def form_object(self) -> NoReturn:
coord_xyz = self.coord_xyz
if coord_xyz is None:
return False
r, b = transform_string_to_r_b(coord_xyz, labels=("x", "y", "z"))
self.__dict__["r"] = r
self.__dict__["b"] = b
it_coord_xyz = self.it_coord_xyz
centring_type = self.centring_type
shift = get_shift_by_centring_type(centring_type)
if it_coord_xyz is not None:
full_r, full_b = [], []
full_coord_xyz = []
for _coord_xyz in it_coord_xyz:
r, b = transform_string_to_r_b(_coord_xyz,
labels=("x", "y", "z"))
for _shift in shift:
b_new = numpy.mod(b + numpy.array(_shift, dtype=Fraction),
1)
_symop = transform_r_b_to_string(r,
b_new,
labels=("x", "y", "z"))
full_coord_xyz.append(_symop)
full_r.append(r)
full_b.append(b_new)
self.__dict__["full_r"] = full_r
self.__dict__["full_b"] = full_b
self.__dict__["full_coord_xyz"] = full_coord_xyz
def get_symops_by_generator_xyz(self, generator_xyz: str) -> Tuple[str]:
r, b = self.r, self.b
symop = []
W_g, w_g = transform_string_to_r_b(generator_xyz,
labels=("x", "y", "z"))
flag = True
W_i = copy.deepcopy(r)
w_i = copy.deepcopy(b) % 1
_symop = transform_r_b_to_string(W_i, w_i, labels=("x", "y", "z"))
symop.append(_symop)
i_stop = 1
while flag:
i_stop += 1
w_i = (mult_matrix_vector(W_g, w_i) + w_g) % 1 # w3 = W2 x w1 + w2
W_i = mult_matrixes(W_g, W_i) # W3 = W2 x W1
_symop = transform_r_b_to_string(W_i, w_i, labels=("x", "y", "z"))
if ((_symop in symop) | (i_stop > 100)):
flag = False
else:
symop.append(_symop)
res = []
gen_orig = ""
if not (self.generator_xyz is None):
gen_orig = f"{self.generator_xyz:}"
res = [
SpaceGroupSymop(id=f"{_i+1:}",
operation_xyz=_symop,
generator_xyz=f"{gen_orig:}_{generator_xyz:}")
for _i, _symop in enumerate(symop)
]
return res
def form_object(self) -> NoReturn:
operation_xyz = self.operation_xyz
if operation_xyz is None:
return False
r, b = transform_string_to_r_b(operation_xyz, labels=("x", "y", "z"))
self.__dict__["r"] = r
self.__dict__["b"] = b
self.__dict__["r_11"] = r[0, 0]
self.__dict__["r_12"] = r[0, 1]
self.__dict__["r_13"] = r[0, 2]
self.__dict__["r_21"] = r[1, 0]
self.__dict__["r_22"] = r[1, 1]
self.__dict__["r_23"] = r[1, 2]
self.__dict__["r_31"] = r[2, 0]
self.__dict__["r_32"] = r[2, 1]
self.__dict__["r_33"] = r[2, 2]
self.__dict__["b_1"] = b[0]
self.__dict__["b_2"] = b[1]
self.__dict__["b_3"] = b[2]
def form_object(self) -> NoReturn:
"""Form object."""
xyz = self.xyz
if xyz is None:
return False
r, b = transform_string_to_r_b(xyz, labels=("x", "y", "z"))
r_11, r_12, r_13 = int(r[0, 0]), int(r[0, 1]), int(r[0, 2])
r_21, r_22, r_23 = int(r[1, 0]), int(r[1, 1]), int(r[1, 2])
r_31, r_32, r_33 = int(r[2, 0]), int(r[2, 1]), int(r[2, 2])
den_1, den_2 = b[0].denominator, b[1].denominator
den_3 = b[2].denominator
num_1, num_2 = b[0].numerator, b[1].numerator
num_3 = b[2].numerator
den = numpy.lcm.reduce([den_1, den_2, den_3])
num_1 *= den // den_1
num_2 *= den // den_2
num_3 *= den // den_3
theta = int(b[3])
self.__dict__["r_11"] = r_11
self.__dict__["r_12"] = r_12
self.__dict__["r_13"] = r_13
self.__dict__["r_21"] = r_21
self.__dict__["r_22"] = r_22
self.__dict__["r_23"] = r_23
self.__dict__["r_31"] = r_31
self.__dict__["r_32"] = r_32
self.__dict__["r_33"] = r_33
self.__dict__["b_1"] = b[0]
self.__dict__["b_2"] = b[1]
self.__dict__["b_3"] = b[2]
self.__dict__["theta"] = theta
self.__dict__["sym_elem"] = numpy.array([
num_1, num_2, num_3, den, r_11, r_12, r_13, r_21, r_22, r_23, r_31,
r_32, r_33, theta * r_11, theta * r_12, theta * r_13, theta * r_21,
theta * r_22, theta * r_23, theta * r_31, theta * r_32,
theta * r_33
],
dtype=int)
def calc_susc_i(self, atom_site_susceptibility: AtomSiteSusceptibilityL):
"""Calculate susceptibility of point i.
