def generate_random_perovskite(lat=None):
'''
This generates a random valid perovskite structure in ASE format.
Useful for testing.
Binary and organic perovskites are not considered.
'''
if not lat:
lat = round(random.uniform(3.5, Perovskite_tilting.OCTAHEDRON_BOND_LENGTH_LIMIT*2), 3)
A_site = random.choice(Perovskite_Structure.A)
B_site = random.choice(Perovskite_Structure.B)
Ci_site = random.choice(Perovskite_Structure.C)
Cii_site = random.choice(Perovskite_Structure.C)
while covalent_radii[chemical_symbols.index(A_site)] - \
covalent_radii[chemical_symbols.index(B_site)] < 0.05 or \
covalent_radii[chemical_symbols.index(A_site)] - \
covalent_radii[chemical_symbols.index(B_site)] > 0.5:
A_site = random.choice(Perovskite_Structure.A)
B_site = random.choice(Perovskite_Structure.B)
return crystal(
[A_site, B_site, Ci_site, Cii_site],
[(0.5, 0.25, 0.0), (0.0, 0.0, 0.0), (0.0, 0.25, 0.0), (0.25, 0.0, 0.75)],
spacegroup=62, cellpar=[lat*math.sqrt(2), 2*lat, lat*math.sqrt(2), 90, 90, 90]
)
def analyse_all_bonds(model, verbose=True, abnormal=True):
'''
Analyse bonds and return all abnormal bond types and list of these
TODO: Make this more bullet proof - what happens if abnormal bonds aren't requested.
A table of bond distance analysis for the supplied model is also possible
Parameters:
model: Atoms object
Structure for which the analysis is to be conducted
verbose: Boolean
Determines whether the output should be printed to screen
abnormal: Boolean
Collect information about rogue looking bond lengths.
(Does enabling this by default add a large time overhead?)
'''
# set() to ensure unique chemical symbols list
list_of_symbols = list(set(model.get_chemical_symbols()))
# Combination as AB = BA for bonds, avoiding redundancy
from itertools import combinations_with_replacement
all_bonds = combinations_with_replacement(list_of_symbols, 2)
# Define lists to collect abnormal observations
abnormal_bonds = []
list_of_abnormal_bonds = []
# Table heading
if verbose:
print_bond_table_header()
from ase.data import chemical_symbols, covalent_radii
# Iterate over all arrangements of chemical symbols
for bonds in all_bonds:
print_AB, AB_Bonds, AB_BondsValues = analyse_bonds(model,
bonds[0],
bonds[1],
verbose=verbose,
multirow=True)
if abnormal and AB_BondsValues is not None:
sum_of_covalent_radii = covalent_radii[chemical_symbols.index(
bonds[0])] + covalent_radii[chemical_symbols.index(bonds[1])]
abnormal_cutoff = max(0.4, sum_of_covalent_radii * 0.75)
for values in AB_BondsValues:
abnormal_values = [i for i in values if i < abnormal_cutoff]
if len(abnormal_values):
abnormal_bonds.append(len(abnormal_values))
list_of_abnormal_bonds.append(print_AB)
# This now returns empty arrays if no abnormal bond checks are done,
# or if genuinely there are no abnormal bonds.
return abnormal_bonds, list_of_abnormal_bonds
def build_central_rdf(self, nbins=5, pcty=False):
atoms = self.build_atoms_obj().copy()
# center atoms at origin (COP)
atoms.positions -= atoms.positions.mean(0)
# calculate distances from origin
dists = np.linalg.norm(atoms.positions, axis=1)
# calculate distances from COP for each metal type
dist_m1 = dists[atoms.symbols == self.metal1]
dist_m2 = dists[atoms.symbols == self.metal2]
fig, ax = plt.subplots()
# get jmol color for each metal
m1_color = jmol_colors[chemical_symbols.index(self.metal1)]
m2_color = jmol_colors[chemical_symbols.index(self.metal2)]
# set nbins to num shells if not given
if not nbins:
nbins = self.nanoparticle.num_shells
counts_m1, bin_edges = np.histogram(dist_m1, bins=nbins)
counts_m2, bin_edges = np.histogram(dist_m2, bins=nbins)
# get bins for ax.hist
bins = np.linspace(0, dists.max(), nbins + 1)
x = (bin_edges[1:] + bin_edges[:-1]) / 2
ax.plot(x,
counts_m1,
'o-',
markeredgecolor='k',
color=m1_color,
markersize=8,
label=self.metal1)
ax.plot(x,
counts_m2,
'o-',
markeredgecolor='k',
color=m2_color,
markersize=8,
label=self.metal2)
ax.set_xlabel('Distance from Core ($\\rm \\AA$)')
ax.set_ylabel('Number of Atoms')
ax.legend()
fig.tight_layout()
plt.show()
return fig
def get_checksum(self):
'''
Retrieve unique hash in a cross-platform manner:
this is how calculation identity is determined
'''
if self._checksum:
return self._checksum
if not self._filename:
raise RuntimeError('Source calc file is required in order to properly save the data!')
calc_checksum = hashlib.sha224()
struc_repr = ""
for ase_obj in self.structures:
struc_repr += "%3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f " % tuple(map(abs, [ase_obj.cell[0][0], ase_obj.cell[0][1], ase_obj.cell[0][2], ase_obj.cell[1][0], ase_obj.cell[1][1], ase_obj.cell[1][2], ase_obj.cell[2][0], ase_obj.cell[2][1], ase_obj.cell[2][2]])) # NB beware of length & minus zeros
for atom in ase_obj:
struc_repr += "%s %3.6f %3.6f %3.6f " % tuple(map(abs, [chemical_symbols.index(atom.symbol), atom.x, atom.y, atom.z])) # NB beware of length & minus zeros
if self.info["energy"] is None:
energy = str(None)
else:
energy = str(round(self.info['energy'], 11 - int(math.log10(math.fabs(self.info['energy'])))))
calc_checksum.update((
struc_repr + "\n" +
energy + "\n" +
self.info['prog'] + "\n" +
str(self.info['input']) + "\n" +
str(sum([2**x for x in self.info['calctypes']]))
).encode('ascii')) # NB this is fixed and should not be changed
result = base64.b32encode(calc_checksum.digest()).decode('ascii')
result = result[:result.index('=')] + 'CI'
return result
def write_lammps_data(filename,
atoms,
atom_types,
comment=None,
cutoff=None,
molecule_ids=None,
charges=None,
units='metal'):
if isinstance(filename, str):
fh = open(filename, 'w')
else:
fh = filename
if comment is None:
comment = 'lammpslib autogenerated data file'
fh.write(comment.strip() + '\n\n')
fh.write('{0} atoms\n'.format(len(atoms)))
fh.write('{0} atom types\n'.format(len(atom_types)))
fh.write('\n')
cell, coord_transform = convert_cell(atoms.get_cell())
fh.write('{0:16.8e} {1:16.8e} xlo xhi\n'.format(0.0, cell[0, 0]))
fh.write('{0:16.8e} {1:16.8e} ylo yhi\n'.format(0.0, cell[1, 1]))
fh.write('{0:16.8e} {1:16.8e} zlo zhi\n'.format(0.0, cell[2, 2]))
fh.write('{0:16.8e} {1:16.8e} {2:16.8e} xy xz yz\n'
''.format(cell[0, 1], cell[0, 2], cell[1, 2]))
fh.write('\nMasses\n\n')
sym_mass = {}
masses = atoms.get_masses()
symbols = atoms.get_chemical_symbols()
for sym in atom_types:
for i in range(len(atoms)): # TODO: Make this more efficient
if symbols[i] == sym:
sym_mass[sym] = convert(masses[i], "mass", "ASE", units)
break
else:
sym_mass[sym] = convert(
ase_atomic_masses[ase_chemical_symbols.index(sym)], "mass",
"ASE", units)
for (sym, typ) in sorted(atom_types.items(), key=operator.itemgetter(1)):
fh.write('{0} {1}\n'.format(typ, sym_mass[sym]))
fh.write('\nAtoms # full\n\n')
if molecule_ids is None:
molecule_ids = np.zeros(len(atoms), dtype=int)
if charges is None:
charges = atoms.get_initial_charges()
for i, (sym, mol, q, pos) in enumerate(
zip(symbols, molecule_ids, charges, atoms.get_positions())):
typ = atom_types[sym]
fh.write(
'{0} {1} {2} {3:16.8e} {4:16.8e} {5:16.8e} {6:16.8e}\n'.format(
i + 1, mol, typ, q, pos[0], pos[1], pos[2]))
if isinstance(filename, str):
fh.close()
def get_atom_kinds(atoms, props={}):
# symbols = atoms.symbols
# formula = atoms.symbols.formula
# atom_kinds = formula.count()
if hasattr(atoms, 'kinds'):
kinds = list(set(atoms.kinds))
else:
atoms.kinds = atoms.get_chemical_symbols()
kinds = list(set(atoms.kinds))
# print(kinds)
atom_kinds = {}
for kind in kinds:
atom_kinds[kind] = {}
element = kind.split('_')[0]
number = chemical_symbols.index(element)
inds = [
atom.index for atom in atoms if atoms.kinds[atom.index] == kind
]
color = jmol_colors[number]
radius = covalent_radii[number]
atom_kinds[kind]['element'] = element
atom_kinds[kind]['positions'] = atoms[inds].positions
atom_kinds[kind]['number'] = number
atom_kinds[kind]['color'] = color
atom_kinds[kind]['transmit'] = 1.0
atom_kinds[kind]['radius'] = radius
atom_kinds[kind]['balltype'] = None
if props:
if kind in props.keys():
for prop, value in props[kind].items():
atom_kinds[kind][prop] = value
return atom_kinds
def __init__(self, tilde_calc):
self.weight = 0
for a in tilde_calc.structures[-1]:
if not a.symbol in chemical_symbols:
raise ModuleError('Unexpected atom has been found!')
try:
self.weight += atomic_masses[chemical_symbols.index(a.symbol)]
except IndexError:
raise ModuleError('Application error!')
self.weight = round(self.weight)
def get_APF(ase_obj):
"""
Example crystal structure descriptor:
https://en.wikipedia.org/wiki/Atomic_packing_factor
"""
volume = 0.0
for atom in ase_obj:
volume += 4 / 3 * np.pi * covalent_radii[chemical_symbols.index(
atom.symbol)]**3
return volume / abs(np.linalg.det(ase_obj.cell))
def __init__(self, symbols: List[str]):
if NULL_SYMBOL in symbols:
raise RuntimeError(
f'Place holder symbol {NULL_SYMBOL} cannot be in list of symbols'
)
if len(symbols) < 1:
raise RuntimeError('List of symbols cannot be empty')
# Ensure that all symbols are valid
for symbol in symbols:
chemical_symbols.index(symbol)
# Ensure that there are no duplicates
if len(set(symbols)) != len(symbols):
raise RuntimeError(
f'List of symbols {symbols} cannot contain duplicates')
self._symbols = [NULL_SYMBOL] + symbols
def __init__(self, tilde_calc):
self.weight = 0
for a in tilde_calc.structures[-1]:
if not a.symbol in chemical_symbols:
raise ModuleError('Unexpected atom has been found!')
try:
self.weight += atomic_masses[chemical_symbols.index(a.symbol)]
except IndexError:
raise ModuleError('Application error!')
self.weight = round(self.weight)
def molecule_search(self,
element_pool={
'C': 2,
'H': 6
},
load_molecules=True,
multiple_bond_search=False):
"""Return the enumeration of molecules which can be produced from
the specified atoms.
