def check_polar(self):
"""
Check if the surface structure is polar.
Comparing atom types at top and bottom.
Applicable for sufcae with vaccums only.
Args:
file:atoms object (surface with vacuum)
Returns:
polar:True/False
"""
up = 0
dn = 0
coords = np.array(self.frac_coords)
z_max = max(coords[:, 2])
z_min = min(coords[:, 2])
for site, element in zip(self.frac_coords, self.elements):
if site[2] == z_max:
up = up + Specie(element).Z
if site[2] == z_min:
dn = dn + Specie(element).Z
polar = False
if up != dn:
print("Seems like a polar materials.")
polar = True
if up == dn:
print("Non-polar")
polar = False
return polar
def coulomb_matrix(atoms="", max_dim=100):
"""Convert Atoms class to max_dim x max_dim matrix.
Args:
atoms: atoms object
max_dim: maximum number of atoms=sqrt(max_dim)
Returns:
z: numpy array of 1 x max_dim dimension
"""
natoms = atoms.num_atoms
mat = np.zeros((natoms, natoms))
for ii, i in enumerate(atoms.elements):
for jj, j in enumerate(atoms.elements):
if ii == jj:
mat[ii, jj] = 0.5 * Specie(i).Z**2.4
else:
a = atoms.cart_coords[ii]
b = atoms.cart_coords[jj]
dist = np.linalg.norm(a - b)
mat[ii, jj] = (Specie(i).Z * Specie(j).Z) / dist
tmp = mat.ravel()
if max_dim < len(tmp):
print("WARNING: Increase max_dim")
padding = max_dim - len(tmp)
z = np.pad(tmp, (0, padding), "constant")
return z
def atomic_fraction_array(self):
"""Get atomic fraction array."""
nelements = len(list(Specie()._data.keys()))
frac_arr = np.zeros(nelements)
fracs = self.atomic_fraction
for i, j in fracs.items():
frac_arr[int(Specie(i).Z) - 1] = j
return frac_arr
def find_ldau_magmom(
atoms="",
default_magmom=True,
U=3.0,
mag=5.0,
amix=0.2,
bmix=0.00001,
amixmag=0.8,
bmixmag=0.00001,
lsorbit=False,
):
"""Get necessary INCAR tags for DFT+U calculations."""
sps = atoms.uniq_species
LDAUL = []
LDAUU = []
LDAUTYPE = 2
lmix = 4
for i in sps:
el = Specie(i)
el_u = 0
el_l = -1
if el.element_property("is_transition_metal"):
el_u = U
el_l = 2
if el.element_property("is_actinoid") or el.element_property(
"is_lanthanoid"):
el_u = U
el_l = 3
lmix = 6
LDAUL.append(el_l)
LDAUU.append(el_u)
if 3 in LDAUL:
LDAUTYPE = 3
nat = atoms.num_atoms
magmom = str(nat) + str("*") + str(mag)
if lsorbit:
magmom = ""
tmp = " 0 0 " + str(mag)
for i in range(0, nat):
magmom = magmom + tmp
info = {}
info["LDAU"] = ".TRUE."
info["LDAUTYPE"] = LDAUTYPE
info["LDAUL"] = " ".join(str(m) for m in LDAUL)
info["LDAUU"] = " ".join(str(m) for m in LDAUU)
info["LDAUPRINT"] = 2
info["LMAMIX"] = lmix
if not default_magmom:
info["MAGMOM"] = magmom
info["AMIX"] = amix
info["BMIX"] = bmix
info["AMIX_MAG"] = amixmag
info["BMIX_MAG"] = bmixmag
return info
def atomic_species_string(self):
"""Obtain string for QE atomic species."""
line = ""
for i in self.species:
line = line + (i + " " + str(round(Specie(i).atomic_mass, 2)) +
" " + str(self.get_psp(i)) + "\n")
return line
def packing_fraction(self):
"""Get packing fraction of the atoms object."""
total_rad = 0
for i in self.elements:
total_rad = total_rad + Specie(i).atomic_rad**3
pf = np.array([4 * np.pi * total_rad / (3 * self.volume)])
return round(pf[0], 5)
def test_sp():
el = Specie("Al")
assert (
el.Z,
round(el.atomic_mass, 2),
el.symbol,
round(el.get_chgdescrp_arr[1], 2),
round(el.get_descrp_arr[1], 2),
) == (13, 26.98, "Al", 12.17, 2792.11)
def get_center_of_mass(self):
"""Get center of mass of the atoms object."""
