def test_distance(self):
X = [0, 0, 0]
Y = [1, 2, 3]
result = [1.0, 1.0]
self.assertEqual(result, distance(X, Y))
def run_example_levy_walk_w_plots():
import numpy as N
import pylab as plt
from scikits.statsmodels.tools.tools import ECDF
from complex_systems.mobility.levy_walk import levy_walk
from complex_systems.mobility.distance import distance
alpha = 0.9
beta = 0.9
f_min = 1.0
f_max = 100.0
s_min = 1.0
s_max = 100.0
velocity = 5.0
(un_sampled, A, B, sampled) = levy_walk(
alpha=alpha,
beta=beta,
sample_length=10.0,
size_max=10000,
velocity=velocity,
f_min=f_min,
f_max=f_max,
s_min=s_min,
s_max=s_max,
duration=1000,
b_c=2,
)
(X, Y, T) = zip(*un_sampled)
(X_sampled, Y_sampled, T_sampled) = zip(*sampled)
print T_sampled, X_sampled, Y_sampled
plt.figure()
plt.plot(X_sampled, Y_sampled, 'ro-')
plt.plot(X, Y, 'o-')
ecdf = ECDF(A)
x = N.linspace(min(A), max(A))
y = 1 - ecdf(x)
ax1 = plt.subplot(121)
plt.loglog(x, y, 'r', linewidth=2.0)
plt.xlim(1, max(A))
plt.ylim(min(y), max(y))
plt.grid(True, which='both')
plt.xlabel('Distance [m]')
plt.ylabel('P(X > x)')
ecdf = ECDF(B)
x = N.linspace(min(B), max(B))
y = 1 - ecdf(x)
ax2 = plt.subplot(122, sharey=ax1)
plt.setp( ax2.get_yticklabels(), visible=False)
plt.loglog(x, y, 'r', linewidth=2.0)
plt.xlim(1, max(B))
plt.ylim(min(y), max(y))
plt.grid(True, which='both')
plt.xlabel('Pause Time [mins]')
plt.savefig('levywalk.pdf')
plt.savefig('levywalk.eps')
#plt.ylabel('P(X > x)')
D = distance(X, Y)
D_sampled = distance(X_sampled, Y_sampled)
Dcumsum = N.cumsum(D)
Dcumsum_sampled = N.cumsum(D_sampled)
plt.figure()
plt.plot(T[0:len(D)], Dcumsum, 'o-r', linewidth=2.0)
plt.plot(T_sampled[0:len(Dcumsum_sampled)], Dcumsum_sampled, 'o-',
linewidth=2.0)
plt.ylabel('Cumulative distance traveled by the random walker (meters)'
)
plt.xlabel('Time (mins)')
plt.show()