Parameters:
-----------
element_pool: dict
Atomic symbols keys paired with the maximum number of that atom.
load_molecules: bool
Load any existing molecules from the database.
multiple_bond_search: bool
Allow atoms to form bonds with other atoms in the molecule.
"""
numbers = np.zeros(len(self.base_valence))
for k, v in element_pool.items():
numbers[chemical_symbols.index(k)] = v
self.element_pool = numbers
self.multiple_bond_search = multiple_bond_search
molecules = {}
if load_molecules:
self.molecules = self.load_molecules(binned=True)
search_molecules = []
for el, cnt in enumerate(self.element_pool):
if cnt == 0:
continue
molecule = Gratoms(symbols=[el])
molecule.nodes[0]['valence'] = self.base_valence[el]
search_molecules += [molecule]
comp_tag, bond_tag = molecule.get_chemical_tags()
if comp_tag not in self.molecules:
molecules[comp_tag] = {bond_tag: [molecule]}
for molecule in search_molecules:
# Recusive use of molecules
new_molecules, molecules = self._branch_molecule(
molecule, molecules)
search_molecules += new_molecules
self.save_molecules(molecules)
def chemical_symbols_to_numbers(symbols: List[str]) -> List[int]:
"""Returns the atomic numbers equivalent to the input chemical
symbols.
Parameters
----------
symbols
chemical symbols
"""
numbers = [chemical_symbols.index(symbols) for symbols in symbols]
return numbers
def search_abnormal_bonds(model, verbose=True):
'''
Check all bond lengths in the model for abnormally
short ones.
Parameters:
model: Atoms object or string. If string it will read a file
in the same folder, e.g. "name.traj"
'''
# Abnormality check
abnormal_bonds, list_of_abnormal_bonds = analyse_all_bonds(model,
verbose=verbose,
abnormal=True)
from ase.data import chemical_symbols, covalent_radii
import numpy as np
if list_of_abnormal_bonds:
sums_of_covalent_radii = []
for i in list_of_abnormal_bonds:
bond_chem_symbols = i.split("-")
sums_of_covalent_radii += [
covalent_radii[chemical_symbols.index(bond_chem_symbols[0])] +
covalent_radii[chemical_symbols.index(bond_chem_symbols[1])]
]
# Check against possible covalent radii values averaged * 0.75
if len(abnormal_bonds) > 0:
if verbose:
print("-" * 40)
print(
"A total of", len(abnormal_bonds),
"abnormal bond lengths observed (<" +
str(max(0.4,
np.average(sums_of_covalent_radii) * 0.75)) + " A")
print("Identities:", list_of_abnormal_bonds)
print("-" * 40)
return False
else:
return True
def get_polyhedra_kinds(atoms, bondlist={}, transmit=0.8, polyhedra={}):
"""
Two modes:
(1) Search atoms bonded to kind
polyhedra: {'kind': ligands}
"""
from scipy.spatial import ConvexHull
from ase.data import covalent_radii
from ase.neighborlist import NeighborList
tstart = time.time()
polyhedra_kinds = {}
for kind, ligand in polyhedra.items():
# print(kind, ligand)
if kind not in polyhedra_kinds.keys():
element = kind.split('_')[0]
number = chemical_symbols.index(element)
color = jmol_colors[number]
polyhedra_kinds[kind] = {'vertices': [], 'edges': [], 'faces': []}
polyhedra_kinds[kind]['material'] = {
'diffuseColor': tuple(color),
'transparency': 0.5
}
inds = [atom.index for atom in atoms if atom.symbol == kind]
for ind in inds:
vertice = []
for bond in bondlist[ind]:
a2, offset = bond
if atoms[a2].symbol in ligand:
temp_pos = atoms[a2].position + np.dot(offset, atoms.cell)
vertice.append(temp_pos)
nverts = len(vertice)
# print(ind, nverts)
if nverts > 3:
# print(ind, vertice)
# search convex polyhedra
hull = ConvexHull(vertice)
face = hull.simplices
nverts = len(polyhedra_kinds[kind]['vertices'])
face = face + nverts
edge = []
for f in face:
edge.append([f[0], f[1]])
edge.append([f[0], f[2]])
edge.append([f[1], f[2]])
polyhedra_kinds[kind]['vertices'] = polyhedra_kinds[kind][
'vertices'] + list(vertice)
polyhedra_kinds[kind][
'edges'] = polyhedra_kinds[kind]['edges'] + list(edge)
polyhedra_kinds[kind][
'faces'] = polyhedra_kinds[kind]['faces'] + list(face)
# print('get_polyhedra_kinds: {0:10.2f} s'.format(time.time() - tstart))
return polyhedra_kinds
def classify(tilde_obj):
if not len(tilde_obj.info['elements']) == 1 or tilde_obj.info['contents'][0] != 1: return tilde_obj
dims = tilde_obj.info['dims'] if tilde_obj.structures[-1].periodicity == 3 else abs(det(tilde_obj.structures[-1].cell))
if tilde_obj.structures[-1].periodicity == 0 or \
float( dims / covalent_radii[chemical_symbols.index(tilde_obj.info['elements'][0])] ) > REL:
# atomic radius should be REL times less than cell dimensions
tilde_obj.info['periodicity'] = -1
tilde_obj.structures[-1].periodicity = -1
return tilde_obj
def _get_molecule_spec(atoms, nuclear_props):
''' Generate the molecule specification section to write
to the Gaussian input file, from the Atoms object and dict
of nuclear properties'''
molecule_spec = []
for i, atom in enumerate(atoms):
symbol_section = atom.symbol + '('
# Check whether any nuclear properties of the atom have been set,
# and if so, add them to the symbol section.
nuclear_props_set = False
for keyword, array in nuclear_props.items():
if array is not None and array[i] is not None:
string = keyword + '=' + str(array[i]) + ', '
symbol_section += string
nuclear_props_set = True
# Check whether the mass of the atom has been modified,
# and if so, add it to the symbol section:
mass_set = False
symbol = atom.symbol
expected_mass = atomic_masses_iupac2016[chemical_symbols.index(symbol)]
if expected_mass != atoms[i].mass:
mass_set = True
string = 'iso' + '=' + str(atoms[i].mass)
symbol_section += string
if nuclear_props_set or mass_set:
symbol_section = symbol_section.strip(', ')
symbol_section += ')'
else:
symbol_section = symbol_section.strip('(')
# Then attach the properties appropriately
# this formatting was chosen for backwards compatibility reasons, but
# it would probably be better to
# 1) Ensure proper spacing between entries with explicit spaces
# 2) Use fewer columns for the element
# 3) Use 'e' (scientific notation) instead of 'f' for positions
molecule_spec.append('{:<10s}{:20.10f}{:20.10f}{:20.10f}'.format(
symbol_section, *atom.position))
# unit cell vectors, in case of periodic boundary conditions
for ipbc, tv in zip(atoms.pbc, atoms.cell):
if ipbc:
molecule_spec.append('TV {:20.10f}{:20.10f}{:20.10f}'.format(*tv))
molecule_spec.append('')
return molecule_spec
def classify(tilde_obj):
if not len(tilde_obj.info['elements']
) == 1 or tilde_obj.info['contents'][0] != 1:
return tilde_obj
dims = tilde_obj.info['dims'] if tilde_obj.structures[
-1].periodicity == 3 else abs(det(tilde_obj.structures[-1].cell))
if tilde_obj.structures[-1].periodicity == 0 or \
float( dims / covalent_radii[chemical_symbols.index(tilde_obj.info['elements'][0])] ) > REL:
# atomic radius should be REL times less than cell dimensions
tilde_obj.info['periodicity'] = -1
tilde_obj.structures[-1].periodicity = -1
return tilde_obj
def classify(tilde_obj):
if sum(tilde_obj.structures[-1].get_pbc()
) == 3: # only those initially 3-periodic
zi, mi = tilde_obj.info['cellpar'].index(
max(tilde_obj.info['cellpar']
[0:3])), tilde_obj.info['cellpar'].index(
min(tilde_obj.info['cellpar'][0:3]))
cmpveci = [i for i in range(3) if i not in [zi, mi]][0]
# vacuum per one atom (av)
atoms_volume = 0.0
for i in tilde_obj.structures[-1]:
atoms_volume += 4 / 3 * math.pi * (
covalent_radii[chemical_symbols.index(i.symbol)] + r_EC)**3
av = (abs(det(tilde_obj.structures[-1].cell)) - atoms_volume) / len(
tilde_obj.structures[-1])
#print "av:", av
# too much vacuum
if av > ADDED_VACUUM_3D_TOL:
# 2D
if tilde_obj.info['cellpar'][cmpveci] * L < tilde_obj.info[
'cellpar'][zi]:
tilde_obj.info['techs'].append(
'vacuum %sA' % int(round(tilde_obj.info['cellpar'][zi])))
tilde_obj.structures[-1].set_pbc((True, True, False))
# TODO: 1D ?
# 0D
elif av > ADDED_VACUUM_3D_TOL * 4: # TODO
tilde_obj.structures[-1].set_pbc((False, False, False))
# extend the last ASE object
tilde_obj.structures[-1].periodicity = int(
sum(tilde_obj.structures[-1].get_pbc()))
tilde_obj.structures[-1].dims = abs(det(tilde_obj.structures[-1].cell))
# TODO: area for surfaces
tilde_obj.info['dims'] = tilde_obj.structures[-1].dims
tilde_obj.info['periodicity'] = tilde_obj.structures[-1].periodicity
return tilde_obj
def get_bond_kinds(atoms, atom_kinds, bondlist):
'''
Build faces for instancing bonds.
