def test_regular_fully_degenerate():
"""Selfenergy with an invertible hopping matrix, and degenerate bands."""
w = 6 # width
t = 0.5 # hopping element
e = 1.3 # Fermi energy
h_hop_s = -t * np.identity(w)
h_cell_s = h_cell_s_func(t, w, e)
h_hop = np.zeros((2*w, 2*w))
h_hop[:w, :w] = h_hop_s
h_hop[w:, w:] = h_hop_s
h_cell = np.zeros((2*w, 2*w))
h_cell[:w, :w] = h_cell_s
h_cell[w:, w:] = h_cell_s
g = np.zeros((2*w, 2*w), dtype=complex)
g[:w, :w] = leads.square_selfenergy(w, t, e)
g[w:, w:] = leads.square_selfenergy(w, t, e)
assert_almost_equal(g, modes_se(h_cell, h_hop))
# Now with conservation laws and symmetries.
conserved = np.identity(2*w)
projectors = [sparse.csr_matrix(i) for i in [conserved[:, :w],
conserved[:, w:]]]
modes2 = leads.modes(h_cell, h_hop, projectors=projectors)
current_conserving(modes2[1])
assert_almost_equal(g, modes2[1].selfenergy())
trs = sparse.identity(2*w)
modes3 = leads.modes(h_cell, h_hop, projectors=projectors,
time_reversal=trs)
current_conserving(modes3[1])
assert_almost_equal(g, modes3[1].selfenergy())
phs = np.eye(2*w, 2*w, w) + np.eye(2*w, 2*w, -w)
modes4 = leads.modes(h_cell, h_hop, projectors=projectors,
time_reversal=trs, particle_hole=phs)
current_conserving(modes4[1])
assert_almost_equal(g, modes4[1].selfenergy())
def test_for_all_evs_equal():
"""Test an 'ideal lead' which has all eigenvalues e^ik equal."""
onsite = np.array([[0., 1.], [1., 0.]], dtype=complex)
hopping = np.array([[0.0], [-1.0]], dtype=complex)
modes = leads.modes(onsite, hopping)[1]
assert modes.vecs.shape == (1, 2)
assert modes.vecslmbdainv.shape == (1, 2)
assert modes.nmodes == 1
def test_modes_bearded_ribbon():
# Check if bearded graphene ribbons work.
lat = kwant.lattice.honeycomb()
syst = kwant.Builder(kwant.TranslationalSymmetry((1, 0)))
syst[lat.shape((lambda pos: -20 < pos[1] < 20),
(0, 0))] = 0.3
syst[lat.neighbors()] = -1
syst = syst.finalized()
h, t = syst.cell_hamiltonian(), syst.inter_cell_hopping()
# The number of expected modes is calculated by plotting the dispersion.
assert leads.modes(h, t)[1].nmodes == 8
def test_modes_bearded_ribbon():
# Check if bearded graphene ribbons work.
lat = kwant.lattice.honeycomb(norbs=1)
syst = kwant.Builder(kwant.TranslationalSymmetry((1, 0)))
syst[lat.shape((lambda pos: -20 < pos[1] < 20),
(0, 0))] = 0.3
syst[lat.neighbors()] = -1
syst = syst.finalized()
h, t = syst.cell_hamiltonian(), syst.inter_cell_hopping()
# The number of expected modes is calculated by plotting the dispersion.
assert leads.modes(h, t)[1].nmodes == 8
def test_for_all_evs_equal():
"""Test an 'ideal lead' which has all eigenvalues e^ik equal."""
onsite = np.array([[0., 1.], [1., 0.]], dtype=complex)
hopping = np.array([[0.0], [-1.0]], dtype=complex)
modes = leads.modes(onsite, hopping)[1]
current_conserving(modes)
assert modes.vecs.shape == (1, 2)
assert modes.vecslmbdainv.shape == (1, 2)
assert modes.nmodes == 1
def test_modes():
h, t = .3, .7
k = np.arccos(-h / (2 * t))
v = 2 * t * np.sin(k)
prop, stab = leads.modes(np.array([[h]]), np.array([[t]]))
assert stab.nmodes == 1
assert stab.sqrt_hop[0] == np.sqrt(np.linalg.norm(t))
np.testing.assert_almost_equal(prop.velocities, [-v, v])
np.testing.assert_almost_equal(prop.momenta, [k, -k])