Parameters
----------
atom_site_susceptibility : AtomSiteSusceptibilityL
DESCRIPTION.
Returns
-------
susc_11 : TYPE
DESCRIPTION.
susc_22 : TYPE
DESCRIPTION.
susc_33 : TYPE
DESCRIPTION.
susc_12 : TYPE
DESCRIPTION.
susc_13 : TYPE
DESCRIPTION.
susc_23 : TYPE
DESCRIPTION.
"""
numpy_basin_atom_label = self.numpy_basin_atom_label
numpy_basin_atom_symop = self.numpy_basin_atom_symop
if numpy_basin_atom_label is None:
numpy_basin_atom_label = numpy.array(self.basin_atom_label,
dtype=str)
numpy_basin_atom_symop = numpy.array(self.basin_atom_symop,
dtype=str)
self.numpy_basin_atom_label = numpy_basin_atom_label
self.numpy_basin_atom_symop = numpy_basin_atom_symop
np_label = numpy.array(atom_site_susceptibility.label, dtype=str)
np_chi_11 = numpy.array(atom_site_susceptibility.chi_11, dtype=float)
np_chi_22 = numpy.array(atom_site_susceptibility.chi_22, dtype=float)
np_chi_33 = numpy.array(atom_site_susceptibility.chi_33, dtype=float)
np_chi_12 = numpy.array(atom_site_susceptibility.chi_12, dtype=float)
np_chi_13 = numpy.array(atom_site_susceptibility.chi_13, dtype=float)
np_chi_23 = numpy.array(atom_site_susceptibility.chi_23, dtype=float)
l_r = []
for symop in numpy_basin_atom_symop:
r, b = transform_string_to_r_b(symop)
r = r.astype(float)
l_r.append((r[0, 0], r[0, 1], r[0, 2], r[1, 0], r[1, 1], r[1, 2],
r[2, 0], r[2, 1], r[2, 2]))
np_r = numpy.array(l_r, dtype=float)
np_r_11, np_r_12, np_r_13 = np_r[:, 0], np_r[:, 1], np_r[:, 2]
np_r_21, np_r_22, np_r_23 = np_r[:, 3], np_r[:, 4], np_r[:, 5]
np_r_31, np_r_32, np_r_33 = np_r[:, 6], np_r[:, 7], np_r[:, 8]
susc_11 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
susc_22 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
susc_33 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
susc_12 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
susc_13 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
susc_23 = numpy.zeros(numpy_basin_atom_label.size, dtype=float)
for _l, chi_11, chi_22, chi_33, chi_12, chi_13, chi_23 in \
zip(np_label, np_chi_11, np_chi_22, np_chi_33, np_chi_12,
np_chi_13, np_chi_23):
f_1 = numpy_basin_atom_label == _l
chi_ij = (chi_11, chi_12, chi_13, chi_12, chi_22, chi_23,
chi_13, chi_23, chi_33)
c_11, c_12, c_13, c_21, c_22, c_23, c_31, c_32, c_33 = \
calc_mRmCmRT((np_r_11[f_1], np_r_12[f_1], np_r_13[f_1],
np_r_21[f_1], np_r_22[f_1], np_r_23[f_1],
np_r_31[f_1], np_r_32[f_1], np_r_33[f_1]),
chi_ij)
susc_11[f_1] = c_11
susc_22[f_1] = c_22
susc_33[f_1] = c_33
susc_12[f_1] = c_12
susc_13[f_1] = c_13
susc_23[f_1] = c_23
l_rs_i = self.l_rs_i
l_c_11, l_c_22, l_c_33, l_c_12, l_c_13, l_c_23 = [], [], [], [], [], []
for s_11, s_22, s_33, s_12, s_13, s_23, rs_i in zip(
susc_11, susc_22, susc_33, susc_12, susc_13, susc_23, l_rs_i):
r_11, r_12, r_13, r_21, r_22, r_23, r_31, r_32, r_33 = rs_i
c_11, c_12, c_13, c_21, c_22, c_23, c_31, c_32, c_33 = \
calc_mRmCmRT(rs_i, (s_11, s_12, s_13,
s_12, s_22, s_23,
s_13, s_23, s_33))
ns = c_11.size
l_c_11.append(c_11.sum()/float(ns))
l_c_22.append(c_22.sum()/float(ns))
l_c_33.append(c_33.sum()/float(ns))
l_c_12.append(c_12.sum()/float(ns))
l_c_13.append(c_13.sum()/float(ns))
l_c_23.append(c_23.sum()/float(ns))
chi_11 = numpy.array(l_c_11, dtype=float)
chi_22 = numpy.array(l_c_22, dtype=float)
chi_33 = numpy.array(l_c_33, dtype=float)
chi_12 = numpy.array(l_c_12, dtype=float)
chi_13 = numpy.array(l_c_13, dtype=float)
chi_23 = numpy.array(l_c_23, dtype=float)
return chi_11, chi_22, chi_33, chi_12, chi_13, chi_23