The radius of bonds is determined by nbins.
mesh.from_pydata(vertices, [], faces)
'''
# view(atoms)
bond_kinds = {}
for ind1, pairs in bondlist.items():
kind = atoms.kinds[ind1]
if kind not in bond_kinds.keys():
lengths = []
centers = []
normals = []
bond_kinds[kind] = {
'lengths': lengths,
'centers': centers,
'normals': normals
}
number = chemical_symbols.index(kind)
color = atom_kinds[kind]['color']
radius = covalent_radii[number]
bond_kinds[kind]['number'] = number
bond_kinds[kind]['color'] = color
bond_kinds[kind]['transmit'] = atom_kinds[kind]['transmit']
for bond in pairs:
ind2, offset = bond
R = np.dot(offset, atoms.cell)
# print(inds, offset)
pos = [atoms.positions[ind1], atoms.positions[ind2] + R]
# print(pos)
center0 = (pos[0] + pos[1]) / 2.0
if pos[0][2] > pos[1][2]:
vec = pos[0] - pos[1]
else:
vec = pos[1] - pos[0]
# print(vec)
length = np.linalg.norm(vec)
nvec = vec / length
# kinds = [atoms[ind].symbol for ind in [a, b]]
center = (center0 + pos[0]) / 2.0
bond_kinds[kind]['centers'].append(center)
bond_kinds[kind]['lengths'].append(length / 4.0)
bond_kinds[kind]['normals'].append(nvec)
# pprint.pprint(bond_kinds)
return bond_kinds
def plot_pdos(self,
energies=None,
pdos_kinds=None,
Emin=-5,
Emax=5,
ax=None,
total=False,
select=None,
fill=True,
output=None,
legend=False,
xylabel=True,
smearing=None):
'''
'''
if energies is None: energies = self.pdos_energies
if pdos_kinds is None: pdos_kinds = self.pdos_kinds
xindex = (energies > Emin) & (energies < Emax)
# print(xindex)
if ax is None:
fig, ax = plt.subplots(figsize=(6, 3))
# if total:
# self.plot_data(self.pdos_energies, self.pdos_tot, label = 'pdos', ax = ax, fill = fill)
for kind, channels in pdos_kinds.items():
if select and kind not in select: continue
number = chemical_symbols.index(kind)
color = jmol_colors[number]
for channel, pdos in channels.items():
if select and channel[-1] not in select[kind]: continue
label = '{0}-{1}'.format(kind, channel)
self.plot_data(energies,
pdos,
label=label,
ax=ax,
xindex=xindex,
fill=fill,
color=color,
smearing=smearing)
if legend: ax.legend()
if xylabel:
ax.set_xlabel('Energy (eV)')
ax.set_ylabel('PDOS (a.u.)')
if output is not None:
plt.savefig('%s' % output)
return ax
def get_checksum(self):
''' retrieve unique hash in a cross-platform manner:
this is how unique identity is determined '''
if self._checksum: return self._checksum
if not self._filename:
raise RuntimeError(
'Source calc file is required in order to properly save the data!'
)
calc_checksum = hashlib.sha224()
struc_repr = ""
for ase_repr in self.structures:
struc_repr += "%3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f %3.6f " % tuple(
map(abs, [
ase_repr.cell[0][0], ase_repr.cell[0][1],
ase_repr.cell[0][2], ase_repr.cell[1][0],
ase_repr.cell[1][1], ase_repr.cell[1][2],
ase_repr.cell[2][0], ase_repr.cell[2][1],
ase_repr.cell[2][2]
])) # NB beware of length & minus zeros
for atom in ase_repr:
struc_repr += "%s %3.6f %3.6f %3.6f " % tuple(
map(abs, [
chemical_symbols.index(atom.symbol), atom.x, atom.y,
atom.z
])) # NB beware of length & minus zeros
if self.info["energy"] is None:
energy = str(None)
else:
energy = str(
round(self.info['energy'],
11 - int(math.log10(math.fabs(self.info['energy'])))))
calc_checksum.update(
(struc_repr + energy + " " + self.info['prog'] + " " +
str(self.info['input']) + " " +
str(sum([2**x for x in self.info['calctypes']]))).encode('ascii')
) # this is fixed in DB schema 5.11 and should not be changed
result = base64.b32encode(calc_checksum.digest()).decode('ascii')
result = result[:result.index('=')] + 'CI'
return result
def get_occupied_primitive_structure(
structure: Atoms, allowed_species: List[List[str]],
symprec: float) -> Tuple[Atoms, List[Tuple[str, ...]]]:
"""
Returns an occupied primitive structure.
Will put hydrogen on sublattice 1, Helium on sublattice 2 and
so on
Parameters
----------
structure
input structure
allowed_species
chemical symbols that are allowed on each site
symprec
tolerance imposed when analyzing the symmetry using spglib
Todo
----
simplify the revert back to unsorted symbols
"""
if len(structure) != len(allowed_species):
raise ValueError(
'structure and chemical symbols need to be the same size.')
sorted_symbols = sorted({tuple(sorted(s)) for s in allowed_species})
decorated_primitive = structure.copy()
for i, sym in enumerate(allowed_species):
sublattice = sorted_symbols.index(tuple(sorted(sym))) + 1
decorated_primitive[i].symbol = chemical_symbols[sublattice]
decorated_primitive = get_primitive_structure(decorated_primitive,
symprec=symprec)
decorated_primitive.wrap()
primitive_chemical_symbols: List[Tuple[str, ...]] = []
for atom in decorated_primitive:
sublattice = chemical_symbols.index(atom.symbol)
primitive_chemical_symbols.append(sorted_symbols[sublattice - 1])
for symbols in allowed_species:
if tuple(sorted(symbols)) in primitive_chemical_symbols:
index = primitive_chemical_symbols.index(tuple(sorted(symbols)))
primitive_chemical_symbols[index] = symbols
return decorated_primitive, primitive_chemical_symbols
def get_atom_kinds(atoms, scale, props={}):
tstart = time.time()
if atoms.info and 'kinds' in atoms.info:
assert len(atoms.info['kinds']) == len(
atoms), """ \n\n kinds not equal to number of atoms.
You increase atoms by *[x, x, x]? Please set atoms.info['kinds'] again.
Or remove the original one.\n"""
kinds = list(set(atoms.info['kinds']))
else:
atoms.info['kinds'] = atoms.get_chemical_symbols()
kinds = list(set(atoms.info['kinds']))
atom_kinds = {}
for kind in kinds:
atom_kinds[kind] = {}
element = kind.split('_')[0]
number = chemical_symbols.index(element)
inds = [
atom.index for atom in atoms
if atoms.info['kinds'][atom.index] == kind
]
color = jmol_colors[number]
radius = covalent_radii[number]
atom_kinds[kind]['element'] = element
atom_kinds[kind]['indexs'] = inds
atom_kinds[kind]['positions'] = np.round(atoms[inds].positions,
decimals=2)
atom_kinds[kind]['number'] = number
atom_kinds[kind]['material'] = {
'diffuseColor': tuple(color),
'transparency': 0.01
}
atom_kinds[kind]['sphere'] = {'radius': radius * scale}
atom_kinds[kind]['balltype'] = None
# bond
atom_kinds[kind]['lengths'] = []
atom_kinds[kind]['centers'] = []
atom_kinds[kind]['rotations'] = []
if props:
if kind in props.keys():
for prop, value in props[kind].items():
atom_kinds[kind][prop] = value
# print('get_atom_kinds: {0:10.2f} s'.format(time.time() - tstart))
return atom_kinds
def write_lammps_atoms(self, atoms):
"""Write atoms infor for LAMMPS"""
fileobj = 'lammps_atoms'
if isinstance(fileobj, str):
fileobj = open(fileobj, 'w')
write_lammps_data(fileobj, atoms,
specorder=atoms.types,
speclist=(atoms.get_tags() + 1),
)
# masses
fileobj.write('\nMasses\n\n')
for i, typ in enumerate(atoms.types):
cs = atoms.split_symbol(typ)[0]
fileobj.write('%6d %g # %s -> %s\n' %
(i + 1,
atomic_masses[chemical_symbols.index(cs)],
typ, cs))
# bonds
btypes, blist = self.get_bonds(atoms)
fileobj.write('\n' + str(len(btypes)) + ' bond types\n')
fileobj.write(str(len(blist)) + ' bonds\n')
fileobj.write('\nBonds\n\n')
for ib, bvals in enumerate(blist):
fileobj.write('%8d %6d %6d %6d\n' %
(ib + 1, bvals[0] + 1, bvals[1] + 1, bvals[2] + 1))
# angles
atypes, alist = self.get_angles()
fileobj.write('\n' + str(len(atypes)) + ' angle types\n')
fileobj.write(str(len(alist)) + ' angles\n')
fileobj.write('\nAngles\n\n')
for ia, avals in enumerate(alist):
fileobj.write('%8d %6d %6d %6d %6d\n' %
(ia + 1, avals[0] + 1,
avals[1] + 1, avals[2] + 1, avals[3] + 1))
return btypes, atypes
def get_atom_kinds(atoms, props={}):
# symbols = atoms.symbols
# formula = atoms.symbols.formula
# atom_kinds = formula.count()
tstart = time.time()
if hasattr(atoms, 'kinds'):
kinds = list(set(atoms.kinds))
else:
atoms.kinds = atoms.get_chemical_symbols()
kinds = list(set(atoms.kinds))
# print(kinds)
atom_kinds = {}
for kind in kinds:
atom_kinds[kind] = {}
element = kind.split('_')[0]
print(kind, element)
number = chemical_symbols.index(element)
inds = [
atom.index for atom in atoms if atoms.kinds[atom.index] == kind
]
color = jmol_colors[number]
radius = covalent_radii[number]
atom_kinds[kind]['element'] = element
atom_kinds[kind]['positions'] = atoms[inds].positions
atom_kinds[kind]['number'] = number
atom_kinds[kind]['color'] = color
atom_kinds[kind]['transmit'] = 1.0
atom_kinds[kind]['radius'] = radius
atom_kinds[kind]['balltype'] = None
# bond
atom_kinds[kind]['lengths'] = []
atom_kinds[kind]['centers'] = []
atom_kinds[kind]['normals'] = []
atom_kinds[kind]['verts'] = []
atom_kinds[kind]['faces'] = []
if props:
if kind in props.keys():
for prop, value in props[kind].items():
atom_kinds[kind][prop] = value
print('get_atom_kinds: {0:10.2f} s'.format(time.time() - tstart))
return atom_kinds
def set_missing_parameters(self):
"""Verify that all necessary variables are set.
"""
symbols = self.atoms.get_chemical_symbols()
# If unspecified default to atom types in alphabetic order
if not self.parameters.specorder:
self.parameters.specorder = sorted(set(symbols))
# !TODO: handle cases were setting masses actual lead to errors
if not self.parameters.masses:
self.parameters.masses = []
for type_id, specie in enumerate(self.parameters.specorder):
mass = atomic_masses[chemical_symbols.index(specie)]
self.parameters.masses += [
"{0:d} {1:f}".format(type_id + 1, mass)
]
# set boundary condtions
if not self.parameters.boundary:
b_str = " ".join(["fp"[int(x)] for x in self.atoms.get_pbc()])
self.parameters.boundary = b_str
def get_atomic_numbers(formula, return_count=False):
''' Return the atomic numbers associated with a chemical formula.
