def test_real_space_SE_fail_nsc_semi():
sq = Geometry([0] * 3, Atom(1, 1.01), [1])
sq.set_nsc([3, 5, 3])
H = Hamiltonian(sq)
H.construct([(0.1, 1.1), (4, -1)])
RSE = RealSpaceSE(H, 1, 0, (3, 4, 1), dk=100)
def test_real_space_SE_fail_k_semi_same():
sq = Geometry([0] * 3, Atom(1, 1.01), [1])
sq.set_nsc([3] * 3)
H = Hamiltonian(sq)
H.construct([(0.1, 1.1), (4, -1)])
RSE = RealSpaceSE(H, 0, 0, (3, 4, 1))
def read_density_matrix(self, **kwargs):
""" Returns the density matrix from the siesta.DM file """
# Now read the sizes used...
spin, no, nsc, nnz = _siesta.read_dm_sizes(self.file)
_bin_check(self, 'read_density_matrix',
'could not read density matrix sizes.')
ncol, col, dDM = _siesta.read_dm(self.file, spin, no, nsc, nnz)
_bin_check(self, 'read_density_matrix',
'could not read density matrix.')
# Try and immediately attach a geometry
geom = kwargs.get('geometry', kwargs.get('geom', None))
if geom is None:
# We truly, have no clue,
# Just generate a boxed system
xyz = [[x, 0, 0] for x in range(no)]
sc = SuperCell([no, 1, 1], nsc=nsc)
geom = Geometry(xyz, Atom(1), sc=sc)
if nsc[0] != 0 and np.any(geom.nsc != nsc):
# We have to update the number of supercells!
geom.set_nsc(nsc)
if geom.no != no:
raise SileError(
str(self) + '.read_density_matrix could not use the '
'passed geometry as the number of atoms or orbitals is '
'inconsistent with DM file.')
# Create the density matrix container
DM = DensityMatrix(geom,
spin,
nnzpr=1,
dtype=np.float64,
orthogonal=False)
# Create the new sparse matrix
DM._csr.ncol = ncol.astype(np.int32, copy=False)
DM._csr.ptr = np.insert(np.cumsum(ncol, dtype=np.int32), 0, 0)
# Correct fortran indices
DM._csr.col = col.astype(np.int32, copy=False) - 1
DM._csr._nnz = len(col)
DM._csr._D = np.empty([nnz, spin + 1], np.float64)
DM._csr._D[:, :spin] = dDM[:, :]
# DM file does not contain overlap matrix... so neglect it for now.
DM._csr._D[:, spin] = 0.
# Convert the supercells to sisl supercells
if nsc[0] != 0 or geom.no_s >= col.max():
_csr_from_siesta(geom, DM._csr)
else:
warn(
str(self) +
'.read_density_matrix may result in a wrong sparse pattern!')
return DM
def test_real_space_SE_fail_nsc_semi_fully_periodic():
sq = Geometry([0] * 3, Atom(1, 1.01), [1])
sq.set_nsc([3, 5, 3])
H = Hamiltonian(sq)
H.construct([(0.1, 1.1), (4, -1)])
with pytest.raises(ValueError):
RSE = RealSpaceSE(H, 1, 0, (3, 4, 1), dk=100)
def test_distance7(self):
# Create a 1D chain
geom = Geometry([0] * 3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 1, 1])
# Try with a short R and a long tolerance list
d = geom.distance(R=1, tol=np.ones(10) * .5)
assert_equal(len(d), 1)
assert_true(np.allclose(d, [1.]))
def test_distance8(self, setup):
geom = Geometry([0]*3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 1, 1])
d = geom.distance(0, method='min')
assert len(d) == 1
d = geom.distance(0, method='median')
assert len(d) == 1
d = geom.distance(0, method='mode')
assert len(d) == 1
def test_distance7(self, setup):
# Create a 1D chain
geom = Geometry([0]*3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 1, 1])
# Try with a short R and a long tolerance list
# We know that the tolerance list prevails, because
d = geom.distance(R=1, tol=np.ones(10) * .5)
assert len(d) == 1
assert np.allclose(d, [1.])
def test_real_space_SE_fail_k_trs():
sq = Geometry([0] * 3, Atom(1, 1.01), [1])
sq.set_nsc([3] * 3)
H = Hamiltonian(sq)
H.construct([(0.1, 1.1), (4, -1)])
RSE = RealSpaceSE(H, 0, 1, (3, 4, 1))
with pytest.raises(ValueError):
RSE.green(0.1, [0, 0, 0.2])
def test_berry_phase_method_fail(self):
g = Geometry([[-.6, 0, 0], [0.6, 0, 0]], Atom(1, 1.001), sc=[2, 10, 10])
g.set_nsc([3, 1, 1])
H = Hamiltonian(g)
H.construct([(0.1, 1.0, 1.5), (0, 1., 0.5)])
# Contour
k = np.linspace(0.0, 1.0, 101)
K = np.zeros([k.size, 3])
K[:, 0] = k
bz = BrillouinZone(H, K)
berry_phase(bz, method='unknown')
def test_optimize_nsc1(self):
# Create a 1D chain
geom = Geometry([0] * 3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 77, 77])
assert_true(np.allclose(geom.optimize_nsc(), [3, 3, 3]))
geom.set_nsc([77, 77, 77])
assert_true(np.allclose(geom.optimize_nsc(1), [77, 3, 77]))
geom.set_nsc([77, 77, 77])
assert_true(np.allclose(geom.optimize_nsc([0, 2]), [3, 77, 3]))
geom.set_nsc([77, 77, 77])
assert_true(np.allclose(geom.optimize_nsc([0, 2], R=2), [5, 77, 5]))
geom.set_nsc([1, 1, 1])
assert_true(np.allclose(geom.optimize_nsc([0, 2], R=2), [5, 1, 5]))
def test_berry_phase_zak(self):
# SSH model, topological cell
g = Geometry([[-.6, 0, 0], [0.6, 0, 0]], Atom(1, 1.001), sc=[2, 10, 10])
g.set_nsc([3, 1, 1])
H = Hamiltonian(g)
H.construct([(0.1, 1.0, 1.5), (0, 1., 0.5)])
# Contour
k = np.linspace(0.0, 1.0, 101)
K = np.zeros([k.size, 3])
K[:, 0] = k
bz = BrillouinZone(H, K)
assert np.allclose(np.abs(berry_phase(bz, sub=0, method='zak')), np.pi)
# Just to do the other branch
berry_phase(bz, method='zak')
def test_repeat3(self):
R, param = [0.1, 1.1, 2.1, 3.1], [1., 2., 3., 4.]