# atomic_mass
m = []
for i in self.elements:
m.append(Specie(i).atomic_mass)
m = np.array(m)
com = np.dot(m, self.cart_coords) / m.sum()
# com = np.linalg.solve(self.lattice_mat.T, com)
return com
def write_prismatic_xyz(atoms=None):
"""Write Atoms in prismatic format."""
from jarvis.core.specie import Specie
line = str(atoms.num_atoms) + "\n"
abc = atoms.lattice.abc
line += str(abc[0]) + " " + str(abc[1]) + " " + str(abc[2]) + "\n"
for i, j in zip(atoms.elements, atoms.cart_coords):
line += (str(Specie(i).Z) + " " + str(j[0]) + " " + str(j[1]) + " " +
str(j[2]) + str(" 1 .076\n"))
line += "-1\n"
return line
def test_sp():
el = Specie("Al")
assert (
el.Z,
round(el.atomic_mass, 2),
el.symbol,
round(el.get_chgdescrp_arr[1], 2),
round(el.get_descrp_arr[1], 2),
) == (13, 26.98, "Al", 12.17, 2792.11)
dat = get_feats_hot_encoded()
fat = get_digitized_feats_hot_encoded()
x, y, z = get_specie_data()
def reduced_formula(self):
"""Get reduced formula."""
form = ""
reduced, repeat = self.reduce()
X = {}
for i, j in reduced.items():
X[i] = Specie(i).X
Y = dict(sorted(X.items(), key=lambda item: item[1]))
Z = {}
for i, j in Y.items():
Z[i] = reduced[i]
for specie, count in Z.items():
if float(count).is_integer():
form = form + specie + str(int(count))
else:
form = form + specie + str(count)
return form.replace("1", "")
def refined_atoms(self):
"""Refine atoms based on spacegroup data."""
phonopy_atoms = (
self._atoms.lattice_mat,
self._atoms.frac_coords,
self._atoms.atomic_numbers,
)
lattice, scaled_positions, numbers = spglib.refine_cell(
phonopy_atoms, self._symprec, self._angle_tolerance)
elements = self._atoms.elements
el_dict = {}
for i in elements:
el_dict.setdefault(Specie(i).Z, i)
ref_elements = [el_dict[i] for i in numbers]
ref_atoms = Atoms(
lattice_mat=lattice,
elements=ref_elements,
coords=scaled_positions,
cartesian=False,
)
return ref_atoms
def primitive_atoms(self):
"""Get primitive atoms."""
phonopy_atoms = (
self._atoms.lattice_mat,
self._atoms.frac_coords,
self._atoms.atomic_numbers,
)
lattice, scaled_positions, numbers = spglib.find_primitive(
phonopy_atoms, symprec=self._symprec)
elements = self._atoms.elements
el_dict = {}
for i in elements:
el_dict.setdefault(Specie(i).Z, i)
prim_elements = [el_dict[i] for i in numbers]
prim_atoms = Atoms(
lattice_mat=lattice,
elements=prim_elements,
coords=scaled_positions,
cartesian=False,
)
return prim_atoms
def read_data(
self,
filename="lammps.data",
element_order=[],
potential_file="pot.mod",
verbose=True,
):
"""Read Lammps data file."""