Parameters
----------
formula : string
A chemical formula to parse into atomic numbers.
return_count : bool
Return the count of each element in the formula.
Returns
-------
numbers : ndarray (n,)
Element numbers in associated species.
counts : ndarray (n,)
Count of each element in a species.
'''
parse = re.findall('[A-Z][a-z]?|[0-9]+', formula)
values = {}
for i, e in enumerate(parse):
if e.isdigit():
values[parse[i - 1]] += int(e) - 1
else:
if e not in values:
values[e] = 1
else:
values[e] += 1
numbers = np.array([chemical_symbols.index(k) for k in values.keys()])
srt = np.argsort(numbers)
numbers = numbers[srt]
if return_count:
counts = np.array([v for v in values.values()])[srt]
return numbers, counts
return numbers
def default_atom_kind(element, positions, color = 'VESTA'):
"""
"""
from ase.data import chemical_symbols
from ase.data.colors import jmol_colors, cpk_colors
from blase.default_data import vesta_color
atom_kind = {}
number = chemical_symbols.index(element)
if color.upper() == 'JMOL':
color = jmol_colors[number]
elif color.upper() == 'CPK':
color = jmol_colors[number]
elif color.upper() == 'VESTA':
color = vesta_color[element]
radius = covalent_radii[number]
atom_kind['element'] = element
atom_kind['color'] = color
atom_kind['transmit'] = 1.0
atom_kind['radius'] = radius
atom_kind['positions'] = positions
atom_kind['balltype'] = None
return atom_kind
def default_bond_kind(element, color = 'VESTA'):
"""
"""
from ase.data import chemical_symbols
from ase.data.colors import jmol_colors, cpk_colors
from blase.default_data import vesta_color
bond_kind = {}
number = chemical_symbols.index(element)
if color.upper() == 'JMOL':
color = jmol_colors[number]
elif color.upper() == 'CPK':
color = jmol_colors[number]
elif color.upper() == 'VESTA':
color = vesta_color[element]
bond_kind['element'] = element
bond_kind['color'] = color
bond_kind['transmit'] = 1.0
bond_kind['lengths'] = []
bond_kind['centers'] = []
bond_kind['normals'] = []
bond_kind['verts'] = []
bond_kind['faces'] = []
return bond_kind
def classify(tilde_obj):
if sum(tilde_obj.structures[-1].get_pbc()) == 3: # only those initially 3-periodic
zi, mi = tilde_obj.info['cellpar'].index(max(tilde_obj.info['cellpar'][0:3])), tilde_obj.info['cellpar'].index(min(tilde_obj.info['cellpar'][0:3]))
cmpveci = [i for i in range(3) if i not in [zi, mi]][0]
# vacuum per one atom (av)
atoms_volume = 0.0
for i in tilde_obj.structures[-1]:
atoms_volume += 4/3 * math.pi * (covalent_radii[ chemical_symbols.index( i.symbol ) ] + r_EC) ** 3
av = (abs(det(tilde_obj.structures[-1].cell)) - atoms_volume)/len(tilde_obj.structures[-1])
#print "av:", av
# too much vacuum
if av > ADDED_VACUUM_3D_TOL:
# 2D
if tilde_obj.info['cellpar'][cmpveci] * L < tilde_obj.info['cellpar'][zi]:
tilde_obj.info['techs'].append( 'vacuum %sA' % int(round(tilde_obj.info['cellpar'][zi])) )
tilde_obj.structures[-1].set_pbc((True, True, False))
# TODO: 1D ?
# 0D
elif av > ADDED_VACUUM_3D_TOL*4: # TODO
tilde_obj.structures[-1].set_pbc((False, False, False))
# extend the last ASE object
tilde_obj.structures[-1].periodicity = int(sum(tilde_obj.structures[-1].get_pbc()))
tilde_obj.structures[-1].dims = abs(det(tilde_obj.structures[-1].cell))
# TODO: area for surfaces
tilde_obj.info['dims'] = tilde_obj.structures[-1].dims
tilde_obj.info['periodicity'] = tilde_obj.structures[-1].periodicity
return tilde_obj
def write_lammps_atoms(self, atoms):
"""Write atoms infor for LAMMPS"""
fileobj = self.prefix + '_atoms'
if isinstance(fileobj, str):
fileobj = open(fileobj, 'w')
# header
fileobj.write(fileobj.name + ' (by ' + str(self.__class__) + ')\n\n')
fileobj.write(str(len(atoms)) + ' atoms\n')
fileobj.write(str(len(atoms.types)) + ' atom types\n')
btypes, blist = self.get_bonds(atoms)
if len(blist):
fileobj.write(str(len(blist)) + ' bonds\n')
fileobj.write(str(len(btypes)) + ' bond types\n')
atypes, alist = self.get_angles()
if len(alist):
fileobj.write(str(len(alist)) + ' angles\n')
fileobj.write(str(len(atypes)) + ' angle types\n')
dtypes, dlist = self.get_dihedrals(alist, atypes)
if len(dlist):
fileobj.write(str(len(dlist)) + ' dihedrals\n')
fileobj.write(str(len(dtypes)) + ' dihedral types\n')
# cell
p = prism(atoms.get_cell())
xhi, yhi, zhi, xy, xz, yz = p.get_lammps_prism_str()
fileobj.write('\n0.0 %s xlo xhi\n' % xhi)
fileobj.write('0.0 %s ylo yhi\n' % yhi)
fileobj.write('0.0 %s zlo zhi\n' % zhi)
# atoms
fileobj.write('\nAtoms\n\n')
tag = atoms.get_tags()
for i, r in enumerate(map(p.pos_to_lammps_str,
atoms.get_positions())):
q = 0 # charge will be overwritten
fileobj.write('%6d %3d %3d %s %s %s %s' % ((i + 1, 1,
tag[i] + 1,
q)
+ tuple(r)))
fileobj.write(' # ' + atoms.types[tag[i]] + '\n')
# velocities
velocities = atoms.get_velocities()
if velocities is not None:
fileobj.write('\nVelocities\n\n')
for i, v in enumerate(velocities):
fileobj.write('%6d %g %g %g\n' %
(i + 1, v[0], v[1], v[2]))
# masses
fileobj.write('\nMasses\n\n')
for i, typ in enumerate(atoms.types):
cs = atoms.split_symbol(typ)[0]
fileobj.write('%6d %g # %s -> %s\n' %
(i + 1,
atomic_masses[chemical_symbols.index(cs)],
typ, cs))
# bonds
if len(blist):
fileobj.write('\nBonds\n\n')
for ib, bvals in enumerate(blist):
fileobj.write('%8d %6d %6d %6d ' %
(ib + 1, bvals[0] + 1, bvals[1] + 1,
bvals[2] + 1))
fileobj.write('# ' + btypes[bvals[0]] + '\n')
# angles
if len(alist):
fileobj.write('\nAngles\n\n')
for ia, avals in enumerate(alist):
fileobj.write('%8d %6d %6d %6d %6d ' %
(ia + 1, avals[0] + 1,
avals[1] + 1, avals[2] + 1, avals[3] + 1))
fileobj.write('# ' + atypes[avals[0]] + '\n')
# dihedrals
if len(dlist):
fileobj.write('\nDihedrals\n\n')
for i, dvals in enumerate(dlist):
fileobj.write('%8d %6d %6d %6d %6d %6d ' %
(i + 1, dvals[0] + 1,
dvals[1] + 1, dvals[2] + 1,
dvals[3] + 1, dvals[4] + 1))
fileobj.write('# ' + dtypes[dvals[0]] + '\n')
return btypes, atypes, dtypes
def write_lammps_atoms(self, atoms):
"""Write atoms infor for LAMMPS"""
fileobj = self.prefix + '_atoms'
if isinstance(fileobj, str):
fileobj = open(fileobj, 'w')
# header
fileobj.write(fileobj.name + ' (by ' + str(self.__class__) + ')\n\n')
fileobj.write(str(len(atoms)) + ' atoms\n')
fileobj.write(str(len(atoms.types)) + ' atom types\n')
btypes, blist = self.get_bonds(atoms)
if len(blist):
fileobj.write(str(len(blist)) + ' bonds\n')
fileobj.write(str(len(btypes)) + ' bond types\n')
atypes, alist = self.get_angles()
if len(alist):
fileobj.write(str(len(alist)) + ' angles\n')
fileobj.write(str(len(atypes)) + ' angle types\n')
dtypes, dlist = self.get_dihedrals(alist, atypes)
if len(dlist):
fileobj.write(str(len(dlist)) + ' dihedrals\n')
fileobj.write(str(len(dtypes)) + ' dihedral types\n')
# cell
p = prism(atoms.get_cell())
xhi, yhi, zhi, xy, xz, yz = p.get_lammps_prism_str()
fileobj.write('\n0.0 %s xlo xhi\n' % xhi)
fileobj.write('0.0 %s ylo yhi\n' % yhi)
fileobj.write('0.0 %s zlo zhi\n' % zhi)
# atoms
fileobj.write('\nAtoms\n\n')
tag = atoms.get_tags()
for i, r in enumerate(map(p.pos_to_lammps_str, atoms.get_positions())):
q = 0 # charge will be overwritten
fileobj.write('%6d %3d %3d %s %s %s %s' %
((i + 1, 1, tag[i] + 1, q) + tuple(r)))
fileobj.write(' # ' + atoms.types[tag[i]] + '\n')
# velocities
velocities = atoms.get_velocities()
if velocities is not None:
fileobj.write('\nVelocities\n\n')
for i, v in enumerate(velocities):
fileobj.write('%6d %g %g %g\n' % (i + 1, v[0], v[1], v[2]))
# masses
fileobj.write('\nMasses\n\n')
for i, typ in enumerate(atoms.types):
cs = atoms.split_symbol(typ)[0]
fileobj.write(
'%6d %g # %s -> %s\n' %
(i + 1, atomic_masses[chemical_symbols.index(cs)], typ, cs))
# bonds
if len(blist):
fileobj.write('\nBonds\n\n')
for ib, bvals in enumerate(blist):
fileobj.write(
'%8d %6d %6d %6d ' %
(ib + 1, bvals[0] + 1, bvals[1] + 1, bvals[2] + 1))
fileobj.write('# ' + btypes[bvals[0]] + '\n')
# angles
if len(alist):
fileobj.write('\nAngles\n\n')
for ia, avals in enumerate(alist):
fileobj.write('%8d %6d %6d %6d %6d ' %
(ia + 1, avals[0] + 1, avals[1] + 1,
avals[2] + 1, avals[3] + 1))
fileobj.write('# ' + atypes[avals[0]] + '\n')
# dihedrals
if len(dlist):
fileobj.write('\nDihedrals\n\n')
for i, dvals in enumerate(dlist):
fileobj.write('%8d %6d %6d %6d %6d %6d ' %
(i + 1, dvals[0] + 1, dvals[1] + 1, dvals[2] + 1,
dvals[3] + 1, dvals[4] + 1))
fileobj.write('# ' + dtypes[dvals[0]] + '\n')
return btypes, atypes, dtypes
def write_ion(fileobj, atoms, pseudo_dir=None, scaled=True,
add_constraints=False, copy_psp=True, comment=''):
"""
Standard ase io file for reading the sparc-x .ion format
inputs:
atoms (ase atoms object):
an ase atoms object of the system being written to a file
pseudos (list):
a list of the locations of the pseudopotential files to be used
"""
directory = os.path.dirname(fileobj.name)
elements = sorted(list(set(atoms.get_chemical_symbols())))
fileobj.write('# Input File Generated By SPARC ASE Calculator #\n')
fileobj.write('#CELL:')
for comp in atoms.cell:
fileobj.write(' {}'.format(
format(np.linalg.norm(comp) / Bohr, ' .15f')))
fileobj.write('\n#LATVEC\n')
for comp in atoms.cell:
fileobj.write('#')
comp_n = comp / np.linalg.norm(comp) # normalize
for i in comp_n:
fileobj.write(' {}'.format(format(i, ' .15f')))
fileobj.write('\n')
if atoms.pbc is not None:
fileobj.write('#PBC: ')
for condition in atoms.pbc:
fileobj.write(str(condition) + ' ')
fileobj.write('\n')
fileobj.write('# ' + comment + '\n\n\n')
# translate the constraints to usable strings
if add_constraints == True:
cons_indices = []
cons_strings = []
for constraint in atoms.constraints:
name = type(constraint).__name__
if name == 'FixAtoms':
cons_indices += list(constraint.index)
cons_strings += ['0 0 0\n'] * len(constraint.index)
elif name == 'FixedLine' or 'FixedPlane':
line_dir = [int(np.ceil(a)) for a in constraint.dir]
max_dirs = 1 # for FixedLine
# The API of FixedLine and FixedPlane is the same
# except for the need for an inversion of indices
if name == 'FixedPlane':
line_dir = [int(not a) for a in line_dir]
max_dirs = 2
if line_dir.count(1) > max_dirs:
warnings.warn('The .ion filetype can only support'
'freezing entire dimensions (x,y,z).'
