def test_get_primitive_structure(self):
coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]]
fcc_ag = Structure(Lattice.cubic(4.09), ["Ag"] * 4, coords)
self.assertEqual(len(fcc_ag.get_primitive_structure()), 1)
coords = [[0, 0, 0], [0.5, 0.5, 0.5]]
bcc_li = Structure(Lattice.cubic(4.09), ["Li"] * 2, coords)
self.assertEqual(len(bcc_li.get_primitive_structure()), 1)
def test_primitive_cell_site_merging(self):
l = Lattice.cubic(10)
coords = [[0, 0, 0], [0, 0, 0.5], [0, 0, 0.26], [0, 0, 0.74]]
sp = ['Ag', 'Ag', 'Be', 'Be']
s = Structure(l, sp, coords)
dm = s.get_primitive_structure().distance_matrix
self.assertArrayAlmostEqual(dm, [[0, 2.5], [2.5, 0]])
def test_primitive_on_large_supercell(self):
coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]]
fcc_ag = Structure(Lattice.cubic(4.09), ["Ag"] * 4, coords)
fcc_ag.make_supercell([2, 2, 2])
fcc_ag_prim = fcc_ag.get_primitive_structure()
self.assertEqual(len(fcc_ag_prim), 1)
self.assertAlmostEqual(fcc_ag_prim.volume, 17.10448225)
def _sanitize_input_structure(input_structure: Structure) -> Structure:
"""Sanitize our input structure by removing magnetic information
and making primitive.
Args:
input_structure: Structure
Returns: Structure
"""
input_structure = input_structure.copy()
# remove any annotated spin
input_structure.remove_spin()
# sanitize input structure: first make primitive ...
input_structure = input_structure.get_primitive_structure(
use_site_props=False)
# ... and strip out existing magmoms, which can cause conflicts
# with later transformations otherwise since sites would end up
# with both magmom site properties and Species spins defined
if "magmom" in input_structure.site_properties:
input_structure.remove_site_property("magmom")
return input_structure
def test_primitive_on_large_supercell(self):
coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]]
fcc_ag = Structure(Lattice.cubic(4.09), ["Ag"] * 4, coords)
fcc_ag.make_supercell([2, 2, 2])
fcc_ag_prim = fcc_ag.get_primitive_structure()
self.assertEqual(len(fcc_ag_prim), 1)
self.assertAlmostEqual(fcc_ag_prim.volume, 17.10448225)
def test_primitive_cell_site_merging(self):
l = Lattice.cubic(10)
coords = [[0, 0, 0], [0, 0, 0.5],
[0, 0, 0.26], [0, 0, 0.74]]
sp = ['Ag', 'Ag', 'Be', 'Be']
s = Structure(l, sp, coords)
dm = s.get_primitive_structure().distance_matrix
self.assertArrayAlmostEqual(dm, [[0, 2.5], [2.5, 0]])
def test_disordered_supercell_primitive_cell(self):
l = Lattice.cubic(2)
f = [[0.5, 0.5, 0.5]]
sp = [{'Si': 0.54738}]
s = Structure(l, sp, f)
#this supercell often breaks things
s.make_supercell([[0,-1,1],[-1,1,0],[1,1,1]])
self.assertEqual(len(s.get_primitive_structure()), 1)
def test_disordered_supercell_primitive_cell(self):
l = Lattice.cubic(2)
f = [[0.5, 0.5, 0.5]]
sp = [{'Si': 0.54738}]
s = Structure(l, sp, f)
#this supercell often breaks things
s.make_supercell([[0, -1, 1], [-1, 1, 0], [1, 1, 1]])
self.assertEqual(len(s.get_primitive_structure()), 1)
def test_primitive_structure_volume_check(self):
l = Lattice.tetragonal(10, 30)
coords = [[0.5, 0.8, 0], [0.5, 0.2, 0],
[0.5, 0.8, 0.333], [0.5, 0.5, 0.333],
[0.5, 0.5, 0.666], [0.5, 0.2, 0.666]]
s = Structure(l, ["Ag"] * 6, coords)
sprim = s.get_primitive_structure(tolerance=0.1)
self.assertEqual(len(sprim), 6)
def structure_parser_wrapper(structure:Structure,
is_primitive_cell=True,
fractional=True,
remove_unused_parameters=True) -> typing.Dict:
"""
Same as above but expects pymatgen.core.structure.Structure object
"""
if is_primitive_cell:
structure = structure.get_primitive_structure()
if fractional:
position_key = 'fractional'
positions = structure.frac_coords.tolist()
else:
position_key = 'xyz'
positions = structure.cart_coords.tolist()
sg = structure.get_space_group_info()
bravais = space_groups.space_group_to_bravais(sg[1])
lattice_parameters = lattice_parameters_from_cif(structure)
# Required for qCore input
if remove_unused_parameters:
lattice_parameters = remove_superflous_parameters(lattice_parameters, bravais)
species = [element_enum.value for element_enum in structure.species]
assert len(species) == len(positions)
crystal_data = {position_key: positions,
'species': species,
'lattice_parameters': lattice_parameters,
'space_group': sg,
'bravais': bravais,
'n_atoms': len(species)}
return crystal_data
def get_analysis_and_structure(
self,
structure: Structure,
calculate_valences: bool = True,
guesstimate_spin: bool = False,
op_threshold: float = 0.1,
) -> Tuple[Dict, Structure]:
"""Obtain an analysis of a given structure and if it may be Jahn-Teller
active or not. This is a heuristic, and may give false positives and
false negatives (false positives are preferred).
Args:
structure: input structure
calculate_valences: whether to attempt to calculate valences or not, structure
should have oxidation states to perform analysis (Default value = True)
guesstimate_spin: whether to guesstimate spin state from magnetic moments
or not, use with caution (Default value = False)
op_threshold: threshold for order parameter above which to consider site
to match an octahedral or tetrahedral motif, since Jahn-Teller structures
can often be
quite distorted, this threshold is smaller than one might expect
Returns:
analysis of structure, with key 'strength' which may be 'none', 'strong',
'weak', or 'unknown' (Default value = 0.1) and decorated structure
"""
structure = structure.get_primitive_structure()
if calculate_valences:
bva = BVAnalyzer()
structure = bva.get_oxi_state_decorated_structure(structure)
# no point testing multiple equivalent sites, doesn't make any difference to analysis
# but makes returned
symmetrized_structure = SpacegroupAnalyzer(
structure
).get_symmetrized_structure()
# to detect structural motifs of a given site
op = LocalStructOrderParams(["oct", "tet"])
# dict of site index to the Jahn-Teller analysis of that site
jt_sites = []
non_jt_sites = []
for indices in symmetrized_structure.equivalent_indices:
idx = indices[0]
site = symmetrized_structure[idx]
# only interested in sites with oxidation states
if (
isinstance(site.specie, Species)
and site.specie.element.is_transition_metal
):
# get motif around site
order_params = op.get_order_parameters(symmetrized_structure, idx)
if order_params[0] > order_params[1] and order_params[0] > op_threshold:
motif = "oct"
motif_order_parameter = order_params[0]
elif order_params[1] > op_threshold:
motif = "tet"
motif_order_parameter = order_params[1]
else:
motif = "unknown"
motif_order_parameter = None
if motif == "oct" or motif == "tet":
# guess spin of metal ion
if guesstimate_spin and "magmom" in site.properties:
# estimate if high spin or low spin
magmom = site.properties["magmom"]
spin_state = self._estimate_spin_state(
site.specie, motif, magmom
)
else:
spin_state = "unknown"
magnitude = self.get_magnitude_of_effect_from_species(
site.specie, spin_state, motif
)
if magnitude != "none":
ligands = get_neighbors_of_site_with_index(
structure, idx, approach="min_dist", delta=0.15
)
ligand_bond_lengths = [
ligand.distance(structure[idx]) for ligand in ligands
]
ligands_species = list(
set([str(ligand.specie) for ligand in ligands])
)
ligand_bond_length_spread = max(ligand_bond_lengths) - min(
ligand_bond_lengths
)
def trim(f):
"""
Avoid storing to unreasonable precision, hurts readability.
