def test_hf_fast_updatecond_complex(chempot, u_int, updater):
"""Test over the fast update after a spin flip"""
w_n = cgf.matsubara_freq(20, 20)
SO_N1 = np.array([[0, 1j, -1], [-1j, 0, 1j], [-1, -1j, 0]]) / 2
# Semi circle GF by iteration
H_loc = SO_N1 + chempot * np.eye(3)
g_0_1 = [1j * wn * np.eye(3) - H_loc for wn in w_n]
g_0 = [la.inv(g) for g in g_0_1]
for i in range(40):
g_0_1 = [
1j * wn * np.eye(3) - H_loc - 0.25 * g for wn, g in zip(w_n, g_0)
]
g_0 = [la.inv(g) for g in g_0_1]
G_tail = [np.eye(3).reshape(3, 3, 1), H_loc.reshape(3, 3, 1), 0]
tau = np.arange(0, 20, 20 / len(w_n))
g_0 = np.array(g_0)
g0t = cgf.gw_invfouriertrans(np.rollaxis(g_0, 0, 3), tau, w_n, G_tail)
v = hf.ising_v(tau[1], u_int, len(tau) * 3)
v = np.squeeze(v)
g0ttp = hf.retarded_weiss(g0t)
kroneker = np.eye(v.size)
groot = hf.gnewclean(g0ttp, v, kroneker)
g_fast_flip = np.copy(groot)
flip = randrange(v.size)
v[flip] *= -1
g_flip = hf.gnewclean(g0ttp, v, kroneker)
updater(g_fast_flip, 2 * v[flip], flip)
assert np.allclose(g_flip, g_fast_flip)
def generate_random_gf(beta, size):
"""Generates a gf in a bethe lattice with a random Hamiltonian"""
w_n = cgf.matsubara_freq(beta=beta, pos_size=beta)
tau = np.arange(0, beta, beta / len(w_n))
H_loc = np.random.randn(size, size) + 1j * np.random.randn(size, size)
H_loc = H_loc + H_loc.T.conj() # Make Hermitian hamiltonian
g_0_1 = [1j * wn * np.eye(size) - H_loc for wn in w_n]
g_0 = [la.inv(g) for g in g_0_1]
for i in range(40):
g_0_1 = [1j * wn * np.eye(size) - H_loc - 0.25 *
g for wn, g in zip(w_n, g_0)]
g_0 = [la.inv(g) for g in g_0_1]
g_tail = [np.eye(size).reshape(size, size, 1),
H_loc.reshape(size, size, 1),
0.25 * np.eye(size).reshape(size, size, 1)]
return np.rollaxis(np.array(g_0), 0, 3), g_tail, w_n, tau
def test_greenf(beta):
wn = cgf.matsubara_freq(beta, 2 * beta)
semicirc = cgf.greenF(wn)
assert np.allclose(semicirc.real, semicirc.real[::-1])
assert np.allclose(semicirc.imag, -semicirc.imag[::-1])
def test_matsubara_axis(beta):
wn = cgf.matsubara_freq(beta, 2 * beta)
assert len(wn) % 2 == 0
assert np.allclose(wn, -wn[::-1])