# n_atoms = len(self._species)
if element_order == []:
# Reading potential file for element order
if verbose:
print("element_order is empty, reading from", potential_file)
pot_file = open(potential_file, "r")
lines = pot_file.read().splitlines()
pot_file.close()
symb = []
# count = 0
for i, line in enumerate(lines):
if "pair_coeff" in line.split():
sp = line.split()
# print("spsplit", sp, os.getcwd())
for el in sp:
try:
if str(Specie(el).Z) != "nan":
# if el=='M':
# el='Mo'
# count=count+1
# if count >4:
symb.append(Specie(el).symbol)
except Exception:
pass
else:
symb = [Specie(i).symbol for i in element_order]
# print("symb=", symb)
f = open(filename, "r")
lines = f.read().splitlines()
for i, line in enumerate(lines):
if "atoms" in line.split():
natoms = int(line.split()[0])
if "types" in line.split():
# print(line)
ntypes = int(line.split()[0])
if "xlo" in line.split():
xlo = float(line.split()[0])
xhi = float(line.split()[1])
if "ylo" in line.split():
ylo = float(line.split()[0])
yhi = float(line.split()[1])
if "zlo" in line.split():
zlo = float(line.split()[0])
zhi = float(line.split()[1])
if "xy" in line.split():
xy = float(line.split()[0])
xz = float(line.split()[1])
yz = float(line.split()[2])
if len(symb) != ntypes:
ValueError("Something wrong in atom type assignment", len(symb),
ntypes)
lat = np.array([[xhi - xlo, 0.0, 0.0], [xy, yhi - ylo, 0.0],
[xz, yz, zhi - zlo]])
typ = np.empty((natoms), dtype="S20")
x = np.zeros((natoms))
y = np.zeros((natoms))
z = np.zeros((natoms))
q = np.zeros((natoms))
coords = list()
for i, line in enumerate(lines):
if "Atoms" in line.split():
for j in range(0, natoms):
# print int(((lines[j+2]).split()[1]))-1
typ[j] = symb[int(((lines[i + j + 2]).split()[1])) - 1]
q[j] = (lines[i + j + 2]).split()[2]
x[j] = (lines[i + j + 2]).split()[3]
y[j] = (lines[i + j + 2]).split()[4]
z[j] = (lines[i + j + 2]).split()[5]
coords.append([x[j], y[j], z[j]])
f.close()
# print ("info",(typ),'coo',(coords),'latt',lat)
typ_sp = [str(i, "utf-8") for i in typ]
# print ('typ_sp',typ_sp)
atoms = Atoms(
lattice_mat=lat,
elements=typ_sp,
coords=np.array(coords),
cartesian=True,
)
return atoms
def write_lammps_in(
self,
lammps_in="init.mod",
lammps_in1="potential.mod",
lammps_in2="in.main",
lammps_trj=None,
lammps_data=None,
parameters={},
):
"""
Write lammps input file.
From ase with custom modifications
LAMMPS input is devided into three parts
Args:
lammps_in: generally"init.mod",
with unit and conversion factor information
lammps_in1: generally "potential.mod",
with force-field/potential style and
element type information
lammps_in2: generally "in.elastic",
a generic main input file to be fed
in LAMMPS usin lmp_*<...,parameters['exec']
parameters: input parameters
"""
f = open(lammps_in, "w")
f1 = open(lammps_in1, "w") # potential.mod
f2 = open(lammps_in2, "w")
f.write(('variable dump_file string "%s"\n' % lammps_trj) +
("variable up equal 1.0e-6\n") +
("variable cfac equal 1.0e-4\n") +
("variable cunits string GPa\n") +
("variable etol equal 0\n") +
("variable ftol equal 1.0e-10\n") +
("variable maxiter equal 1000\n") +
("variable maxeval equal 10000\n") +
("variable dmax equal 1.0e-2\n") +
('variable data_file string "%s"\n' % "data"))
if "control_file" in parameters:
f2.write("include %s \n" % parameters["control_file"])
if "units" in parameters:
f.write("units %s \n" % parameters["units"])
else:
f.write("units metal \n")
if "atom_style" in parameters:
f.write("atom_style %s \n" % parameters["atom_style"])
else:
f.write("atom_style atomic \n")
if "boundary" in parameters:
f.write("boundary %s \n" % parameters["boundary"])
else:
pbc = self.pbc
f.write("boundary %s %s %s \n" % (pbc[0], pbc[1], pbc[2]))
f.write("atom_modify sort 0 0.0 \n")
for key in ("neighbor", "newton"):
if key in parameters:
f.write("%s %s \n" % (key, parameters[key]))
f.write("\n")
f.write("read_data %s\n" % "data")
f.write("\n### interactions \n")
if "lib" in parameters:
lib = parameters["lib"]
f1.write("%s \n" % lib)
if ("pair_style" in parameters) and ("pair_coeff" in parameters):
pair_style = parameters["pair_style"]
f1.write("pair_style %s \n" % pair_style)
symbols = self.LammpsDataObj._element_order
tag = ""
for i in symbols:
tag = tag + " " + i
pair_coef = "* * " + str(parameters["pair_coeff"]) + " " + tag
f1.write("pair_coeff %s \n" % pair_coef)
masses = []
for i in symbols:
m = Specie(i).atomic_mass
if m not in masses:
masses.append(m)
count = 0
for i in masses:
count = count + 1
f.write("mass" + " " + str(count) + " " + str(i) + "\n")
else:
# default interaction
f.write("pair_style lj/cut 2.5 \n" + "pair_coeff * * 1 1 \n" +
"mass * 1.0 \n")
f1.write("neighbor 1.0 nsq\n")
f1.write("neigh_modify once no every 1 delay 0 check yes\n")
if "min" not in parameters:
f1.write("min_style cg\n")
f1.write("min_modify dmax ${dmax} line quadratic\n")
f1.write("thermo 1\n")
f1.write("thermo_style custom step temp press cpu pxx pyy pzz\
pxy pxz pyz ke pe etotal vol lx ly lz atoms\n")
f1.write("thermo_modify norm no\n")
if "fix" in parameters:
if parameters["fix"]:
for i in parameters["fix"]:
f1.write("fix %s\n" % i)
f.close()
f1.close()
f2.close()
def get_string(self, cart=True, sort_order="X"):
"""
Convert Atoms to string.
