def read_es(self, **kwargs):
""" Returns the electronic structure from the siesta.TSHS file """
# First read the geometry
geom = self.read_geom()
# Now read the sizes used...
sizes = _siesta.read_tshs_sizes(self.file)
spin = sizes[0]
no = sizes[2]
nnz = sizes[4]
ncol, col, dH, dS = _siesta.read_tshs_es(self.file, spin, no, nnz)
# Create the Hamiltonian container
H = Hamiltonian(geom, nnzpr=1, orthogonal=False, spin=spin)
# Create the new sparse matrix
H._data.ncol = np.array(ncol, np.int32)
ptr = np.cumsum(ncol)
ptr = np.insert(ptr, 0, 0)
H._data.ptr = np.array(ptr, np.int32)
# Correct fortran indices
H._data.col = np.array(col, np.int32) - 1
H._data._nnz = len(col)
H._data._D = np.empty([nnz, spin + 1], np.float64)
for i in range(spin):
# this is because of the F-ordering
H._data._D[:, i] = dH[:, i]
H._data._D[:, spin] = dS[:]
return H
def read_es(self, **kwargs):
""" Returns a tight-binding model from the underlying NetCDF file """
# Get the default spin channel
ispin = kwargs.get('ispin', -1)
spin = 1
if ispin == -1:
spin = len(self._dimension('spin'))
# First read the geometry
geom = self.read_geom()
# Populate the things
sp = self._crt_grp(self, 'SPARSE')
v = sp.variables['isc_off']
# pre-allocate the super-cells
geom.sc.set_nsc(np.amax(v[:, :], axis=0) * 2 + 1)
geom.sc.sc_off[:, :] = v[:, :]
# Now create the tight-binding stuff (we re-create the
# array, hence just allocate the smallest amount possible)
ham = Hamiltonian(geom, nnzpr=1, orthogonal=False, spin=spin)
# Use Ef to move H to Ef = 0
Ef = float(self._value('Ef')[0]) * Ry2eV**ham._E_order
S = np.array(sp.variables['S'][:], np.float64)
ncol = np.array(sp.variables['n_col'][:], np.int32)
# Update maximum number of connections (in case future stuff happens)
ptr = np.append(np.array(0, np.int32), np.cumsum(ncol)).flatten()
col = np.array(sp.variables['list_col'][:], np.int32) - 1
# Copy information over
ham._data.ncol = ncol
ham._data.ptr = ptr
ham._data.col = col
ham._nnz = len(col)
# Create new container
H = np.array(sp.variables['H'][ispin, :],
np.float64) * Ry2eV**ham._E_order
# Correct for the Fermi-level, Ef == 0
H -= Ef * S[:]
ham._data._D = np.empty([ham._data.ptr[-1], spin + 1], np.float64)
if ispin == -1:
for i in range(spin):
# Create new container
H = np.array(sp.variables['H'][i, :],
np.float64) * Ry2eV**ham._E_order
# Correct for the Fermi-level, Ef == 0
H -= Ef * S[:]
ham._data._D[:, i] = H[:]
else:
# Create new container
H = np.array(sp.variables['H'][ispin, :],
np.float64) * Ry2eV**ham._E_order
# Correct for the Fermi-level, Ef == 0
H -= Ef * S[:]
ham._data._D[:, 0] = H[:]
ham._data._D[:, ham.S_idx] = S[:]
return ham