# Test for normalization by current.
np.testing.assert_almost_equal(
2 * (stab.vecs[0] * stab.vecslmbdainv[0].conj()).imag, [1, -1])
def test_modes():
h, t = .3, .7
k = np.arccos(-h / (2 * t))
v = 2 * t * np.sin(k)
prop, stab = leads.modes(np.array([[h]]), np.array([[t]]))
current_conserving(stab)
assert stab.nmodes == 1
assert stab.sqrt_hop[0] == np.sqrt(np.linalg.norm(t))
np.testing.assert_almost_equal(prop.velocities, [-v, v])
np.testing.assert_almost_equal(prop.momenta, [k, -k])
# Test for normalization by current.
np.testing.assert_almost_equal(
2 * (stab.vecs[0] * stab.vecslmbdainv[0].conj()).imag, [1, -1])
def test_zero_hopping():
h_cell = np.identity(2)
h_hop = np.zeros((2, 1))
expected = (leads.PropagatingModes(np.zeros((2, 0)), np.zeros((0,)),
np.zeros((0,))),
leads.StabilizedModes(np.zeros((0, 0)), np.zeros((0, 0)), 0,
np.zeros((1, 0))))
actual = leads.modes(h_cell, h_hop)
assert all(np.alltrue(getattr(actual[1], attr) ==
getattr(expected[1], attr)) for attr
in ('vecs', 'vecslmbdainv', 'nmodes', 'sqrt_hop'))
assert all(np.alltrue(getattr(actual[0], attr) ==
getattr(expected[0], attr)) for attr
in ('wave_functions', 'velocities', 'momenta'))
def test_zero_hopping():
h_cell = np.identity(2)
h_hop = np.zeros((2, 1))
expected = (leads.PropagatingModes(np.zeros((2, 0)), np.zeros((0,)),
np.zeros((0,))),
leads.StabilizedModes(np.zeros((0, 0)), np.zeros((0, 0)), 0,
np.zeros((1, 0))))
actual = leads.modes(h_cell, h_hop)
assert all(np.alltrue(getattr(actual[1], attr) ==
getattr(expected[1], attr)) for attr
in ('vecs', 'vecslmbdainv', 'nmodes', 'sqrt_hop'))
assert all(np.alltrue(getattr(actual[0], attr) ==
getattr(expected[0], attr)) for attr
in ('wave_functions', 'velocities', 'momenta'))
def check_equivalence(h, t, n, sym='', particle_hole=None, chiral=None,
time_reversal=None):
"""Compare modes stabilization algorithms for a given Hamiltonian."""
u, s, vh = la.svd(t)
u, v = u * np.sqrt(s), vh.T.conj() * np.sqrt(s)
prop_vecs = []
evan_vecs = []
algos = [None, (True, True), (True, False), (False, True), (False, False)]
for algo in algos:
result = leads.modes(h, t, stabilization=algo, chiral=chiral,
particle_hole=particle_hole,
time_reversal=time_reversal)[1]
current_conserving(result, (sym, algo, n))
vecs, vecslmbdainv = result.vecs, result.vecslmbdainv
# Bring the calculated vectors to real space
if algo is not None:
vecs = np.dot(v, vecs)
np.testing.assert_almost_equal(result.sqrt_hop, v)
else:
vecslmbdainv = (np.dot(v.T.conj(), vecslmbdainv) /
np.sqrt(np.linalg.norm(t)))
vecs = vecs * np.sqrt(np.linalg.norm(t))
full_vecs = np.r_[vecslmbdainv, vecs]
prop_vecs.append(full_vecs[:, : 2 * result.nmodes])
evan_vecs.append(full_vecs[:, 2 * result.nmodes :])
msg = 'Stabilization {0} failed.in symmetry class {1}'
for vecs, algo in zip(prop_vecs, algos):
# Propagating modes should have identical ordering, and only vary
# By a phase
np.testing.assert_allclose(np.abs(np.sum(vecs/prop_vecs[0], axis=0)),
vecs.shape[0],
err_msg=msg.format(algo, sym))
for vecs, algo in zip(evan_vecs, algos):
# Evanescent modes must span the same linear space.
mat = np.c_[vecs, evan_vecs[0]]
# Scale largest singular value to 1 if the array is not empty
mat = mat/np.linalg.norm(mat, ord=2)
# As a tolerance, take the square root of machine precision times the
# largest matrix dimension.
tol = np.abs(np.sqrt(max(mat.shape)*np.finfo(mat.dtype).eps))
assert (np.linalg.matrix_rank(mat, tol=tol) ==
vecs.shape[1]), msg.format(algo)+' in symmetry class '+sym
def check_equivalence(h, t, n, sym='', particle_hole=None, chiral=None, time_reversal=None):
"""Compare modes stabilization algorithms for a given Hamiltonian."""
