occupy_index
) #beta :correspoding position in D matrix or the particle index
if conf[0][i] != 0:
occupy_index.append(i)
D = M[occupy_index]
det_old = np.linalg.det(D)
D[beta, :] = M[after, :]
det_new = np.linalg.det(D)
print det_old, det_new
return det_new / det_old
def Det(conf, L, u):
#h,dia,u=H(L)
M = u[:, np.arange(0, L, 1)]
occupy_index = []
for i in range(np.size(conf)):
if conf[0][i] != 0:
occupy_index.append(i)
D = M[occupy_index]
return np.linalg.det(D)
if __name__ == '__main__':
L = 32
h, dia, u = H(L)
conf = conf_initial(L, 16)
#conf=np.array([[1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1]])
#print Ratio(conf,15,8,L,u)
print Ratio1(conf, 3, 4, L, u)
#print Ratio2(conf,15,8,L,u)
hop_index = random.choice(neighbour)
if conf[0][hop_index] == 1:
return "reject"
ratio = Ratio(conf, select, hop_index, L)[0]
rho = (ratio * ratio.conjugate()).real
print rho
if random.uniform(0, 1) < min(1, rho):
conf[0][select] = 0
conf[0][hop_index] = 1
return [conf, ratio]
else:
return "reject"
conf = conf_initial(L, N_up)
samples = 0
E_total = 0
while True:
samples = samples + 1
try_hop = hop(conf, L)
if try_hop != "reject":
conf, ratio = try_hop
Eloc = E_loc1(conf, L)
E_total = E_total + Eloc
if samples == sample_goal:
break
E = E_total / samples
E_site = E / L
conf[0][after] = conf[0][before]
conf[0][before] = 0
occupy_index = []
for i in range(np.size(conf)):
if conf[0][i] != 0:
occupy_index.append(i)
D1 = M[occupy_index]
ratio = np.linalg.det(D1) / np.linalg.det(D)
print ratio
return [ratio, 0, 0, 0, 0]
def Det(conf, L):
h, dia, u = H(L)
M = u[:, np.arange(0, L, 1)]
occupy_index = []
for i in range(np.size(conf)):
if conf[0][i] != 0:
occupy_index.append(i)
D = M[occupy_index]
return np.linalg.det(D)
if __name__ == '__main__':
L = 10
h, dia, u = H(L)
conf = conf_initial(L, 5)
conf = np.array(
[[0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1]])
Ratio(conf, 4, 3, L)
Ratio1(conf, 4, 3, L)