def read_supercell(self):
""" Reads a supercell from the Sile """
# 1st line is number of supercells
nsc = _a.fromiteri(map(int, self.readline().split()[:3]))
self.readline() # natoms, nspecies
self.readline() # species
cell = _a.fromiterd(map(float, self.readline().split()[:9]))
# Typically ScaleUp uses very large unit-cells
# so supercells will typically be restricted to [3, 3, 3]
return SuperCell(cell * Bohr2Ang, nsc=nsc)
def read_geometry(self, primary=False, **kwargs):
""" Reads a geometry from the Sile """
# 1st line is number of supercells
nsc = _a.fromiteri(map(int, self.readline().split()[:3]))
na, ns = map(int, self.readline().split()[:2])
# Convert species to atom objects
try:
species = get_sile(self.file.rsplit('REF', 1)[0] +
'orbocc').read_atom()
except:
species = [Atom(s) for s in self.readline().split()[:ns]]
# Total number of super-cells
if primary:
# Only read in the primary unit-cell
ns = 1
else:
ns = np.prod(nsc)
cell = _a.fromiterd(map(float, self.readline().split()))
try:
cell.shape = (3, 3)
if primary:
cell[0, :] /= nsc[0]
cell[1, :] /= nsc[1]
cell[2, :] /= nsc[2]
except:
c = np.empty([3, 3], np.float64)
c[0, 0] = 1. + cell[0]
c[0, 1] = cell[5] / 2.
c[0, 2] = cell[4] / 2.
c[1, 0] = cell[5] / 2.
c[1, 1] = 1. + cell[1]
c[1, 2] = cell[3] / 2.
c[2, 0] = cell[4] / 2.
c[2, 1] = cell[3] / 2.
c[2, 2] = 1. + cell[2]
cell = c * Ang2Bohr
sc = SuperCell(cell * Bohr2Ang, nsc=nsc)
# Create list of coordinates and atoms
xyz = np.empty([na * ns, 3], np.float64)
atoms = [None] * na * ns
# Read the geometry
for ia in range(na * ns):
# Retrieve line
# ix iy iz ia is x y z
line = self.readline().split()
atoms[ia] = species[int(line[4]) - 1]
xyz[ia, :] = _a.fromiterd(map(float, line[5:8]))
return Geometry(xyz * Bohr2Ang, atoms, sc=sc)
def Neumann(idx_bc, idx_p1):
# TODO check this BC
# Set all boundary equations to 0
s = array_arange(A.indptr[idx_bc], A.indptr[idx_bc+1])
A.data[s] = 0
# force the boundary cells to equal the neighbouring cell
A[idx_bc, idx_bc] = -C # I am not sure this is correct, but setting it to 0 does NOT work
A[idx_bc, idx_p1] = C
# ensure the neighbouring cell doesn't connect to the boundary (no back propagation)
A[idx_p1, idx_bc] = 0
# Ensure the sum of the source for the neighbouring cells equals 0
# To make it easy to figure out the diagonal elements we first
# set it to 0, then sum off-diagonal terms, and set the diagonal
# equal to the sum.
A[idx_p1, idx_p1] = 0
n = A.indptr[idx_p1+1] - A.indptr[idx_p1]
s = array_arange(A.indptr[idx_p1], n=n)
n = np.split(A.data[s], np.cumsum(n)[:-1])
n = _a.fromiteri(map(np.sum, n))
# update diagonal
A[idx_p1, idx_p1] = -n
del s, n
A.eliminate_zeros()
b[idx_bc] = 0.