'The {} constraint on this atoms'
' Object is being converted to allow '
'movement in any directions in which the'
' atom had some freedom to move'.format(name))
cons_indices += constraint.get_indices()
cons_strings.append('{} {} {}\n'.format(*line_dir))
else:
warnings.warn('The constraint type {} is not supported by'
' the .ion format. This constraint will be'
' ignored')
# just make sure that there aren't repeats
assert len(set(cons_indices)) == len(cons_indices)
for i, element in enumerate(elements):
fileobj.write('ATOM_TYPE: ')
fileobj.write(element + '\n')
fileobj.write('N_TYPE_ATOM: ')
fileobj.write(str(atoms.get_chemical_symbols().count(element)) + '\n')
if add_constraints:
constraints_string = 'RELAX:\n'
magmoms = [float(a) for a in atoms.get_initial_magnetic_moments()]
if magmoms != [0.] * len(atoms):
spin_string = 'SPIN:\n'
# TODO: fix this psuedopotential finding code
if pseudo_dir is not None:#there is a pseudopotential directory provided
if not os.path.isdir(pseudo_dir):#if it is not a directory, then the SPARC_PSP_PATH is used
pseudo_dir = os.environ['SPARC_PSP_PATH']
warnings.warn('The path entered for `pseudo_dir` could '
'not be found. Using SPARC_PSP_PATH and generic pseudopotential '
'file names (<symbol>.pot). Psuedopotentials must be added '
'manually for SPARC to execute successfully.')
fileobj.write('PSEUDO_POT: {}.pot\n'.format(element))
else:
pseudos_in_dir = [a for a in os.listdir(pseudo_dir)
if a.endswith(element+'.pot') or a.endswith(element+'.psp8')]
filename = [a for a in os.listdir(pseudo_dir)
if a.endswith(element+'.pot') or a.endswith(element+'.psp8')]
if len(filename) == 0:
filename = element+'.pot'
warnings.warn('No pseudopotential detected for '+element+'. '
'Using generic filename ('+element +
'.pot). The pseudopotential '
' must be added manually for SPARC to execute successfully.')
else:
if len(filename) > 1:
warnings.warn('Multiple psudopotentials detected for '
'{} ({}).'.format(element, str(filename))+' Using '+filename[0]+'.')
if copy_psp:
filename = filename[0]
full_psp_path = os.path.abspath(os.path.join(pseudo_dir, filename))
full_dest_path = os.path.abspath(os.path.join(directory, filename))
if full_psp_path != full_dest_path:
#shutil.copyfile(os.path.join(pseudo_dir, filename),
# os.path.join(directory, filename))
shutil.copyfile(full_psp_path, full_dest_path)
else:
filename = os.path.join(pseudo_dir, filename[0])
#os.system('cp $SPARC_PSP_PATH/' + filename + ' .')
fileobj.write('PSEUDO_POT: {}\n'.format(filename))
else:
fileobj.write('PSEUDO_POT: {}.pot\n'.format(element))
atomic_number = chemical_symbols.index(element)
atomic_mass = atomic_masses_iupac2016[atomic_number]
fileobj.write('ATOMIC_MASS: {}\n'.format(atomic_mass))
if scaled == False:
fileobj.write('COORD:\n')
positions = atoms.get_positions(wrap=False)
positions /= Bohr
else:
fileobj.write('COORD_FRAC:\n')
positions = atoms.get_scaled_positions(wrap=False)
for atom, position in zip(atoms, positions):
if atom.symbol == element:
for component in position:
fileobj.write(' {}'.format(format(component, ' .15f')))
fileobj.write(' # index {}'.format(atom.index))
fileobj.write('\n')
# mess with the constraints
if add_constraints:
if atom.index in cons_indices:
constraints_indices_index = cons_indices.index(
atom.index)
constraints_string += cons_strings[constraints_indices_index]
else:
constraints_string += '1 1 1\n'
if 'spin_string' in locals():
spin_string += format(atom.magmom, ' .15f') + '\n'
# dump in the constraints
if add_constraints:
fileobj.write(constraints_string)
if 'spin_string' in locals():
fileobj.write(spin_string)
fileobj.write('\n\n')
def write_lammps_atoms(self, atoms, connectivities):
"""Write atoms input for LAMMPS"""
fname = self.prefix + '_atoms'
fileobj = open(fname, 'w')
# header
fileobj.write(fileobj.name + ' (by ' + str(self.__class__) + ')\n\n')
fileobj.write(str(len(atoms)) + ' atoms\n')
fileobj.write(str(len(atoms.types)) + ' atom types\n')
blist = connectivities['bonds']
if len(blist):
btypes = connectivities['bond types']
fileobj.write(str(len(blist)) + ' bonds\n')
fileobj.write(str(len(btypes)) + ' bond types\n')
alist = connectivities['angles']
if len(alist):
atypes = connectivities['angle types']
fileobj.write(str(len(alist)) + ' angles\n')
fileobj.write(str(len(atypes)) + ' angle types\n')
dlist = connectivities['dihedrals']
if len(dlist):
dtypes = connectivities['dihedral types']
fileobj.write(str(len(dlist)) + ' dihedrals\n')
fileobj.write(str(len(dtypes)) + ' dihedral types\n')
# cell
p = Prism(atoms.get_cell())
xhi, yhi, zhi, xy, xz, yz = p.get_lammps_prism_str()
fileobj.write('\n0.0 %s xlo xhi\n' % xhi)
fileobj.write('0.0 %s ylo yhi\n' % yhi)
fileobj.write('0.0 %s zlo zhi\n' % zhi)
# atoms
fileobj.write('\nAtoms\n\n')
tag = atoms.get_tags()
if atoms.has('molid'):
molid = atoms.get_array('molid')
else:
molid = [1] * len(atoms)
for i, r in enumerate(p.positions_to_lammps_strs(
atoms.get_positions())):
atype = atoms.types[tag[i]]
if len(atype) < 2:
atype = atype + ' '
q = self.data['one'][atype][2]
fileobj.write('%6d %3d %3d %s %s %s %s' %
((i + 1, molid[i], tag[i] + 1, q) + tuple(r)))
fileobj.write(' # ' + atoms.types[tag[i]] + '\n')
# velocities
velocities = atoms.get_velocities()
if velocities is not None:
fileobj.write('\nVelocities\n\n')
for i, v in enumerate(velocities):
fileobj.write('%6d %g %g %g\n' % (i + 1, v[0], v[1], v[2]))
# masses
fileobj.write('\nMasses\n\n')
for i, typ in enumerate(atoms.types):
cs = atoms.split_symbol(typ)[0]
fileobj.write(
'%6d %g # %s -> %s\n' %
(i + 1, atomic_masses[chemical_symbols.index(cs)], typ, cs))
# bonds
if len(blist):
fileobj.write('\nBonds\n\n')
for ib, bvals in enumerate(blist):
fileobj.write(
'%8d %6d %6d %6d ' %
(ib + 1, bvals[0] + 1, bvals[1] + 1, bvals[2] + 1))
try:
fileobj.write('# ' + btypes[bvals[0]])
except:
pass
fileobj.write('\n')
# angles
if len(alist):
fileobj.write('\nAngles\n\n')
for ia, avals in enumerate(alist):
fileobj.write('%8d %6d %6d %6d %6d ' %
(ia + 1, avals[0] + 1, avals[1] + 1,
avals[2] + 1, avals[3] + 1))
try:
fileobj.write('# ' + atypes[avals[0]])
except:
pass
fileobj.write('\n')
# dihedrals
if len(dlist):
fileobj.write('\nDihedrals\n\n')
for i, dvals in enumerate(dlist):
fileobj.write('%8d %6d %6d %6d %6d %6d ' %
(i + 1, dvals[0] + 1, dvals[1] + 1, dvals[2] + 1,
dvals[3] + 1, dvals[4] + 1))
try:
fileobj.write('# ' + dtypes[dvals[0]])
except:
pass
fileobj.write('\n')
x = []
while len(x) < n:
a, b, c = array([numpy_random.normal(init[0], s), numpy_random.normal(init[1], s), numpy_random.normal(init[2], s)])
if abs(a) < 1 and abs(b) < 1 and abs(c) < 1:
x.append([a,b,c])
X.extend(x)
X = array(X)[:N]
return X
if DISTRIB == 'RANDOM':
set_x, set_y = [random.choice(chemical_symbols) for i in range(N)], [random.choice(chemical_symbols) for i in range(N)]
set_z = [round(random.uniform(0.1, 15.0), 2) for i in range(N)]
data, ref = [], []
for i in range(N):
formula = set_x[i] + set_y[i]
set_x[i] = get_element_group(chemical_symbols.index(set_x[i]))
set_y[i] = get_element_group(chemical_symbols.index(set_y[i]))
data.append(Point([set_x[i], set_y[i], set_z[i]], formula))
ref.append([set_x[i], set_y[i], set_z[i]])
else:
nte = len(chemical_symbols)
G = gaussian_distribution(N, k_from_n(N))
set_x = (G[:,0] + 1)/2*nte
set_x = map(lambda x: int(math.floor(x)), set_x.tolist())
set_y = (G[:,1] + 1)/2*nte
set_y = map(lambda x: int(math.floor(x)), set_y.tolist())
set_z = (G[:,2] + 1)/2*15
def build_shell_dist_fig(bimet, show=False):