u, s, vh = np.linalg.svd(t)
u, v = u * np.sqrt(s), vh.T.conj() * np.sqrt(s)
prop_vecs = []
evan_vecs = []
algos = [None, (True, True), (True, False), (False, True), (False, False)]
for algo in algos:
result = leads.modes(h, t, stabilization=algo, chiral=chiral,
particle_hole=particle_hole, time_reversal=time_reversal)[1]
vecs, vecslmbdainv = result.vecs, result.vecslmbdainv
# Bring the calculated vectors to real space
if algo is not None:
vecs = np.dot(v, vecs)
np.testing.assert_almost_equal(result.sqrt_hop, v)
else:
vecslmbdainv = (np.dot(v.T.conj(), vecslmbdainv) /
np.sqrt(np.linalg.norm(t)))
vecs = vecs * np.sqrt(np.linalg.norm(t))
full_vecs = np.r_[vecslmbdainv, vecs]
prop_vecs.append(full_vecs[:, : 2 * result.nmodes])
evan_vecs.append(full_vecs[:, 2 * result.nmodes :])
msg = 'Stabilization {0} failed.'
for vecs, algo in zip(prop_vecs, algos):
# Propagating modes should have identical ordering, and only vary
# By a phase
np.testing.assert_allclose(np.abs(np.sum(vecs/prop_vecs[0],
axis=0)), vecs.shape[0],
err_msg=msg.format(algo)+' in symmetry class '+sym)
for vecs, algo in zip(evan_vecs, algos):
# Evanescent modes must span the same linear space.
mat = np.c_[vecs, evan_vecs[0]]
# Scale largest singular value to 1 if the array is not empty
mat = mat/np.linalg.norm(mat, ord=2)
# As a tolerance, take the square root of machine precision times the largest
# matrix dimension.
tol = np.abs(np.sqrt(max(mat.shape)*np.finfo(mat.dtype).eps))
assert (np.linalg.matrix_rank(mat, tol=tol) ==
vecs.shape[1]), msg.format(algo)+' in symmetry class '+sym
def test_algorithm_equivalence():
np.random.seed(400)
n = 12
h = np.random.randn(n, n) + 1j * np.random.randn(n, n)
h += h.T.conj()
t = np.random.randn(n, n) + 1j * np.random.randn(n, n)
u, s, vh = np.linalg.svd(t)
u, v = u * np.sqrt(s), vh.T.conj() * np.sqrt(s)
prop_vecs = []
evan_vecs = []
algos = [None, (True, True), (True, False), (False, True), (False, False)]
for algo in algos:
result = leads.modes(h, t, stabilization=algo)[1]
vecs, vecslmbdainv = result.vecs, result.vecslmbdainv
# Bring the calculated vectors to real space
if algo is not None:
vecs = np.dot(v, vecs)
np.testing.assert_almost_equal(result.sqrt_hop, v)
else:
vecslmbdainv = (np.dot(v.T.conj(), vecslmbdainv) /
np.sqrt(np.linalg.norm(t)))
vecs = vecs * np.sqrt(np.linalg.norm(t))
full_vecs = np.r_[vecslmbdainv, vecs]
prop_vecs.append(full_vecs[:, : 2 * result.nmodes])
evan_vecs.append(full_vecs[:, 2 * result.nmodes :])
msg = 'Stabilization {0} failed.'
