def __init__(self):
self.title = "Title"
self.scaling = 1.0
self.cell = cell.Cell(np.identity(3))
self.atoms = ['A']
self.atom_numbers = [1]
self.coordinate_type = 'Direct'
self.coordinates = np.array([[0.0, 0.0, 0.0]])
self.selective_dynamics = False
def to_configuration(self):
atoms = [
atom.Atom(label, coordinates)
for (label, coordinates
) in zip(self.labels(), self.fractional_coordinates())
]
config = configuration.Configuration(
cell.Cell(matrix=self.cell.matrix * self.scaling), atoms)
return (config)
def interpolate_to_orthorhombic_grid( self, dimensions ): # warning. This may need a more robust minimim image function in Cell.py for highly non-orthorhombic cells
old_grid = self
old_cell = old_grid.poscar.cell
new_grid = Grid( dimensions = dimensions )
new_grid.poscar.cell = cell.Cell( np.diag( np.diag( self.poscar.cell.matrix ) ) )
index_grid = np.array( [ [ i, j, k ] for ( i, j, k ), value in np.ndenumerate( new_grid.grid ) ] )
cart_coord_grid = np.array( [ new_grid.cartesian_coordinate_at_index( index ) for index in index_grid ] )
init_frac_coord_grid = np.array( [ old_cell.cartesian_to_fractional_coordinates( r ) for r in cart_coord_grid ] )
frac_coord_grid = np.array( [ old_cell.inside_cell( r ) for r in init_frac_coord_grid ] )
new_grid_data = np.array( [ old_grid.interpolated_value_at_fractional_coordinate( r ) for r in frac_coord_grid ] )
new_grid.grid = new_grid_data.reshape( new_grid.dimensions )
return( new_grid )
def swap_axes(self, axes):
new_poscar = copy.deepcopy(self)
new_poscar.cell = cell.Cell(swap_axes(self.cell.matrix, axes))
return new_poscar