def __init__(self, L=50):
self.L = L # number of interior points in x and y
self.omega = 1.88177 # over-relaxation parameter for L = 50
self.N = L + 2 # interior plus two boundary points
N = self.N
self.V = cpt.Matrix(N, N) # potential to be found
self.rho = cpt.Matrix(N, N) # given charge density
self.VNew = cpt.Matrix(N, N) # new potential after each step
self.h = 1.0 / (L + 1) # lattice spacing assuming size in x and y = 1
self.q = 10.0 # point charge
i = N / 2 # center of lattice
self.rho[i][i] = self.q / self.h**2 # charge density
import numpy as np
print " Random walk in 1 dimension"
print " --------------------------"
n_walkers = int(input(" Enter number of walkers: "))
n_steps = int(input(" Enter number of steps: "))
# walker positions initialized at x = 0 y=0
x = [ 0.0 ] * n_walkers
y = [ 0.0 ] * n_walkers
z = [ 0.0 ] * n_walkers
steps = [ 0.0 ] * n_steps # to save step number i (time)
x2ave = [ 0.0 ] * n_steps # to accumulate x^2 values
sigma = [ 0.0 ] * n_steps # to accumulate fluctuations in x^2
x2in = cpt.Matrix(n_walkers,n_steps) # individual x^2+y^2
# loop over walkers
for walker in range(n_walkers):
# loop over number of steps
for step in range(n_steps):
# take a random step
x[walker] += random.choice( (-1, 1) )
y[walker] += random.choice( (-1, 1) )
z[walker] += random.choice( (-1, 1) )
#while x[walker]**2+y[walker]**2 >= 1:
# x[walker] += random.choice( (-1, 1) )
# y[walker] += random.choice( (-1, 1) )
# accumulate data