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 _r_geometry_sisl(self, na, header, sp, xyz):
""" Read the geometry as though it was created with sisl """
# Default version of the header is 1
v = header.get("sisl-version", 1)
nsc = list(map(int, header.pop("nsc").split()))
cell = _a.fromiterd(header.pop("cell").split()).reshape(3, 3)
return Geometry(xyz, atom=sp, sc=SuperCell(cell, nsc=nsc))
def _r_geometry_ase(self, na, header, sp, xyz):
""" Read the geometry as though it was created with ASE """
# Convert F T to nsc
# F = 1
# T = 3
nsc = list(map(lambda x: "FT".index(x) * 2 + 1, header.pop("pbc").strip('"').split()))
cell = _a.fromiterd(header.pop("Lattice").strip('"').split()).reshape(3, 3)
return Geometry(xyz, atom=sp, sc=SuperCell(cell, nsc=nsc))
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_data(self, *args, **kwargs):
""" Read tabular data from the file.
Parameters
----------
columns : list of int, optional
only return the indices of the columns that are provided
delimiter : str, optional
the delimiter used in the file, will automatically try to guess if not specified
ret_comment : bool, optional
also return the comments at the top of the file (if queried)
ret_header : bool, optional
also return the header information (if queried)
comment : str, optional
lines starting with this are discarded as comments
"""
# Override the comment in the file
self._comment = [kwargs.get('comment', self._comment[0])]
# Skip to next line
comment = []
header = ''
# Also read comments
line = self.readline(True)
while line.startswith(self._comment[0] + ' '):
comment.append(line)
line = self.readline(True)
if line.startswith(self._comment[0]):
header = line
line = self.readline()
# Now we are ready to read the data
dat = [[]]
# First we need to figure out the separator:
len_sep = 0
sep = kwargs.get('delimiter', '')
if len(sep) == 0:
for cur_sep in ['\t', ' ', ',']:
s = line.split(cur_sep)
if len(s) > len_sep:
len_sep = len(s)
sep = cur_sep
if len(sep) == 0:
raise ValueError(self.__class__.__name__ +
'.read_data could not determine '
'column separator...')
empty = re.compile(r'\s*\n')
while len(line) > 0:
# If we start a line by a comment, or a newline
# then we have a new data set
if empty.match(line) is not None:
if len(dat[-1]) > 0:
dat[-1] = _a.asarrayd(dat[-1])
dat.append([])
else:
line = [l for l in line.split(sep) if len(l) > 0]
dat[-1].append(_a.fromiterd(map(float, line)))
line = self.readline()
if len(dat[-1]) > 0:
dat[-1] = _a.asarrayd(dat[-1])
# Ensure we have no false positives
dat = _a.asarrayd([d for d in dat if len(d) > 0])
if dat.shape[0] == 1:
s = list(dat.shape)
s.pop(0)
dat.shape = tuple(s)
if dat.ndim == 2:
if dat.shape[1] == 1:
# surely a 1D data
dat.shape = (-1, )
# For 2D data we need to transpose because the data is
# read row wise, but stored column wise
if dat.ndim > 1:
dat = np.swapaxes(dat, -2, -1)
ret_comment = kwargs.get('ret_comment', False)
ret_header = kwargs.get('ret_header', False)
if ret_comment:
if ret_header:
return dat, comment, header
return dat, comment
elif ret_header:
return dat, header
return dat
