def test_decrease_difference_cluster(self):
S1, S2 = read_vasp('POSCAR1',
iscluster=True), read_vasp('POSCAR2',
iscluster=True)
SD = StructureDifference(S1, S2)
A, B = SD.get_structure_decdistance_diff(2)
self.assertEqual(np.shape(B)[0], 60)
def test_decrease_difference_periodic(self):
S1 = read_vasp('POSCAR_BN_2')
S2 = read_vasp('POSCAR_BN_12.vasp')
SD = StructureDifference(S1, S2)
A, B = SD.get_structure_decdistance_diff(Rcut=2)
self.assertLessEqual(A, 1e-4)
def test_graphene(self):
S = read_vasp('primitive_cell_cart.vasp')
self.assertEqual(len(S.get_equivalent_atoms()), 1)
def test_eigen_spectral(self):
S = read_vasp('POSCAR_BN_12.vasp')
S.draw_eig_spectra(atom_seq=range(12))
def test_direct(self):
S = read_vasp('primitive_cell_direct.vasp')
self.assertEqual(np.shape(S.positions)[0], 8)
def get_structure_decdistance_diff(self, Rcut=5):
d1 = self._structure1.get_decrease_distance_matrix(Rcut=Rcut)
d2 = self._structure2.get_decrease_distance_matrix(Rcut=Rcut)
d = np.zeros((len(d1), len(d2)))
for I in range(len(d1)):
for J in range(len(d2)):
eigval1, eigvec1 = ftk.get_EPA(d1[I])
eigval2, eigvec2 = ftk.get_EPA(d2[J])
n1, n2 = np.shape(eigvec1)[0], np.shape(eigvec2)[0]
dd = np.zeros((n1, n2))
for ii in range(n1):
for jj in range(n2):
dd[ii, jj] = ftk.d_EPF_atom(eigval1, eigval2,
eigvec1[ii], eigvec2[jj])
row_ind, col_ind = linear_sum_assignment(dd)
d[I, J] = dd[row_ind, col_ind].sum()
row_ind, col_ind = linear_sum_assignment(d)
corresponding_sequence = np.array(list(zip(row_ind, col_ind)))
return d[row_ind, col_ind].sum(), corresponding_sequence[np.argsort(
corresponding_sequence[:, 0])]
if __name__ == "__main__":
from eigprofuc.io.vasp import read_vasp
s = read_vasp('/home/hecc/Desktop/POSCAR14.vasp')
import time
a = time.time()
for i in range(10):
d1 = s.get_atomsdistance_decdistance(Rcut=3, isparallel=True)
print(time.time() - a)