for vecs, algo in zip(prop_vecs, algos):
# Propagating modes should have identical ordering, and only vary
# By a phase
np.testing.assert_allclose(np.abs(np.sum(vecs/prop_vecs[0],
axis=0)), vecs.shape[0],
err_msg=msg.format(algo))
for vecs, algo in zip(evan_vecs, algos):
# Evanescent modes must span the same linear space.
assert (np.linalg.matrix_rank(np.c_[vecs, evan_vecs[0]], tol=1e-12) ==
vecs.shape[1]), msg.format(algo)
def test_modes_symmetries():
rng = ensure_rng(10)
for n in (4, 8, 40, 60):
for sym in kwant.rmt.sym_list:
# Random onsite and hopping matrices in symmetry class
h_cell = kwant.rmt.gaussian(n, sym, rng=rng)
# Hopping is an offdiagonal block of a Hamiltonian. We rescale it
# to ensure that there are modes at the Fermi level.
h_hop = 10 * kwant.rmt.gaussian(2*n, sym, rng=rng)[:n, n:]
if kwant.rmt.p(sym):
p_mat = np.array(kwant.rmt.h_p_matrix[sym])
p_mat = np.kron(np.identity(n // len(p_mat)), p_mat)
else:
p_mat = None
if kwant.rmt.t(sym):
t_mat = np.array(kwant.rmt.h_t_matrix[sym])
t_mat = np.kron(np.identity(n // len(t_mat)), t_mat)
else:
t_mat = None
if kwant.rmt.c(sym):
c_mat = np.kron(np.identity(n // 2), np.diag([1, -1]))
else:
c_mat = None
prop_modes, stab_modes = leads.modes(h_cell, h_hop,
particle_hole=p_mat,
time_reversal=t_mat,
chiral=c_mat)
current_conserving(stab_modes)
wave_functions = prop_modes.wave_functions
momenta = prop_modes.momenta
nmodes = stab_modes.nmodes
if t_mat is not None:
assert_almost_equal(wave_functions[:, nmodes:],
t_mat.dot(wave_functions[:, :nmodes].conj()),
err_msg='TRS broken in ' + sym)
if c_mat is not None:
assert_almost_equal(wave_functions[:, nmodes:],
c_mat.dot(wave_functions[:, :nmodes][:, ::-1]),
err_msg='SLS broken in ' + sym)
if p_mat is not None:
# If P^2 = -1, then P psi(-k) = -psi(k) for k>0, so one must
# look at positive and negative momenta separately. Test
# positive momenta.
first, last = momenta[:nmodes], momenta[nmodes:]
in_positive_k = (np.pi > first) * (first > 0)
out_positive_k = (np.pi > last) * (last > 0)
wf_first = wave_functions[:, :nmodes]
wf_last = wave_functions[:, nmodes:]
assert_almost_equal(wf_first[:, in_positive_k[::-1]],
p_mat.dot((wf_first[:, in_positive_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
assert_almost_equal(wf_last[:, out_positive_k[::-1]],
p_mat.dot((wf_last[:, out_positive_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
# Test negative momenta. Need the sign of P^2 here.
p_squared_sign = np.sign(p_mat.dot(p_mat.conj())[0, 0].real)
in_neg_k = (-np.pi < first) * (first < 0)
out_neg_k = (-np.pi < last) * (last < 0)
assert_almost_equal(p_squared_sign*wf_first[:, in_neg_k[::-1]],
p_mat.dot((wf_first[:, in_neg_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
assert_almost_equal(p_squared_sign*wf_last[:, out_neg_k[::-1]],
p_mat.dot((wf_last[:, out_neg_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
def test_PHS_TRIM_degenerate_ordering():
""" Test PHS at a TRIM, both when it squares to 1 and -1.
Take a Hamiltonian with 3 degenerate bands, the degeneracy of each is given
in the tuple dims. The bands have different velocities. All bands intersect
zero energy only at k = 0 and at the edge of the BZ, so all momenta are 0
or -pi. We thus have multiple TRIM modes, both with the same and different
velocities.
If P^2 = 1, all TRIM modes are eigenmodes of P.
If P^2 = -1, TRIM modes come in pairs of particle-hole partners, ordered by
a predefined convention."""
sy = np.array([[0,-1j],[1j,0]])
sz = np.array([[1,0],[0,-1]])
