def _get_tmat_cell(cell, options):
t_mat = np.array([frac2val(x) for x in options.t_mat.split()])
t_mat = _get_matrix(t_mat)
if t_mat is False:
sys.stderr.write("Transformation matrix is not correctly set.\n")
return False
else:
if options.is_verbose:
print "Transform cell using transformation matrix:"
print t_mat
return reduce_points(t_mat, cell)
def _get_tmat_cell(cell, options):
t_mat = np.array([frac2val(x) for x in options.t_mat.split()])
t_mat = _get_matrix(t_mat)
if t_mat is False:
sys.stderr.write("Transformation matrix is not correctly set.\n")
return False
else:
if options.is_verbose:
print "Transform cell using transformation matrix:"
print t_mat
return reduce_points(t_mat, cell)
def get_supercell(cell, supercell_matrix, tolerance=1e-5):
"""
Build supercell with supercell matrix as follows:
1) Create supercell with surrounding simple lattice.
2) Trim the surrounding supercell with the target lattice.
"""
smat = np.array(supercell_matrix)
frame = get_smallest_surrounding_lattice_multiplicities(smat)
surrounding_cell = get_simple_supercell(frame, cell)
if (np.diag(np.diagonal(smat)) == smat).all():
return surrounding_cell
transformation_matrix = np.array([smat[0] / float(frame[0]),
smat[1] / float(frame[1]),
smat[2] / float(frame[2])])
supercell = reduce_points(transformation_matrix,
surrounding_cell,
tolerance)
return supercell