Optional arguments below.
Args:
cart:True/False for cartesian/fractional coords.
sort_order: sort by chemical properties of
elements. Default electronegativity.
"""
system = str(self.composition.formula)
header = (str(system) + str("\n1.0\n") + str(self.lattice_mat[0][0]) +
" " + str(self.lattice_mat[0][1]) + " " +
str(self.lattice_mat[0][2]) + "\n" +
str(self.lattice_mat[1][0]) + " " +
str(self.lattice_mat[1][1]) + " " +
str(self.lattice_mat[1][2]) + "\n" +
str(self.lattice_mat[2][0]) + " " +
str(self.lattice_mat[2][1]) + " " +
str(self.lattice_mat[2][2]) + "\n")
if sort_order is None:
order = np.argsort(self.elements)
else:
order = np.argsort([
Specie(i).element_property(sort_order) for i in self.elements
])
if cart:
coords = self.cart_coords
else:
coords = self.frac_coords
coords_ordered = np.array(coords)[order]
elements_ordered = np.array(self.elements)[order]
props_ordered = np.array(self.props)[order]
# check_selective_dynamics = False # TODO
counts = get_counts(elements_ordered)
cart_frac = ""
if cart:
cart_frac = "\nCartesian\n"
else:
cart_frac = "\nDirect\n"
if "T" in "".join(map(str, self.props[0])):
middle = (" ".join(map(str, counts.keys())) + "\n" +
" ".join(map(str, counts.values())) +
"\nSelective dynamics\n" + cart_frac)
else:
middle = (" ".join(map(str, counts.keys())) + "\n" +
" ".join(map(str, counts.values())) + cart_frac)
rest = ""
for ii, i in enumerate(coords_ordered):
if self.show_props:
rest = (rest + " ".join(map(str, i)) + " " +
str(props_ordered[ii]) + "\n")
else:
rest = rest + " ".join(map(str, i)) + "\n"
result = header + middle + rest
return result
def dfpt_data(self, fc_mass=True):
"""Get DFPT IBRION=8 related data."""
info = {}
hessian = []
data = self._data
for i in (data["modeling"]["calculation"]["dynmat"]["varray"])[0]["v"]:
hessian.append(i.split())
hessian = np.array(hessian, dtype="double")
struct = self.all_structures[-1]
natoms = struct.num_atoms
force_constants = np.zeros((natoms, natoms, 3, 3), dtype="double")
for i in range(natoms):
for j in range(natoms):
force_constants[i, j] = hessian[
i * 3 : (i + 1) * 3, j * 3 : (j + 1) * 3
]
masses = [Specie(i).atomic_mass for i in struct.elements]
if fc_mass:
for i in range(natoms):
for j in range(natoms):
force_constants[i, j] *= -np.sqrt(masses[i] * masses[j])
born_charges = []
for n in range(natoms):
born_charges.append(
[
i.split()
for i in (data["modeling"]["calculation"]["array"]["set"])[
n
]["v"]
]
)
born_charges = np.array(born_charges, dtype="double")
phonon_eigenvals = np.array(
data["modeling"]["calculation"]["dynmat"]["v"]["#text"].split(),
dtype="double",
)
eigvecs = np.array(
[
i.split()
for i in (
data["modeling"]["calculation"]["dynmat"]["varray"][1]["v"]
)
],
dtype="float",
)
phonon_eigenvectors = []
for ev in eigvecs:
phonon_eigenvectors.append(np.array(ev).reshape(natoms, 3))
phonon_eigenvectors = np.array(phonon_eigenvectors, dtype="float")
epsilon = {}
for i in data["modeling"]["calculation"]["varray"]:
if "epsilon" in i["@name"]:
epsilon[i["@name"]] = np.array(
[j.split() for j in i["v"]], dtype="float"
)
info["born_charges"] = born_charges
info["phonon_eigenvectors"] = phonon_eigenvectors
info["epsilon"] = epsilon
info["phonon_eigenvalues"] = phonon_eigenvals
info["masses"] = masses
return info
def atomic_numbers(self):
"""Get list of atomic numbers of atoms in the atoms object."""