# P squares to 1.
rng = ensure_rng(42)
dims = (4, 8, 12)
ts = (1.0, 1.7, 13.8)
rand_hop = 1j*(0.1+rng.random_sample())
hop = la.block_diag(*[t*rand_hop*np.eye(dim) for t, dim in zip(ts, dims)])
pmat = np.eye(sum(dims))
onsite = np.zeros(hop.shape, dtype=complex)
prop, stab = leads.modes(onsite, hop, particle_hole=pmat)
current_conserving(stab)
assert np.all([np.any(momentum - np.array([0, -np.pi])) for momentum in
prop.momenta])
assert np.all([np.allclose(wf, pmat.dot(wf.conj())) for wf in
prop.wave_functions.T])
# P squares to -1
dims = (1, 4, 10)
ts = (1.0, 17.2, 13.4)
hop_mat = np.kron(sz, 1j * (0.1 + rng.random_sample()) * np.eye(2))
blocks = []
for t, dim in zip(ts, dims):
blocks += dim*[t*hop_mat]
hop = la.block_diag(*blocks)
pmat = np.kron(np.eye(sum(dims)), 1j*np.kron(sz, sy))
assert_almost_equal(pmat.dot(pmat.conj()), -np.eye(pmat.shape[0]))
# The Hamiltonian anticommutes with P
assert_almost_equal(pmat.dot(hop.conj()).dot(np.linalg.inv(pmat)), -hop)
onsite = np.zeros(hop.shape, dtype=complex)
prop, stab = leads.modes(onsite, hop, particle_hole=pmat)
current_conserving(stab)
# By design, all momenta are either 0 or -pi.
assert np.all([np.any(momentum - np.array([0, -np.pi])) for momentum in
prop.momenta])
wfs = prop.wave_functions
momenta = prop.momenta
velocities = prop.velocities
nmodes = stab.nmodes
# By design, all modes are at a TRIM here. Each must thus have a
# particle-hole partner at the same TRIM and with the same velocity.
# Incident modes
check_PHS(0, momenta[:nmodes], velocities[:nmodes], wfs[:, :nmodes], pmat)
check_PHS(-np.pi, momenta[:nmodes], velocities[:nmodes], wfs[:, :nmodes],
pmat)
# Outgoing modes
check_PHS(0, momenta[nmodes:], velocities[nmodes:], wfs[:, nmodes:], pmat)
check_PHS(-np.pi, momenta[nmodes:], velocities[nmodes:], wfs[:, nmodes:],
pmat)
def test_PHS_TRIM_degenerate_ordering():
""" Test PHS at a TRIM, both when it squares to 1 and -1.
Take a Hamiltonian with 3 degenerate bands, the degeneracy of each is given
in the tuple dims. The bands have different velocities. All bands intersect
zero energy only at k = 0 and at the edge of the BZ, so all momenta are 0
or -pi. We thus have multiple TRIM modes, both with the same and different
velocities.
If P^2 = 1, all TRIM modes are eigenmodes of P.
If P^2 = -1, TRIM modes come in pairs of particle-hole partners, ordered by
a predefined convention."""
sy = np.array([[0,-1j],[1j,0]])
sz = np.array([[1,0],[0,-1]])
### P squares to 1 ###
np.random.seed(42)
dims = (4, 10, 20)
ts = (1.0, 1.7, 13.8)
rand_hop = 1j*(0.1+np.random.rand())
hop = la.block_diag(*[t*rand_hop*np.eye(dim) for t, dim in zip(ts, dims)])
# Particle-hole operator
pmat = np.eye(sum(dims))
onsite = np.zeros(hop.shape, dtype=complex)
prop, stab = leads.modes(onsite, hop, particle_hole=pmat)
# All momenta are either 0 or -pi.
assert np.all([np.any(ele - np.array([0, -np.pi])) for ele in prop.momenta])
# All modes are eigenmodes of P.
assert np.all([np.allclose(wf, pmat.dot(wf.conj())) for wf in prop.wave_functions.T])
###########
### P squares to -1 ###
np.random.seed(1337)
dims = (1, 4, 16)
ts = (1.0, 17.2, 13.4)
hop_mat = np.kron(sz, 1j*(0.1+np.random.rand())*np.eye(2))
blocks = []
for t, dim in zip(ts, dims):
blocks += dim*[t*hop_mat]
hop = la.block_diag(*blocks)
# Particle-hole operator
pmat = np.kron(np.eye(sum(dims)), 1j*np.kron(sz, sy))
# P squares to -1
assert np.allclose(pmat.dot(pmat.conj()), -np.eye(pmat.shape[0]))
# The Hamiltonian anticommutes with P
assert np.allclose(pmat.dot(hop.conj()).dot(npl.inv(pmat)), -hop)
onsite = np.zeros(hop.shape, dtype=complex)
prop, stab = leads.modes(onsite, hop, particle_hole=pmat)
# By design, all momenta are either 0 or -pi.
assert np.all([np.any(ele - np.array([0, -np.pi])) for ele in prop.momenta])
