import numpy as np
from scipy import stats
import math as ma
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
n = 10
rate= 3
Junksum = 1000
time = 1000
R = 5
ER = 6000
t = np.arange(n)
kurt = stats.poisson.pmf(t , rate)
kurt /= sum(kurt)
num = np.ones(n , dtype = int)
Junksum -= n
tmp = Junksum
while Junksum:
ma = 0
for i in range(n):
tt = num[i]
q = kurt[i]*kurt[i]/(tt)/(tt+1)
if ma < q:
ma = q
pos = i
num[pos] += 1
Junksum -= 1
x = [] ; y = [] ; z = []
for i in range(n):
for j in range(num[i]):
alpha = 2*np.random.rand()*np.pi
beta = 2*np.random.rand()*np.pi
r = (np.random.rand() + i)*300 + ER + 1000
xx = r*np.cos(alpha)*np.sin(beta)
yy = r*np.cos(alpha)*np.cos(beta)
zz = r*np.sin(alpha)
if(zz > ER):
ttt = np.random.rand()
if ttt > 0.5:
continue
x.append(xx)
y.append(yy)
z.append(zz)
Junksum = tmp
for i in range(Junksum):
alpha = 2*np.random.rand()*np.pi
r = (np.random.rand() + i/100)*3000 + ER + 3000
xx = r*np.cos(alpha)
yy = r*np.sin(alpha)
zz = 0
x.append(xx)
y.append(yy)
z.append(zz)
x = np.array(x)
y = np.array(y)
z = np.array(z)
fig = plt.figure()
ax = fig.gca(projection = '3d' , axisbg='k')
u = np.linspace(0,2*np.pi , 100)
v = np.linspace(0,2*np.pi, 100)
X = ER*np.outer(np.cos(u) , np.sin(v))
Y = ER*np.outer(np.sin(u) , np.sin(v))
Z = ER*np.outer(np.ones(np.size(u)),np.cos(v))
surf = ax.plot_surface(X , Y , Z , rstride=1, cstride=1, color ='b',
linewidth=0, antialiased=False)
ax.scatter(x, y ,z, zdir='z' ,c='w',edgecolors = 'w' )
plt.axis('off')
ax.set_zlim(-16000,16000)
ax.set_xlim(-16000,16000)
ax.set_ylim(-16000,16000)
plt.show()