numbers = []
for i in self.elements:
numbers.append(Specie(i).Z)
return numbers
def simulate(self, atoms=None):
"""
Simulate XRD pattern.
Forked from https://github.com/qzhu2017/XRD.
"""
# atoms=Spacegroup3D(atoms).conventional_standard_structure
rec_matrix = atoms.lattice.reciprocal_lattice_crystallographic().matrix
min_r = self.wavelength / np.sin(self.max2theta / 2) / 2
r1 = list(range(1, self.max_index + 1))
r2 = list(range(-self.max_index, 1))
r2.reverse()
r3 = r1 + r2
r = r3
hkl_index = [
miller for miller in itertools.product(r, r, r)
if any([i != 0 for i in miller])
]
# hkl_index=symmetrically_distinct_miller_indices(cvn_atoms=atoms,max_index=self.max_index)
hkl_max = np.array([self.max_index, self.max_index, self.max_index])
for index in hkl_index:
d = np.linalg.norm(np.dot(index, rec_matrix))
multiple = int(np.ceil(1 / d / min_r))
index *= multiple
for i in range(len(hkl_max)):
if hkl_max[i] < index[i]:
hkl_max[i] = index[i]
h1, k1, l1 = hkl_max
h = np.arange(-h1, h1 + 1)
k = np.arange(-k1, k1 + 1)
L = np.arange(-l1, l1 + 1)
hkl = np.array((np.meshgrid(h, k, L))).transpose()
hkl_list = np.reshape(hkl, [len(h) * len(k) * len(L), 3])
hkl_list = hkl_list[np.where(hkl_list.any(axis=1))[0]]
d_hkl = 1 / np.linalg.norm(np.dot(hkl_list, rec_matrix), axis=1)
shortlist = d_hkl > (min_r)
d_hkl = d_hkl[shortlist]
hkl_list = hkl_list[shortlist]
sintheta = self.wavelength / 2 / d_hkl
self.theta = np.arcsin(sintheta)
self.hkl_list = np.array(hkl_list)
self.d_hkl = d_hkl
fp = os.path.join(os.path.dirname(__file__),
"atomic_scattering_params.json")
with open(fp, "r") as f:
ATOMIC_SCATTERING_PARAMS = json.load(f)
d0 = (1 / 2 / self.d_hkl)**2
coeffs = []
zs = []
for elem in atoms.elements:
if elem == "D":
elem = "H"
c = ATOMIC_SCATTERING_PARAMS[elem]
z = Specie(elem).Z
coeffs.append(c)
zs.append(z)
coeffs = np.array(coeffs)
self.peaks = {}
two_thetas = []
for hkl, s2, theta, d_hkl in zip(self.hkl_list, d0, self.theta,
self.d_hkl):
# Calculate the scattering factor sf
g_dot_r = np.dot(atoms.frac_coords, np.transpose([hkl])).T[0]
sf = zs - 41.78214 * s2 * np.sum(
coeffs[:, :, 0] * np.exp(-coeffs[:, :, 1] * s2), axis=1)
# Calculate the structure factor f
f = np.sum(sf * np.exp(2j * np.pi * g_dot_r))
# Calculate the lorentz polarization factor lf
lf = (1 + np.cos(2 * theta)**2) / (np.sin(theta)**2 *
np.cos(theta))
po = 1
# Calculate the intensity I
intensity_tmp = (f * f.conjugate()).real
# calculate 2*theta
two_theta = np.degrees(2 * theta)
# Find where the scattered angles are equal
ind = np.where(
np.abs(np.subtract(two_thetas, two_theta)) < self.two_theta_tol
)
# Append intensity, hkl plane, and thetas to lists
if len(ind[0]) > 0:
self.peaks[two_thetas[ind[0][0]]][0] += intensity_tmp * lf * po
self.peaks[two_thetas[ind[0][0]]][1].append(tuple(hkl))
else:
self.peaks[two_theta] = [
intensity_tmp * lf * po,
[tuple(hkl)],
d_hkl,
]
two_thetas.append(two_theta)
# max_intensity = max([v[0] for v in self.peaks.values()])
x = []
y = []
d_hkls = []
hkl_families = []
for k in sorted(self.peaks.keys()):
v = self.peaks[k]
x.append(k)
y.append(v[0])
d_hkls.append(v[2])
family = self.get_unique_families(v[1])
hkl_families.append(family)
x = np.array(x)
y = np.array(y)
d_hkls = np.array(d_hkls)
const = np.sum(y, axis=0)
y = y * self.scaling_factor / const
screen = y > self.intensity_tol
self.intensity_array = y[screen]
self.two_theta_array = x[screen]
self.dhkl_array = d_hkls[screen]
return (
x[screen].tolist(),
d_hkls[screen].tolist(),
y[screen].tolist(),
# hkl_families[screen],
)
def weight(self):
"""Get atomic weight."""
