beta = complex(sqrt(2) / sqrt(3), 0)
# beta = complex(1.0 / sqrt(2), 0)
ro_0 = [[0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 0, abs(alpha)**2, alpha * beta.conjugate(), 0],
[0, 0, beta * alpha.conjugate(),
abs(beta)**2, 0], [0, 0, 0, 0, 0]]
# for i in ro_0:
# print(i)
_a = [[0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
a = Matrix(m=len(_a), n=len(_a), dtype=np.complex128)
a.data = csc_matrix(_a)
across = Matrix(m=len(_a), n=len(_a), dtype=np.complex128)
across.data = a.data.getH()
# ro_t = copy.deepcopy(ro_0)
# ro_t = Matrix(m=len(ro_0), n=len(ro_0), dtype=np.complex128)
# ro_t.data = csc_matrix(ro_0)
# config.l = 0.01*config.g
dt = 0.1 * 1e-9
# dt = 0.01 / config.l
config.l = 1.0 / dt
print(config.l / config.g)
# print('n_atoms', n_atoms)
for n_levels in range(N_LEVELS_1, N_LEVELS_2 + 1):
# print('n_levels', n_levels)
for i in range(n_levels):
# print('i', i, end='')
for j in range(i):
# print('j', j)
for i_atom in range(1, n_atoms + 1):
s = op.Sigma2(i_=i,
j_=j,
num=i_atom,
n_atoms=n_atoms,
n_levels=n_levels)
# filename = 'dark_dense/' + '_'.join(str(i)
# for i in [n_atoms, n_levels, i, j, i_atom]) + '.csv'
# print(filename)
m = Matrix(m=np.shape(s)[0], n=np.shape(s)[0], dtype=float)
# print(s)
m.data = s.todense()
m.to_csv('dark_dense/' + '_'.join(
str(i)
for i in [n_atoms, n_levels, i, j, i_atom]) + '.csv',
precision=0)
# for p in s.todense():
# print(p)
