def from_supercell(cls, atoms, supercell, grid_type, value=None, folder='kALDo'):
_direct_grid = Grid(supercell, grid_type)
replicated_positions = _direct_grid.grid(is_wrapping=False).dot(atoms.cell)[:, np.newaxis, :] + atoms.positions[
np.newaxis, :, :]
inst = cls(atoms=atoms,
replicated_positions=replicated_positions,
supercell=supercell,
value=value,
folder=folder)
inst._direct_grid = _direct_grid
return inst
def __init__(self, *kargs, **kwargs):
Observable.__init__(self, *kargs, **kwargs)
self.atoms = kwargs['atoms']
replicated_positions = kwargs['replicated_positions']
self.supercell = kwargs['supercell']
try:
self.value = kwargs['value']
except KeyError:
self.value = None
self._replicated_atoms = None
self.replicated_positions = replicated_positions.reshape(
(-1, self.atoms.positions.shape[0], self.atoms.positions.shape[1]))
self.n_replicas = np.prod(self.supercell)
self._cell_inv = None
self._replicated_cell_inv = None
self._list_of_replicas = None
n_replicas, n_unit_atoms, _ = self.replicated_positions.shape
atoms_positions = self.atoms.positions
detected_grid = np.round(
(replicated_positions.reshape((n_replicas, n_unit_atoms, 3)) -
atoms_positions[np.newaxis, :, :]).dot(
np.linalg.inv(self.atoms.cell))[:, 0, :], 0).astype(np.int)
grid_c = Grid(grid_shape=self.supercell, order='C')
grid_fortran = Grid(grid_shape=self.supercell, order='F')
if (grid_c.grid(is_wrapping=False) == detected_grid).all():
grid_type = 'C'
logging.debug("Using C-style position grid")
elif (grid_fortran.grid(is_wrapping=False) == detected_grid).all():
grid_type = 'F'
logging.debug("Using fortran-style position grid")
else:
err_msg = "Unable to detect grid type"
logging.error(err_msg)
raise TypeError(err_msg)
self._direct_grid = Grid(self.supercell, grid_type)
class ForceConstant(Observable):
def __init__(self, *kargs, **kwargs):
Observable.__init__(self, *kargs, **kwargs)
self.atoms = kwargs['atoms']
replicated_positions = kwargs['replicated_positions']
self.supercell = kwargs['supercell']
try:
self.value = kwargs['value']
except KeyError:
self.value = None
self._replicated_atoms = None
self.replicated_positions = replicated_positions.reshape(
(-1, self.atoms.positions.shape[0], self.atoms.positions.shape[1]))
self.n_replicas = np.prod(self.supercell)
self._cell_inv = None
self._replicated_cell_inv = None
self._list_of_replicas = None
n_replicas, n_unit_atoms, _ = self.replicated_positions.shape
atoms_positions = self.atoms.positions
detected_grid = np.round(
(replicated_positions.reshape((n_replicas, n_unit_atoms, 3)) - atoms_positions[np.newaxis, :, :]).dot(
np.linalg.inv(self.atoms.cell))[:, 0, :], 0).astype(np.int)
grid_c = Grid(grid_shape=self.supercell, order='C')
grid_fortran = Grid(grid_shape=self.supercell, order='F')
if (grid_c.grid(is_wrapping=False) == detected_grid).all():
grid_type = 'C'
logging.debug("Using C-style position grid")
elif (grid_fortran.grid(is_wrapping=False) == detected_grid).all():
grid_type = 'F'
logging.debug("Using fortran-style position grid")
else:
err_msg = "Unable to detect grid type"
logging.error(err_msg)
raise TypeError(err_msg)
self._direct_grid = Grid(self.supercell, grid_type)
@classmethod
def from_supercell(cls, atoms, supercell, grid_type, value=None, folder='kALDo'):
_direct_grid = Grid(supercell, grid_type)
replicated_positions = _direct_grid.grid(is_wrapping=False).dot(atoms.cell)[:, np.newaxis, :] + atoms.positions[
np.newaxis, :, :]
inst = cls(atoms=atoms,
replicated_positions=replicated_positions,
supercell=supercell,
value=value,
folder=folder)
inst._direct_grid = _direct_grid
return inst
@property
def positions(self):
return self.atoms.positions
@property
def cell_inv(self):
if self._cell_inv is None:
self._cell_inv = np.linalg.inv(self.atoms.cell)
return self._cell_inv
@property
def replicated_atoms(self):
# TODO: remove this method
if self._replicated_atoms is None:
supercell = self.supercell
atoms = self.atoms
replicated_atoms = atoms.copy() * supercell
replicated_positions = self._direct_grid.grid(is_wrapping=False).dot(atoms.cell)[:, np.newaxis, :] + atoms.positions[
np.newaxis, :, :]
replicated_atoms.set_positions(replicated_positions.reshape(-1, 3))
self._replicated_atoms = replicated_atoms
return self._replicated_atoms
@property
def replicated_cell_inv(self):
if self._replicated_cell_inv is None:
self._replicated_cell_inv = np.linalg.inv(self.replicated_atoms.cell)
return self._replicated_cell_inv
@property
def list_of_replicas(self):
if self._list_of_replicas is None:
list_of_index = self._direct_grid.grid(is_wrapping=True)
self._list_of_replicas = list_of_index.dot(self.atoms.cell)
return self._list_of_replicas
def _chi_k(self, k_points):
n_k_points = np.shape(k_points)[0]
ch = np.zeros((n_k_points, self.n_replicas), dtype=np.complex)
for index_q in range(n_k_points):
k_point = k_points[index_q]
list_of_replicas = self.list_of_replicas
cell_inv = self.cell_inv
ch[index_q] = chi(k_point, list_of_replicas, cell_inv)
return ch