49979457,   5.44708431,   5.33581921,   5.55440859,   5.99876128,
                  6.97607676,   7.20959136,   5.67555216,   7.05477342,   5.62924139,
                  6.87467146,   5.49979457,   5.46471241,   8.43869703,   6.60614702,
                  5.50652576,   7.6992562,    5.60748354,   5.49979457,   6.65544662,
                  5.53312733,   7.03998354,   5.54562619,   8.43869703,   6.70474623,
                  7.98685649,   5.54642812,   7.6992562,    5.79311695,   5.57370352,
                  5.33095537,   5.49979457,   8.96209787,   8.39607735,   5.55972891,
                  5.89263704,   7.89667302,   5.63000355,   9.13127276,   5.88936335,
                  8.40673227,   7.98685649,   8.93445391,   7.7239333,    5.3879134,
                  8.41312522,   8.96209787,   7.6992562,    8.43869703,   5.3777794,
                  7.98685649,   8.42043146,   7.91312442,   5.58496353,   9.13127276,
                  8.42271465,   10.84809562,  7.97249164,   7.85436942,   5.49166632,
                  9.07215872,   9.24042836,   7.82264171,   5.41324842,   7.96387274,
                  8.57566622,   9.0446435,    7.94855023,   8.9548522,    7.94581407,
                  9.17907497,   9.12221396,   10.85799351,  7.97249164,   9.08002021,
                  9.24042836,   8.95734376,   9.20468097,   8.58170542,   9.11803858,
                  8.97978622,   10.87449001,  8.98035007,   9.11574339,   8.76398243,
                  9.11768456,   10.79365718,  8.59023265,   10.84809562,  10.87119071,
                  10.85092359,  10.79365718])


points = np.vstack((xdata, ydata)).T

hull = ConvexHull(points)


plt.plot(points[:, 0], points[:, 1], 'o')

for simplex in hull.simplices:
    plt.plot(points[simplex, 0], points[simplex, 1], 'k-')

plt.xscale("log")
plt.grid(True)
plt.show()

Code: Alles auswählen

plt.xscale("log")

Code: Alles auswählen

points = np.vstack((np.log(xdata), ydata)).T
hull = ConvexHull(points)

plt.plot(xdata, ydata, 'o')

for simplex in hull.simplices:
    plt.plot(xdata[simplex], ydata[simplex], 'k-')

plt.xscale("log")
plt.grid(True)
plt.show()