# Create reference
g = Geometry([[0] * 3], Atom('H', R=[4.]), sc=[1.] * 3)
g.set_nsc([7] * 3)
# Now create bigger geometry
G = g.repeat(2, 0).repeat(2, 1).repeat(2, 2)
HG = Hamiltonian(G.repeat(2, 0).repeat(2, 1).repeat(2, 2))
HG.construct([R, param])
HG.finalize()
H = Hamiltonian(G)
H.construct([R, param])
H.finalize()
H = H.repeat(2, 0).repeat(2, 1).repeat(2, 2)
assert_true(HG.spsame(H))
def test_real_space_HS_SE_unfold_with_k():
# check that calculating the real-space Green function is equivalent for two equivalent systems
sq = Geometry([0] * 3, Atom(1, 1.01), [1])
sq.set_nsc([3] * 3)
H = Hamiltonian(sq)
H.construct([(0.1, 1.1), (4, -1)])
RSE = RealSpaceSE(H, 0, 1, (3, 4, 1), dk=100, trs=False)
k1 = [0, 0, 0.2]
k2 = [0, 0, 0.3]
for E in [0.1, 1.5]:
G1 = RSE.green(E, k1)
G2 = RSE.green(E, k2)
assert not np.allclose(G1, G2)
SE1 = RSE.self_energy(E, k1)
SE2 = RSE.self_energy(E, k2)
assert not np.allclose(SE1, SE2)
def test_tile3(self, setup):
R, param = [0.1, 1.1, 2.1, 3.1], [1., 2., 3., 4.]
# Create reference
g = Geometry([[0] * 3], Atom('H', R=[4.]), sc=[1.] * 3)
g.set_nsc([7] * 3)
# Now create bigger geometry
G = g.tile(2, 0).tile(2, 1).tile(2, 2)
HG = Hamiltonian(G.tile(2, 0).tile(2, 1).tile(2, 2))
HG.construct([R, param])
HG.finalize()
H = Hamiltonian(G)
H.construct([R, param])
H.finalize()
H = H.tile(2, 0).tile(2, 1).tile(2, 2)
assert HG.spsame(H)
H.finalize()
HG.finalize()
assert np.allclose(H._csr._D, HG._csr._D)
def test_distance6(self, setup):
# Create a 1D chain
geom = Geometry([0]*3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 1, 1])
d = geom.distance(0)
assert len(d) == 1
assert np.allclose(d, [1.])
# Do twice
d = geom.distance(R=2)
assert len(d) == 2
assert np.allclose(d, [1., 2.])
# Do all
d = geom.distance(R=np.inf)
assert len(d) == 77 // 2
# Add one due arange not adding the last item
assert np.allclose(d, range(1, 78 // 2))
# Create a 2D grid
geom.set_nsc([3, 3, 1])
d = geom.distance(R=2, tol=[.4, .3, .2, .1])
assert len(d) == 2 # 1, sqrt(2)
# Add one due arange not adding the last item
assert np.allclose(d, [1, 2 ** .5])
# Create a 2D grid
geom.set_nsc([5, 5, 1])
d = geom.distance(R=2, tol=[.4, .3, .2, .1])
assert len(d) == 3 # 1, sqrt(2), 2
# Add one due arange not adding the last item
assert np.allclose(d, [1, 2 ** .5, 2])
def test_optimize_nsc1(self, setup):
# Create a 1D chain
geom = Geometry([0]*3, Atom(1, R=1.), sc=1)
geom.set_nsc([77, 77, 77])
assert np.allclose(geom.optimize_nsc(), [3, 3, 3])
geom.set_nsc([77, 77, 77])
assert np.allclose(geom.optimize_nsc(1), [77, 3, 77])
geom.set_nsc([77, 77, 77])
assert np.allclose(geom.optimize_nsc([0, 2]), [3, 77, 3])
geom.set_nsc([77, 77, 77])
assert np.allclose(geom.optimize_nsc([0, 2], R=2.00000001), [5, 77, 5])
geom.set_nsc([1, 1, 1])
assert np.allclose(geom.optimize_nsc([0, 2], R=2.0000001), [5, 1, 5])
geom.set_nsc([5, 1, 5])
assert np.allclose(geom.optimize_nsc([0, 2], R=0.9999), [1, 1, 1])