wt = 0.0
for specie, count in self._content.items():
wt = wt + Specie(specie).atomic_mass * count
return wt
def from_atoms(
atoms=None,
get_prim=False,
zero_diag=False,
node_atomwise_angle_dist=False,
node_atomwise_rdf=False,
features="basic",
enforce_c_size=10.0,
max_n=100,
max_cut=5.0,
verbose=False,
make_colormap=True,
):
"""
Get Networkx graph. Requires Networkx installation.
Args:
atoms: jarvis.core.Atoms object.
rcut: cut-off after which distance will be set to zero
in the adjacency matrix.
features: Node features.
'atomic_number': graph with atomic numbers only.
'cfid': 438 chemical descriptors from CFID.
'basic':10 features
'atomic_fraction': graph with atomic fractions
in 103 elements.
array: array with CFID chemical descriptor names.
See: jarvis/core/specie.py
enforce_c_size: minimum size of the simulation cell in Angst.
"""
if get_prim:
atoms = atoms.get_primitive_atoms
dim = get_supercell_dims(atoms=atoms, enforce_c_size=enforce_c_size)
atoms = atoms.make_supercell(dim)
adj = np.array(atoms.raw_distance_matrix.copy())
# zero out edges with bond length greater than threshold
adj[adj >= max_cut] = 0
if zero_diag:
np.fill_diagonal(adj, 0.0)
nodes = np.arange(atoms.num_atoms)
if features == "atomic_number":
node_attributes = np.array(
[[np.array(Specie(i).Z)] for i in atoms.elements],
dtype="float",
)
if features == "atomic_fraction":
node_attributes = []
fracs = atoms.composition.atomic_fraction_array
for i in fracs:
node_attributes.append(np.array([float(i)]))
node_attributes = np.array(node_attributes)
elif features == "basic":
feats = [
"Z",
"coulmn",
"row",
"X",
"atom_rad",
"nsvalence",
"npvalence",
"ndvalence",
"nfvalence",
"first_ion_en",
"elec_aff",
]
node_attributes = []
for i in atoms.elements:
tmp = []
for j in feats:
tmp.append(Specie(i).element_property(j))
node_attributes.append(tmp)
node_attributes = np.array(node_attributes, dtype="float")
elif features == "cfid":
node_attributes = np.array(
[np.array(Specie(i).get_descrp_arr) for i in atoms.elements],
dtype="float",
)
elif isinstance(features, list):
node_attributes = []
for i in atoms.elements:
tmp = []
for j in features:
tmp.append(Specie(i).element_property(j))
node_attributes.append(tmp)
node_attributes = np.array(node_attributes, dtype="float")
else:
print("Please check the input options.")