wfs = prop.wave_functions
momenta = prop.momenta
velocities = prop.velocities
nmodes = stab.nmodes
# By design, all modes are at a TRIM here. Each must thus have a particle-hole
# partner at the same TRIM and with the same velocity.
# Incident modes
check_PHS(0, momenta[:nmodes], velocities[:nmodes], wfs[:, :nmodes], pmat)
check_PHS(-np.pi, momenta[:nmodes], velocities[:nmodes], wfs[:, :nmodes], pmat)
# Outgoing modes
check_PHS(0, momenta[nmodes:], velocities[nmodes:], wfs[:, nmodes:], pmat)
check_PHS(-np.pi, momenta[nmodes:], velocities[nmodes:], wfs[:, nmodes:], pmat)
def test_modes_symmetries():
np.random.seed(10)
for n in (4, 8, 40, 60):
for sym in kwant.rmt.sym_list:
# Random onsite and hopping matrices in symmetry class
h_cell = kwant.rmt.gaussian(n, sym)
# Hopping is an offdiagonal block of a Hamiltonian. We rescale it
# to ensure that there are modes at the Fermi level.
h_hop = 10 * kwant.rmt.gaussian(2*n, sym)[:n, n:]
if kwant.rmt.p(sym):
p_mat = np.array(kwant.rmt.h_p_matrix[sym])
p_mat = np.kron(np.identity(n // len(p_mat)), p_mat)
else:
p_mat = None
if kwant.rmt.t(sym):
t_mat = np.array(kwant.rmt.h_t_matrix[sym])
t_mat = np.kron(np.identity(n // len(t_mat)), t_mat)
else:
t_mat = None
if kwant.rmt.c(sym):
c_mat = np.kron(np.identity(n // 2), np.diag([1, -1]))
else:
c_mat = None
prop_modes, stab_modes = leads.modes(h_cell, h_hop, particle_hole=p_mat,
time_reversal=t_mat, chiral=c_mat)
wave_functions = prop_modes.wave_functions
momenta = prop_modes.momenta
nmodes = stab_modes.nmodes
if t_mat is not None:
assert_almost_equal(wave_functions[:, nmodes:],
t_mat.dot(wave_functions[:, :nmodes].conj()),
err_msg='TRS broken in ' + sym)
if c_mat is not None:
assert_almost_equal(wave_functions[:, nmodes:],
c_mat.dot(wave_functions[:, :nmodes:-1]),
err_msg='SLS broken in ' + sym)
if p_mat is not None:
# If P^2 = -1, then P psi(-k) = -psi(k) for k>0, so one must look at
# positive and negative momenta separately.
# Test positive momenta.
in_positive_k = (np.pi > momenta[:nmodes]) * (momenta[:nmodes] > 0)
out_positive_k = (np.pi > momenta[nmodes:]) * (momenta[nmodes:] > 0)
assert_almost_equal(wave_functions[:, :nmodes][:, in_positive_k[::-1]],
p_mat.dot((wave_functions[:, :nmodes][:, in_positive_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
assert_almost_equal(wave_functions[:, nmodes:][:, out_positive_k[::-1]],
p_mat.dot((wave_functions[:, nmodes:][:, out_positive_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
# Test negative momenta. Need the sign of P^2 here.
p_squared_sign = np.sign(p_mat.dot(p_mat.conj())[0, 0].real)
in_neg_k = (-np.pi < momenta[:nmodes]) * (momenta[:nmodes] < 0)
out_neg_k = (-np.pi < momenta[nmodes:]) * (momenta[nmodes:] < 0)
assert_almost_equal(p_squared_sign*wave_functions[:, :nmodes][:, in_neg_k[::-1]],
p_mat.dot((wave_functions[:, :nmodes][:, in_neg_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
assert_almost_equal(p_squared_sign*wave_functions[:, nmodes:][:, out_neg_k[::-1]],
p_mat.dot((wave_functions[:, nmodes:][:, out_neg_k][:, ::-1]).conj()),
err_msg='PHS broken in ' + sym)
def test_cons_blocks_sizes():
