else:
for j in range(L):
if A[j] > mi and A[j] <= mx:
H[2 * i] = H[2 * i] + 1
H[2 * i + 1] = H[2 * i + 1] + 1
H[2 * i] = H[2 * i] / L
H[2 * i + 1] = H[2 * i + 1] / L
plt.plot(X, H)
plt.ylim(0, 1)
plt.show()
A = np.zeros(100000)
for m in range(100000):
A[m] = intr.exponential(2)
hist(A, 100)
#The histogram looks very similar to the expected probability density function.
[w, Sw] = intr.monte_carlo_3d(intr.density, [-0.5, -0.5, -1], [1, 1, 1],
intr.sphere, 100000)
[x, Sx] = intr.monte_carlo_3d(intr.xmoment, [-0.5, -0.5, -1], [1, 1, 1],
intr.sphere, 100000)
[y, Sy] = intr.monte_carlo_3d(intr.ymoment, [-0.5, -0.5, -1], [1, 1, 1],
intr.sphere, 100000)
[z, Sz] = intr.monte_carlo_3d(intr.zmoment, [-0.5, -0.5, -1], [1, 1, 1],
intr.sphere, 100000)
print(x / w, y / w, z / w)
# plt.plot(x,y1,x,y2) # [x,w] = intr.gauss_leg(0,1,3) ##print(x,w) # print(intr.trap(intr.quar,0,1,100)) # print(intr.gauss_quad(intr.quar,x,w)) # rng.init(pow(2,31),65539,0,1) # N = 10000 # rand1 = np.zeros(N) # rand2 = np.zeros(N) # rand3 = np.zeros(N) # for i in range(N): # rand1[i]=rng.lcg() # rand2[i]=rng.lcg() # rand3[i]=rng.lcg() # ##plt.scatter(rand1,rand2) # fig = plt.figure() # ax = fig.add_subplot(111,projection="3d") # ax.scatter(rand1,rand2,rand3) # # plt.show() [w, Sw] = intr.monte_carlo_3d(intr.density, [0, -4, -1], [4, 4, 1], intr.torus, 100000) [x, Sx] = intr.monte_carlo_3d(intr.xmoment, [0, -4, -1], [4, 4, 1], intr.torus, 100000) [y, Sy] = intr.monte_carlo_3d(intr.ymoment, [0, -4, -1], [4, 4, 1], intr.torus, 100000) [z, Sz] = intr.monte_carlo_3d(intr.zmoment, [0, -4, -1], [4, 4, 1], intr.torus, 100000) print(x / w, y / w, z / w)
# break
# elif check >= max_A:
# index = N-1
#sum_check = 0
## finds out which bin the "check" value lies within and spits out the index of that bin location
#for h in range(index+1):
# sum_check = sum_check+normalized[h]*dx
##sums up the area up until the "check" value
#print("estimated")
#print(1-sum_check)
#
#plt.show()
#############################
#Assignment 6 Problem 2
[w,Sw] = intr.monte_carlo_3d(intr.density,[-.5,-.5,-1],[1,1,1],intr.sphere,100000)
[x,Sx] = intr.monte_carlo_3d(intr.xmoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000)
[y,Sy] = intr.monte_carlo_3d(intr.ymoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000)
[z,Sz] = intr.monte_carlo_3d(intr.zmoment,[-.5,-.5,-1],[1,1,1],intr.sphere,100000)
print("CG of sphere with box (CGx,CGy,CGz)
print(x/w,y/w,z/w)
#print(intr.trap(intr.cub, 0, 1, 100))
##########################
#a = -1
#b = 1
#N = 10000
#h = (b-a) / (N-1)
#x = np.zeros(N)
#print(intr.trap(intr.quar,0,1,100))
#print(intr.gauss_quad(intr.quar,x,w))
#rng.init(pow(2,31),65539,0,1)
#N = 10000
#rand1 = np.zeros(N)
#rand2 = np.zeros(N)
#rand3 = np.zeros(N)
#for i in range(N):
# rand1[i]=rng.lcg()
# rand2[i]=rng.lcg()
# rand3[i]=rng.lcg()
#
##plt.scatter(rand1,rand2)
#fig = plt.figure()
#ax = fig.add_subplot(111,projection="3d")
#ax.scatter(rand1,rand2,rand3)
#
#plt.show()
[w, Sw] = intr.monte_carlo_3d(intr.density, [0, -4, -1], [4, 4, 1], intr.torus,
100000)
[x, Sx] = intr.monte_carlo_3d(intr.xmoment, [0, -4, -1], [4, 4, 1], intr.torus,
100000)
[y, Sy] = intr.monte_carlo_3d(intr.ymoment, [0, -4, -1], [4, 4, 1], intr.torus,
100000)
[z, Sz] = intr.monte_carlo_3d(intr.zmoment, [0, -4, -1], [4, 4, 1], intr.torus,
100000)
print(x / w, y / w, z / w)