"""
Creates shell distribution figure of
BimetallicResult
Args:
Returns:
(plt.Figure)
"""
# get atoms object
atoms = bimet.build_atoms_obj().copy()
# build shells dictionary
shape = bimet.shape
num_shells = bimet.nanoparticle.num_shells
shells_dict = build_atoms_in_shell_dict(shape, num_shells)
# list of all shells in NP (0 is core atom)
shell_ls = sorted(shells_dict)
# calc total and metal counts for each shell
tot_count = np.zeros(len(shell_ls))
m1_count = np.zeros(len(shell_ls))
m2_count = np.zeros(len(shell_ls))
for i, shell in enumerate(sorted(shells_dict)):
# indices of atoms in current shell
indices = shells_dict[shell]
# total atom count of current shell
tot_count[i] = len(indices)
# metal type counts for current shell
m1_count[i] = (atoms[indices].symbols == bimet.metal1).sum()
m2_count[i] = (atoms[indices].symbols == bimet.metal2).sum()
# normalize counts to concentrations
norm_m1 = m1_count / tot_count
norm_m2 = m2_count / tot_count
fig, axes = plt.subplots(2, 1, sharex=True)
ax1, ax2 = axes
m1_color = jmol_colors[chemical_symbols.index(bimet.metal1)]
m2_color = jmol_colors[chemical_symbols.index(bimet.metal2)]
ax1.plot(shell_ls, m1_count, 'o-', markeredgecolor='k',
color=m1_color, markersize=8, label=bimet.metal1)
ax1.plot(shell_ls, m2_count, 'o-', markeredgecolor='k',
color=m2_color, markersize=8, label=bimet.metal2)
ax1.legend(loc='upper left')
ax1.set_xticks(list(range(1, bimet.nanoparticle.num_shells + 1)))
# set ylim to maximum number of atoms on surface
high = int(round((tot_count[-1] + 10) * 10) / 10)
ax1.set_ylim(-2, high)
ax1.set_ylabel('# of Atoms')
ax2.plot(shell_ls, norm_m1, 'o-', markeredgecolor='k',
color=m1_color, markersize=8)
ax2.plot(shell_ls, norm_m2, 'o-', markeredgecolor='k',
color=m2_color, markersize=8)
ax2.set_ylabel('Concentration')
yticks = [0, 0.25, 0.5, 0.75, 1]
ax2.set_yticks(yticks)
ax2.set_yticklabels(['{:,.0%}'.format(x) for x in yticks])
ax2.set_xticks(shell_ls)
# create xtick labels based on shell number
xticklabels = ['Shell %i' % i for i in shell_ls]
xticklabels[0] = 'Core'
xticklabels[-1] = 'Surface'
ax2.set_xticklabels(xticklabels, rotation=45)
fig.suptitle(bimet.build_chem_formula(latex=True) + ' - %s' % shape)
fig.tight_layout(rect=(0, 0, 1, 0.9))
if show:
plt.show()
return fig
def classify(tilde_obj):
if len(tilde_obj.info['elements']) == 1: return tilde_obj
C_site = [e for e in tilde_obj.info['elements'] if e in Perovskite_Structure.C]
if not C_site: return tilde_obj
A_site = [e for e in tilde_obj.info['elements'] if e in Perovskite_Structure.A]
B_site = [e for e in tilde_obj.info['elements'] if e in Perovskite_Structure.B]
# proportional content coefficient D_prop
AB, C = 0, 0
for i in set(A_site + B_site):
AB += tilde_obj.info['contents'][ tilde_obj.info['elements'].index(i) ]
for i in C_site:
C += tilde_obj.info['contents'][ tilde_obj.info['elements'].index(i) ]
try: D_prop = float(C) / AB
except ZeroDivisionError: return tilde_obj
# 2-component pseudo-perovskites
# TODO account other pseudo-perovskites e.g. Mn2O3 or binary-metal ones
if tilde_obj.info['elements'][0] in ['W', 'Re'] and len(tilde_obj.info['elements']) == 2:
if round(D_prop) == 3: tilde_obj.info['tags'].append(0x4)
return tilde_obj
if not A_site or not B_site: return tilde_obj
if not 1.3 < D_prop < 2.3: return tilde_obj # D_prop grows for 2D adsorption cases (>1.9)
n_combs, n_offs = 0, 0
for A in A_site:
for B in B_site:
if B == A: continue
for C in C_site:
rA = covalent_radii[chemical_symbols.index(A)]
rB = covalent_radii[chemical_symbols.index(B)]
rC = covalent_radii[chemical_symbols.index(C)]
# Goldschmidt tolerance factor
# t = (rA + rC) / sqrt(2) * (rB + rC)
# 0.71 =< t =< 1.2
# t < 0.71 ilmenite, corundum or KNbO3 structure
# t > 1 hexagonal perovskite polytypes
# http://en.wikipedia.org/wiki/Goldschmidt_tolerance_factor
factor = (rA + rC) / (math.sqrt(2) * (rB + rC))
if not 0.71 <= factor <= 1.4: n_offs += 1
n_combs += 1
if n_offs == n_combs: return tilde_obj
tilde_obj.info['tags'].append(0x4)
if tilde_obj.structures[-1].periodicity != 3: return tilde_obj # all below is for 3d case : TODO
contents = []
impurities, A_hosts, B_hosts = {}, {}, {}
# What is a defect?
# Empirical criteria of defect for ab initio modeling: =< 25% of the content
for n, i in enumerate(tilde_obj.info['elements']):
contents.append( [n, i, float(tilde_obj.info['contents'][n])/sum(tilde_obj.info['contents'])] )
contents = sorted(contents, key = lambda i: i[2])
for num in range(len(contents)):
try: contents[num+1]
except IndexError: break
# defect content differs at least 2x from the smallest content; defect partial weight <= 1/16
if contents[num][2] <= 0.0625 and contents[num][2] / contents[num+1][2] <= 0.5:
impurities[ contents[num][1] ] = tilde_obj.info['contents'][ contents[num][0] ] # ex: ['Fe', 2]
elif contents[num][1] in Perovskite_Structure.A:
A_hosts[ contents[num][1] ] = tilde_obj.info['contents'][ contents[num][0] ]
elif contents[num][1] in Perovskite_Structure.B:
B_hosts[ contents[num][1] ] = tilde_obj.info['contents'][ contents[num][0] ]
#print impurities, A_hosts, B_hosts
if len(A_hosts) > 1 or len(B_hosts) > 1: return tilde_obj # skip complex perovskites and those where an element may occupy either A or B
# A site or B site?
num = 0
for impurity_element, content in six.iteritems(impurities):
e = tilde_obj.info['elements'].index(impurity_element)
tilde_obj.info['elements'].pop(e) # TODO
tilde_obj.info['contents'].pop(e) # TODO
tilde_obj.info['impurity' + str(num)] = impurity_element + str(content) if content > 1 else impurity_element
num += 1
if impurity_element in Perovskite_Structure.A: A_hosts[A_hosts.keys()[0]] += content
elif impurity_element in Perovskite_Structure.B: B_hosts[B_hosts.keys()[0]] += content
for n, i in enumerate(tilde_obj.info['elements']):
if i in A_hosts:
tilde_obj.info['contents'][n] = A_hosts[i] # TODO
elif i in B_hosts:
tilde_obj.info['contents'][n] = B_hosts[i] # TODO
for i in C_site:
c_content = tilde_obj.info['contents'][ tilde_obj.info['elements'].index(i) ]
tot_content = sum(tilde_obj.info['contents'])
D_O = float(c_content) / tot_content
if D_O < 0.6: # C-site lack
tilde_obj.info['lack'] = i
break # TODO
return tilde_obj
def write_lammps_data(filename, atoms, atom_types, comment=None, cutoff=None,
molecule_ids=None, charges=None, units='metal',
bond_types=None, angle_types=None, dihedral_types=None):
if isinstance(filename, basestring):
fh = open(filename, 'w')
else:
fh = filename
if comment is None:
comment = 'lammpslib autogenerated data file'
fh.write(comment.strip() + '\n\n')
fh.write('{0} atoms\n'.format(len(atoms)))
fh.write('{0} atom types\n'.format(len(atom_types)))
if bond_types:
from matscipy.neighbours import neighbour_list
i_list, j_list = neighbour_list('ij', atoms, cutoff)
print 'Bonds:'
bonds = []
for bond_type, (Z1, Z2) in enumerate(bond_types):
bond_mask = (atoms.numbers[i_list] == Z1) & (atoms.numbers[j_list] == Z2)
print (Z1, Z2), bond_mask.sum()
for (I, J) in zip(i_list[bond_mask], j_list[bond_mask]):
#NB: LAMMPS uses 1-based indices for bond types and particle indices
bond = (bond_type+1, I+1, J+1)
bonds.append(bond)
print
if len(bonds) > 0:
fh.write('{0} bonds\n'.format(len(bonds)))
fh.write('{0} bond types\n'.format(len(bond_types)))
if angle_types:
print 'Angles:'
angle_count = { angle : 0 for angle in angle_types }
angles = []
for I in range(len(atoms)):
for J in j_list[i_list == I]:
for K in j_list[i_list == J]:
if J < K:
continue
Zi, Zj, Zk = atoms.numbers[[I, J, K]]
if (Zj, Zi, Zk) in angle_types:
angle = (angle_types.index((Zj, Zi, Zk))+1, J+1, I+1, K+1)
angle_count[(Zj, Zi, Zk)] += 1
angles.append(angle)
for angle in angle_types:
print angle, angle_count[angle]
print
if len(angles) > 0:
fh.write('{0} angles\n'.format(len(angles)))
fh.write('{0} angle types\n'.format(len(angle_types)))
if dihedral_types:
print 'Dihedrals:'
dihedral_count = { dihedral : 0 for dihedral in dihedral_types }
dihedrals = []
for I in range(len(atoms)):
for J in j_list[i_list == I]:
for K in j_list[i_list == J]:
for L in j_list[i_list == K]:
Zi, Zj, Zk, Zl = atoms.numbers[[I, J, K, L]]
if (Zi, Zj, Zk, Zl) in dihedral_types:
dihedral = (dihedral_types.index((Zi, Zj, Zk, Zl))+1,
I+1, J+1, K+1, L+1)
dihedral_count[(Zi, Zj, Zk, Zl)] += 1
dihedrals.append(dihedral)
for dihedral in dihedral_types:
print dihedral, dihedral_count[dihedral]
print
if len(dihedrals) > 0:
fh.write('{0} dihedrals\n'.format(len(dihedrals)))
fh.write('{0} dihedral types\n'.format(len(dihedral_types)))
fh.write('\n')
cell, coord_transform = convert_cell(atoms.get_cell())
fh.write('{0:16.8e} {1:16.8e} xlo xhi\n'.format(0.0, cell[0, 0]))
fh.write('{0:16.8e} {1:16.8e} ylo yhi\n'.format(0.0, cell[1, 1]))
fh.write('{0:16.8e} {1:16.8e} zlo zhi\n'.format(0.0, cell[2, 2]))
fh.write('{0:16.8e} {1:16.8e} {2:16.8e} xy xz yz\n'.format(cell[0, 1], cell[0, 2], cell[1, 2]))
fh.write('\nMasses\n\n')
sym_mass = {}
masses = atoms.get_masses()
symbols = atoms.get_chemical_symbols()
for sym in atom_types:
for i in range(len(atoms)):
if symbols[i] == sym:
sym_mass[sym] = masses[i] / unit_convert("mass", units)
break
else:
sym_mass[sym] = atomic_masses[chemical_symbols.index(sym)] / unit_convert("mass", units)
for (sym, typ) in sorted(atom_types.items(), key=operator.itemgetter(1)):
fh.write('{0} {1}\n'.format(typ, sym_mass[sym]))
fh.write('\nAtoms # full\n\n')
if molecule_ids is None:
molecule_ids = np.zeros(len(atoms), dtype=int)
if charges is None:
charges = atoms.get_initial_charges()
for i, (sym, mol, q, pos) in enumerate(zip(symbols, molecule_ids,
charges, atoms.get_positions())):
typ = atom_types[sym]
fh.write('{0} {1} {2} {3:16.8e} {4:16.8e} {5:16.8e} {6:16.8e}\n'
.format(i+1, mol, typ, q, pos[0], pos[1], pos[2]))
if bond_types and len(bonds) > 0:
fh.write('\nBonds\n\n')
for idx, bond in enumerate(bonds):
fh.write('{0} {1} {2} {3}\n'
.format(*[idx+1] + list(bond)))
if angle_types and len(angles) > 0:
fh.write('\nAngles\n\n')
for idx, angle in enumerate(angles):
fh.write('{0} {1} {2} {3} {4}\n'
.format(*[idx+1] + list(angle)))
if dihedral_types and len(dihedrals) > 0:
fh.write('\nDihedrals\n\n')
for idx, dihedral in enumerate(dihedrals):
fh.write('{0} {1} {2} {3} {4} {5}\n'
.format(*[idx+1] + list(dihedral)))
if isinstance(filename, basestring):
fh.close()
def write_lammps_atoms(self, atoms, connectivities):
"""Write atoms input for LAMMPS"""
fname = self.prefix + '_atoms'
fileobj = open(fname, 'w')
# header
fileobj.write(fileobj.name + ' (by ' + str(self.__class__) + ')\n\n')
fileobj.write(str(len(atoms)) + ' atoms\n')
fileobj.write(str(len(atoms.types)) + ' atom types\n')
blist = connectivities['bonds']
if len(blist):
btypes = connectivities['bond types']
fileobj.write(str(len(blist)) + ' bonds\n')
fileobj.write(str(len(btypes)) + ' bond types\n')
alist = connectivities['angles']
if len(alist):
atypes = connectivities['angle types']
fileobj.write(str(len(alist)) + ' angles\n')
fileobj.write(str(len(atypes)) + ' angle types\n')
dlist = connectivities['dihedrals']
if len(dlist):
dtypes = connectivities['dihedral types']
fileobj.write(str(len(dlist)) + ' dihedrals\n')
fileobj.write(str(len(dtypes)) + ' dihedral types\n')
# cell
p = Prism(atoms.get_cell())
xhi, yhi, zhi, xy, xz, yz = p.get_lammps_prism_str()
fileobj.write('\n0.0 %s xlo xhi\n' % xhi)
fileobj.write('0.0 %s ylo yhi\n' % yhi)
fileobj.write('0.0 %s zlo zhi\n' % zhi)
# atoms
fileobj.write('\nAtoms\n\n')
tag = atoms.get_tags()
if atoms.has('molid'):
molid = atoms.get_array('molid')
else:
molid = [1] * len(atoms)
for i, r in enumerate(map(p.pos_to_lammps_str,
atoms.get_positions())):
q = self.data['one'][atoms.types[tag[i]]][2]
fileobj.write('%6d %3d %3d %s %s %s %s' % ((i + 1, molid[i],
tag[i] + 1,
q)
+ tuple(r)))
fileobj.write(' # ' + atoms.types[tag[i]] + '\n')
# velocities
velocities = atoms.get_velocities()
if velocities is not None:
fileobj.write('\nVelocities\n\n')
for i, v in enumerate(velocities):
fileobj.write('%6d %g %g %g\n' %
(i + 1, v[0], v[1], v[2]))
# masses
fileobj.write('\nMasses\n\n')
for i, typ in enumerate(atoms.types):
cs = atoms.split_symbol(typ)[0]
fileobj.write('%6d %g # %s -> %s\n' %
(i + 1,
atomic_masses[chemical_symbols.index(cs)],
typ, cs))
# bonds
if len(blist):
fileobj.write('\nBonds\n\n')
for ib, bvals in enumerate(blist):
fileobj.write('%8d %6d %6d %6d ' %
(ib + 1, bvals[0] + 1, bvals[1] + 1,
bvals[2] + 1))
try:
fileobj.write('# ' + btypes[bvals[0]])
except:
pass
fileobj.write('\n')
# angles
if len(alist):
fileobj.write('\nAngles\n\n')
for ia, avals in enumerate(alist):
fileobj.write('%8d %6d %6d %6d %6d ' %
(ia + 1, avals[0] + 1,
avals[1] + 1, avals[2] + 1, avals[3] + 1))
try:
fileobj.write('# ' + atypes[avals[0]])
except:
pass
fileobj.write('\n')
# dihedrals
if len(dlist):
fileobj.write('\nDihedrals\n\n')
for i, dvals in enumerate(dlist):
fileobj.write('%8d %6d %6d %6d %6d %6d ' %
(i + 1, dvals[0] + 1,
dvals[1] + 1, dvals[2] + 1,
dvals[3] + 1, dvals[4] + 1))
try:
fileobj.write('# ' + dtypes[dvals[0]])
except:
pass
fileobj.write('\n')
def write_cfg(f, a):
"""Write atomic configuration to a CFG-file (native AtomEye format).
See: http://mt.seas.upenn.edu/Archive/Graphics/A/
"""
if isinstance(f, basestring):
f = paropen(f, 'w')
if isinstance(a, list):
if len(a) == 1:
a = a[0]
else:
raise RuntimeError('Cannot write sequence to single .cfg file.')
f.write('Number of particles = %i\n' % len(a))
f.write('A = 1.0 Angstrom\n')
cell = a.get_cell(complete=True)
for i in range(3):
for j in range(3):
f.write('H0(%1.1i,%1.1i) = %f A\n' % (i + 1, j + 1, cell[i, j]))
entry_count = 3
for x in a.arrays.keys():
if x not in cfg_default_fields:
if len(a.get_array(x).shape) == 1:
entry_count += 1
else:
entry_count += a.get_array(x).shape[1]
vels = a.get_velocities()
if isinstance(vels, np.ndarray):
entry_count += 3
else:
f.write('.NO_VELOCITY.\n')
f.write('entry_count = %i\n' % entry_count)
i = 0
for name, aux in a.arrays.items():
if name not in cfg_default_fields:
if len(aux.shape) == 1:
f.write('auxiliary[%i] = %s [a.u.]\n' % (i, name))
i += 1
else:
if aux.shape[1] == 3:
for j in range(3):
f.write('auxiliary[%i] = %s_%s [a.u.]\n' %
(i, name, chr(ord('x') + j)))
i += 1
else:
for j in range(aux.shape[1]):
f.write('auxiliary[%i] = %s_%1.1i [a.u.]\n' %
(i, name, j))
i += 1
# Distinct elements
spos = a.get_scaled_positions()
for i in a:
el = i.symbol
f.write('%f\n' % ase.data.atomic_masses[chemical_symbols.index(el)])
f.write('%s\n' % el)
x, y, z = spos[i.index, :]
s = '%e %e %e ' % (x, y, z)
if isinstance(vels, np.ndarray):
vx, vy, vz = vels[i.index, :]
s = s + ' %e %e %e ' % (vx, vy, vz)
for name, aux in a.arrays.items():
if name not in cfg_default_fields:
if len(aux.shape) == 1:
s += ' %e' % aux[i.index]
else:
s += (aux.shape[1] * ' %e') % tuple(aux[i.index].tolist())
f.write('%s\n' % s)
def bdplotter(task, **kwargs):
'''
bdplotter is based on the fact that phonon DOS/bands and
electron DOS/bands are the objects of the same kind.