# Hamiltonian with a conservation law consisting of three blocks of
# different size.
n = 8
# Three blocks of different sizes.
cs = np.diag(n*[-1] + 2*n*[1] + 3*n*[2])
# Onsite and hopping
hc, hh = random_onsite_hop(6*n)
hc[:n, n:] = 0
hc[n:, :n] = 0
hc[n:3*n, 3*n:] = 0
hc[3*n:, n:3*n] = 0
assert_almost_equal(cs.dot(hc) - hc.dot(cs), 0)
hh[:n, n:] = 0
hh[n:, :n] = 0
hh[n:3*n, 3*n:] = 0
hh[3*n:, n:3*n] = 0
assert_almost_equal(cs.dot(hh) - hh.dot(cs), 0)
# Mix them with a random unitary
U = kwant.rmt.circular(6*n, 'A', rng=23)
cs_t = U.T.conj().dot(cs).dot(U)
hc_t = U.T.conj().dot(hc).dot(U)
hh_t = U.T.conj().dot(hh).dot(U)
assert_almost_equal(cs_t.dot(hc_t) - hc_t.dot(cs_t), 0)
assert_almost_equal(cs_t.dot(hh_t) - hh_t.dot(cs_t), 0)
# Get the projectors. There are three of them, composed of
# the eigenvectors of the conservation law.
evals, evecs = np.linalg.eigh(cs_t)
# Make sure the ordering is correct.
assert_almost_equal(evals-np.array(n*[-1] + 2*n*[1] + 3*n*[2]), 0)
# First projector projects onto block with
# eigenvalue -1 of size n, second to eigenvalue 1 of
# size 2n, third onto block with eigenvalue 2 of size 3n.
projectors = [np.reshape(evecs[:,:n], (6*n, n)),
np.reshape(evecs[:,n:3*n], (6*n, 2*n)),
np.reshape(evecs[:,3*n:6*n], (6*n, 3*n))]
# Check that projectors are correctly defined.
assert_almost_equal(evecs, np.hstack(projectors))
########
# Compute self energy with and without specifying projectors.
# Make the projectors sparse.
projectors = [sparse.csr_matrix(p) for p in projectors]
# Modes without projectors
modes1 = leads.modes(hc_t, hh_t)
current_conserving(modes1[1])
# With projectors
modes2 = leads.modes(hc_t, hh_t, projectors=projectors)
current_conserving(modes2[1])
assert_almost_equal(modes1[1].selfenergy(), modes2[1].selfenergy())
########
# Check that the number of modes per block with projectors matches the
# total number of modes, with and without projectors.
prop1, stab1 = modes1 # No projectors
prop2, stab2 = modes2 # Projectors
assert_almost_equal(stab1.nmodes, sum(prop1.block_nmodes))
assert_almost_equal(stab2.nmodes, sum(prop2.block_nmodes))
assert_almost_equal(stab1.nmodes, sum(prop2.block_nmodes))
########
# Check that with projectors specified, in/out propagating modes and
# evanescent modes in vecs and vecslmbdainv are block diagonal.
# Do this by checking explicitly that the blocks are nonzero, and that
# if all blocks are set to zero manually, the stabilized modes only contain
# zeros (meaning that anything off the block diagonal is zero).
nmodes = stab2.nmodes
block_nmodes = prop2.block_nmodes
vecs, vecslmbdainv = stab2.vecs, stab2.vecslmbdainv
# Row indices for the blocks.
# The first block is of size n, second of size 2n
# and the third of size 3n.
block_rows = [slice(0, n), slice(n, 3*n), slice(3*n, 6*n)]
### Check propagating modes ###
# Column indices for the blocks.
offsets = np.cumsum([0]+block_nmodes)
block_cols = [slice(*i) for i in np.vstack([offsets[:-1], offsets[1:]]).T]
prop_modes = (vecs[:, :nmodes], vecs[:, nmodes:2*nmodes],
vecslmbdainv[:, :nmodes], vecslmbdainv[:, nmodes:2*nmodes])
check_bdiag_modes(prop_modes, block_rows, block_cols)
### Check evanescent modes ###
# First figure out the number of evanescent modes per block.
# The number of relevant evanescent modes (outward decaying) in a block is
# N - nmodes, where N is the dimension of the block and nmodes the number
# of incident or outgoing propagating modes.
block_cols = [N - nmodes for nmodes, N in zip(block_nmodes, [n, 2*n, 3*n])]
offsets = np.cumsum([0]+block_cols)
block_cols = [slice(*i) for i in np.vstack([offsets[:-1], offsets[1:]]).T]
ev_modes = (vecs[:, 2*nmodes:], vecslmbdainv[:, 2*nmodes:])
check_bdiag_modes(ev_modes, block_rows, block_cols)