if node_atomwise_rdf or node_atomwise_angle_dist:
nbr = NeighborsAnalysis(atoms,
max_n=max_n,
verbose=verbose,
max_cut=max_cut)
if node_atomwise_rdf:
node_attributes = np.concatenate(
(node_attributes, nbr.atomwise_radial_dist()), axis=1)
node_attributes = np.array(node_attributes, dtype="float")
if node_atomwise_angle_dist:
node_attributes = np.concatenate(
(node_attributes, nbr.atomwise_angle_dist()), axis=1)
node_attributes = np.array(node_attributes, dtype="float")
# construct edge list
uv = []
edge_features = []
for ii, i in enumerate(atoms.elements):
for jj, j in enumerate(atoms.elements):
bondlength = adj[ii, jj]
if bondlength > 0:
uv.append((ii, jj))
edge_features.append(bondlength)
edge_attributes = edge_features
if make_colormap:
sps = atoms.uniq_species
color_dict = random_colors(number_of_colors=len(sps))
new_colors = {}
for i, j in color_dict.items():
new_colors[sps[i]] = j
color_map = []
for ii, i in enumerate(atoms.elements):
color_map.append(new_colors[i])
return Graph(
nodes=nodes,
edges=uv,
node_attributes=np.array(node_attributes),
edge_attributes=np.array(edge_attributes),
color_map=color_map,
)
def get_comp_descp(
self,
jcell=True,
jmean_chem=True,
jmean_chg=True,
jrdf=False,
jrdf_adf=True,
print_names=False,
):
"""
Get chemo-structural CFID decriptors.
Args:
struct: Structure object
jcell: whether to use cell-size descriptors
jmean_chem: whether to use average chemical descriptors
jmean_chg: whether to use average charge distribution descriptors
jmean_rdf: whether to use radial distribution descriptors
jrdf_adf: whether to use radial and angle distribution descriptors
print_names: whether to print names of descriptors
Returns:
cat: catenated final descriptors
"""
cat = []
s = self._atoms
cell = []
mean_chem = []
rdf = []
nn = []
mean_chg = []
adfa = []
adfb = []
ddf = []
if jmean_chem:
el_dict = s.composition._content
# print (el_dict,type(el_dict))
arr = []
for k, v in el_dict.items():
des = Specie(k).get_descrp_arr
arr.append(des)
mean_chem = np.mean(np.array(arr), axis=0)
# print ('mean_chem',len(mean_chem))
if jcell:
v_pa = round(float(s.volume) / float(s.num_atoms), 5)
logv_pa = round(log(v_pa), 5)
pf = s.packing_fraction
density = round(s.density, 5)
cell = np.array([v_pa, logv_pa, pf, density])
# print ('jcell',len(cell))
if jrdf:
Nbrs = NeighborsAnalysis(s)
_, distrdf, nn = Nbrs.get_rdf()
rdf = np.array(distrdf)
print("rdf", len(rdf))
if jrdf_adf:
try:
adfa = np.zeros(179)
adfb = np.zeros(179)
ddf = np.zeros(179)
rdf = np.zeros(100)
nn = np.zeros(100)
distributions = NeighborsAnalysis(s).get_all_distributions
rdf = distributions["rdf"]
nn = distributions["nn"]
adfa = distributions["adfa"]
adfb = distributions["adfb"]
ddf = distributions["ddf"]
except Exception:
pass
adfa = np.array(adfa)
adfb = np.array(adfb)
rdf = np.array(rdf)
ddf = np.array(ddf)
nn = np.array(nn)
# print ('adfa',len(adfa))
# print ('ddf',len(ddf))
# print ('adfb',len(adfb))
# print ('rdf',len(rdf))
# print ('nn',len(nn))
if jmean_chg:
chgarr = []
el_dict = s.composition._content
for k, v in el_dict.items():
chg = Specie(k).get_chgdescrp_arr
chgarr.append(chg)
mean_chg = np.mean(chgarr, axis=0)
# print ('mean_chg',len(mean_chg))
if print_names:
nmes = []
chem_nm = get_descrp_arr_name()
for d, nm in zip(
[mean_chem, cell, mean_chg, rdf,
adfa, adfb, ddf, nn],
["mean_chem", "cell", "mean_chg",
"rdf", "adfa", "adfb", "ddf", "nn"],
):
if len(d) != 0:
for ff, dd in enumerate(d):
cat.append(dd)
if nm == "mean_chem":
tag = chem_nm[ff]
else:
tag = str(nm) + str("_") + str(ff)
nmes.append(str(tag))
cat = np.array(cat).astype(float)
# print (nmes,len(nmes))
return nmes
else:
for d, nm in zip(
[mean_chem, cell, mean_chg, rdf,
adfa, adfb, ddf, nn],
["mean_chem", "cell", "mean_chg",
"rdf", "adfa", "adfb", "ddf", "nn"],
):
if len(d) != 0:
# if d != []:
for ff, dd in enumerate(d):
cat.append(dd)
cat = np.array(cat).astype(float)
return cat