1) DOS is formatted precomputed / smeared according to a normal distribution
2) bands are formatted precomputed / interpolated through natural cubic spline function
'''
if task == 'bands': # CRYSTAL, "VASP", EXCITING
results = []
if 'precomputed' in kwargs:
for n in range(len(kwargs['precomputed']['ticks'])):
if kwargs['precomputed']['ticks'][n][1] == 'GAMMA': kwargs['precomputed']['ticks'][n][1] = 'Γ'
for stripe in kwargs['precomputed']['stripes']:
results.append({'color':'#000000', 'data':[], 'ticks':kwargs['precomputed']['ticks']})
for n, val in enumerate(stripe):
results[-1]['data'].append([ kwargs['precomputed']['abscissa'][n], val])
else:
if not 'order' in kwargs: order = sorted( kwargs['values'].keys() ) # TODO
else: order = kwargs['order']
nullstand = '0 0 0'
if not '0 0 0' in kwargs['values']: # possible case when there is shifted Gamma point
nullstand = order[0]
# reduce k if too much
if len(order)>20:
red_order = []
for i in range(0, len(order), int(math.floor(len(order)/10))):
red_order.append(order[i])
order = red_order
for N in range(len( kwargs['values'][nullstand] )):
# interpolate for each curve throughout the BZ
results.append({'color':'#000000', 'data':[], 'ticks':[]})
d = 0.0
x = []
y = []
bz_vec_ref = [0, 0, 0]
for bz in order:
y.append( kwargs['values'][bz][N] )
bz_coords = map(frac2float, bz.split() )
bz_vec_cur = dot( bz_coords, linalg.inv( kwargs['xyz_matrix'] ).transpose() )
bz_vec_dir = map(sum, zip(bz_vec_cur, bz_vec_ref))
bz_vec_ref = bz_vec_cur
d += linalg.norm( bz_vec_dir )
x.append(d)
results[-1]['ticks'].append( [d, bz.replace(' ', '')] )
# end in nullstand point (normally, Gamma)
#y.append(kwargs['values'][nullstand][N])
#if d == 0: d+=0.5
#else: d += linalg.norm( bz_vec_ref )
#x.append(d)
#results[-1]['ticks'].append( [d, nullstand.replace(' ', '')] )
divider = 10 if len(order)<10 else 1.5
step = (max(x)-min(x)) / len(kwargs['values']) / divider
xnew = arange(min(x), max(x)+step/2, step).tolist()
ynew = []
f = NaturalCubicSpline( array(x), array(y) )
for i in xnew:
results[-1]['data'].append([ round( i, 3 ), round( f(i), 3 ) ]) # round to reduce output
return results
elif task == 'dos': # CRYSTAL, VASP, EXCITING
results = []
if 'precomputed' in kwargs:
total_dos = [[i, kwargs['precomputed']['total'][n]] for n, i in enumerate(kwargs['precomputed']['x'])]
else:
tdos = TotalDos( kwargs['eigenvalues'], sigma=kwargs['sigma'] )
tdos.set_draw_area(omega_min=kwargs['omega_min'], omega_max=kwargs['omega_max'], omega_pitch=kwargs['omega_pitch'])
total_dos = tdos.calculate()
results.append({'label':'total', 'color': '#000000', 'data': total_dos})
if 'precomputed' in kwargs:
partial_doses = []
for k in kwargs['precomputed'].keys():
if k in ['x', 'total']: continue
partial_doses.append({ 'label': k, 'data': [[i, kwargs['precomputed'][k][n]] for n, i in enumerate(kwargs['precomputed']['x'])] })
elif 'impacts' in kwargs and 'atomtypes' in kwargs:
# get the order of atoms to evaluate their partial impact
labels = {}
types = []
index, subtractor = 0, 0
for k, atom in enumerate(kwargs['atomtypes']): # determine the order of atoms for the partial impact of every type
if atom not in labels:
#if atom == 'X' and not calc.phonons: # artificial GHOST case for phonons, decrease atomic index
# subtractor += 1
# continue
labels[atom] = index
types.append([k+1-subtractor])
index += 1
else:
types[ labels[atom] ].append(k+1-subtractor)
pdos = PartialDos( kwargs['eigenvalues'], kwargs['impacts'], sigma=kwargs['sigma'] )
pdos.set_draw_area(omega_min=kwargs['omega_min'], omega_max=kwargs['omega_max'], omega_pitch=kwargs['omega_pitch'])
partial_doses = pdos.calculate( types, labels )
# add colors to partials
for i in range(len(partial_doses)):
if partial_doses[i]['label'] == 'X': color = '#000000'
elif partial_doses[i]['label'] == 'H': color = '#CCCCCC'
else:
try: color = jmol_to_hex( jmol_colors[ chemical_symbols.index(partial_doses[i]['label']) ] )
except ValueError: color = '#FFCC66'
partial_doses[i].update({'color': color})
results.extend(partial_doses)
return results
def save(self, calc, session):
'''
Saves tilde_obj into the database
NB: this is the PUBLIC method
@returns checksum, error
'''
checksum = calc.get_checksum()
try:
existing_calc = session.query(model.Calculation).filter(model.Calculation.checksum == checksum).one()
except NoResultFound:
pass
else:
del calc
return None, "This calculation already exists!"
if not calc.download_size:
for f in calc.related_files:
calc.download_size += os.stat(f).st_size
ormcalc = model.Calculation(checksum = checksum)
if calc._calcset:
ormcalc.meta_data = model.Metadata(chemical_formula = calc.info['standard'], download_size = calc.download_size)
for child in session.query(model.Calculation).filter(model.Calculation.checksum.in_(calc._calcset)).all():
ormcalc.children.append(child)
ormcalc.siblings_count = len(ormcalc.children)
ormcalc.nested_depth = calc._nested_depth
else:
# prepare phonon data for saving
# this is actually a dict to list conversion TODO re-structure this
if calc.phonons['modes']:
phonons_json = []
for bzpoint, frqset in calc.phonons['modes'].items():
# re-orientate eigenvectors
for i in range(0, len(calc.phonons['ph_eigvecs'][bzpoint])):
for j in range(0, len(calc.phonons['ph_eigvecs'][bzpoint][i])//3):
eigv = array([calc.phonons['ph_eigvecs'][bzpoint][i][j*3], calc.phonons['ph_eigvecs'][bzpoint][i][j*3+1], calc.phonons['ph_eigvecs'][bzpoint][i][j*3+2]])
R = dot( eigv, calc.structures[-1].cell ).tolist()
calc.phonons['ph_eigvecs'][bzpoint][i][j*3], calc.phonons['ph_eigvecs'][bzpoint][i][j*3+1], calc.phonons['ph_eigvecs'][bzpoint][i][j*3+2] = [round(x, 3) for x in R]
try: irreps = calc.phonons['irreps'][bzpoint]
except KeyError:
empty = []
for i in range(len(frqset)):
empty.append('')
irreps = empty
phonons_json.append({ 'bzpoint':bzpoint, 'freqs':frqset, 'irreps':irreps, 'ph_eigvecs':calc.phonons['ph_eigvecs'][bzpoint] })
if bzpoint == '0 0 0':
phonons_json[-1]['ir_active'] = calc.phonons['ir_active']
phonons_json[-1]['raman_active'] = calc.phonons['raman_active']
if calc.phonons['ph_k_degeneracy']:
phonons_json[-1]['ph_k_degeneracy'] = calc.phonons['ph_k_degeneracy'][bzpoint]
ormcalc.phonons = model.Phonons()
ormcalc.spectra.append( model.Spectra(kind = model.Spectra.PHONON, eigenvalues = json.dumps(phonons_json)) )
# prepare electron data for saving TODO re-structure this
for task in ['dos', 'bands']: # projected?
if calc.electrons[task]:
calc.electrons[task] = calc.electrons[task].todict()
if calc.electrons['dos'] or calc.electrons['bands']:
ormcalc.electrons = model.Electrons(gap = calc.info['bandgap'])
if 'bandgaptype' in calc.info:
ormcalc.electrons.is_direct = 1 if calc.info['bandgaptype'] == 'direct' else -1
ormcalc.spectra.append(model.Spectra(
kind = model.Spectra.ELECTRON,
dos = json.dumps(calc.electrons['dos']),
bands = json.dumps(calc.electrons['bands']),
projected = json.dumps(calc.electrons['projected']),
eigenvalues = json.dumps(calc.electrons['eigvals'])
))
# construct ORM for other props
calc.related_files = list(map(virtualize_path, calc.related_files))
ormcalc.meta_data = model.Metadata(location = calc.info['location'], finished = calc.info['finished'], raw_input = calc.info['input'], modeling_time = calc.info['duration'], chemical_formula = html_formula(calc.info['standard']), download_size = calc.download_size, filenames = json.dumps(calc.related_files))
codefamily = model.Codefamily.as_unique(session, content = calc.info['framework'])
codeversion = model.Codeversion.as_unique(session, content = calc.info['prog'])
codeversion.instances.append( ormcalc.meta_data )
codefamily.versions.append( codeversion )
pot = model.Pottype.as_unique(session, name = calc.info['H'])
pot.instances.append(ormcalc)
ormcalc.recipinteg = model.Recipinteg(kgrid = calc.info['k'], kshift = calc.info['kshift'], smearing = calc.info['smear'], smeartype = calc.info['smeartype'])
ormcalc.basis = model.Basis(kind = calc.info['ansatz'], content = json.dumps(calc.electrons['basis_set']) if calc.electrons['basis_set'] else None)
ormcalc.energy = model.Energy(convergence = json.dumps(calc.convergence), total = calc.info['energy'])
ormcalc.spacegroup = model.Spacegroup(n=calc.info['ng'])
ormcalc.struct_ratios = model.Struct_ratios(chemical_formula=calc.info['standard'], formula_units=calc.info['expanded'], nelem=calc.info['nelem'], dimensions=calc.info['dims'])
if len(calc.tresholds) > 1:
ormcalc.struct_optimisation = model.Struct_optimisation(tresholds=json.dumps(calc.tresholds), ncycles=json.dumps(calc.ncycles))
for n, ase_repr in enumerate(calc.structures):
is_final = True if n == len(calc.structures)-1 else False
struct = model.Structure(step = n, final = is_final)
s = cell_to_cellpar(ase_repr.cell)
struct.lattice = model.Lattice(a=s[0], b=s[1], c=s[2], alpha=s[3], beta=s[4], gamma=s[5], a11=ase_repr.cell[0][0], a12=ase_repr.cell[0][1], a13=ase_repr.cell[0][2], a21=ase_repr.cell[1][0], a22=ase_repr.cell[1][1], a23=ase_repr.cell[1][2], a31=ase_repr.cell[2][0], a32=ase_repr.cell[2][1], a33=ase_repr.cell[2][2])
#rmts = ase_repr.get_array('rmts') if 'rmts' in ase_repr.arrays else [None for j in range(len(ase_repr))]
charges = ase_repr.get_array('charges') if 'charges' in ase_repr.arrays else [None for j in range(len(ase_repr))]
magmoms = ase_repr.get_array('magmoms') if 'magmoms' in ase_repr.arrays else [None for j in range(len(ase_repr))]
for n, i in enumerate(ase_repr):
struct.atoms.append( model.Atom( number=chemical_symbols.index(i.symbol), x=i.x, y=i.y, z=i.z, charge=charges[n], magmom=magmoms[n] ) )
ormcalc.structures.append(struct)
# TODO Forces
ormcalc.uigrid = model.Grid(info=json.dumps(calc.info))
# tags ORM
uitopics = []
for entity in self.hierarchy:
if not entity['creates_topic']:
continue
if entity['multiple'] or calc._calcset:
for item in calc.info.get( entity['source'], [] ):
uitopics.append( model.topic(cid=entity['cid'], topic=item) )
else:
topic = calc.info.get(entity['source'])
if topic or not entity['optional']:
uitopics.append( model.topic(cid=entity['cid'], topic=topic) )
uitopics = [model.Topic.as_unique(session, cid=x.cid, topic="%s" % x.topic) for x in uitopics]
ormcalc.uitopics.extend(uitopics)
if calc._calcset:
session.add(ormcalc)
else:
session.add_all([codefamily, codeversion, pot, ormcalc])
session.commit()
del calc, ormcalc
return checksum, None