"""
return float("{:.4f}".format(f))
# to be Jahn-Teller active, all ligands have to be the same
if len(ligands_species) == 1:
jt_sites.append(
{
"strength": magnitude,
"motif": motif,
"motif_order_parameter": trim(
motif_order_parameter
),
"spin_state": spin_state,
"species": str(site.specie),
"ligand": ligands_species[0],
"ligand_bond_lengths": [
trim(length) for length in ligand_bond_lengths
],
"ligand_bond_length_spread": trim(
ligand_bond_length_spread
),
"site_indices": indices,
}
)
# store reasons for not being J-T active
else:
non_jt_sites.append(
{
"site_indices": indices,
"strength": "none",
"reason": "Not Jahn-Teller active for this "
"electronic configuration.",
}
)
else:
non_jt_sites.append(
{
"site_indices": indices,
"strength": "none",
"reason": "motif is {}".format(motif),
}
)
# perform aggregation of all sites
if jt_sites:
analysis = {"active": True} # type: Dict[str, Any]
# if any site could exhibit 'strong' Jahn-Teller effect
# then mark whole structure as strong
strong_magnitudes = [site["strength"] == "strong" for site in jt_sites]
if any(strong_magnitudes):
analysis["strength"] = "strong"
else:
analysis["strength"] = "weak"
analysis["sites"] = jt_sites
return analysis, structure
else:
return {"active": False, "sites": non_jt_sites}, structure
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
#common representations for elements/water in cif files
special_symbols = {"D":"D", "Hw":"H", "Ow":"O", "Wat":"O", "wat": "O"}
elements = map(str, ptable.all_elements)
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except KeyError:
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(special_symbols.get(symbol) +
str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol) if \
symbol in special_symbols else symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
if coord not in coord_to_species:
coord_to_species[coord] = {el: occu}
else:
coord_to_species[coord][el] = occu
coord_to_species = {k: Composition(v)
for k, v in coord_to_species.items()}
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(
sorted(list(coord_to_species.items()), key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
#rescale occupancies if necessary
for species in allspecies:
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
for key, value in six.iteritems(species):
species[key] = value / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def __init__(
self,
structure: Structure,
overwrite_magmom_mode: Union[OverwriteMagmomMode, str] = "none",
round_magmoms: bool = False,
detect_valences: bool = False,
make_primitive: bool = True,
default_magmoms: bool = None,
set_net_positive: bool = True,
threshold: float = 0.1,
):
"""
A class which provides a few helpful methods to analyze
collinear magnetic structures.
If magnetic moments are not defined, moments will be
taken either from default_magmoms.yaml (similar to the
default magmoms in MPRelaxSet, with a few extra definitions)
or from a specie:magmom dict provided by the default_magmoms
kwarg.
Input magmoms can be replaced using the 'overwrite_magmom_mode'
kwarg. This can be:
* "none" to do nothing,
* "respect_sign" which will overwrite existing magmoms with
those from default_magmoms but will keep sites with positive magmoms
positive, negative magmoms negative and zero magmoms zero,
* "respect_zeros", which will give a ferromagnetic structure
(all positive magmoms from default_magmoms) but still keep sites with
zero magmoms as zero,
* "replace_all" which will try to guess initial magmoms for
all sites in the structure irrespective of input structure
(this is most suitable for an initial DFT calculation),
* "replace_all_if_undefined" is the same as "replace_all" but only if
no magmoms are defined in input structure, otherwise it will respect
existing magmoms.
* "normalize" will normalize magmoms to unity, but will respect sign
(used for comparing orderings), magmoms < theshold will be set to zero
:param structure: Structure object
:param overwrite_magmom_mode (str): default "none"
:param round_magmoms (int or bool): will round input magmoms to
specified number of decimal places if integer is supplied, if set
to a float will try and group magmoms together using a kernel density
estimator of provided width, and extracting peaks of the estimator
:param detect_valences (bool): if True, will attempt to assign valences
to input structure
:param make_primitive (bool): if True, will transform to primitive
magnetic cell
:param default_magmoms (dict): (optional) dict specifying default magmoms
:param set_net_positive (bool): if True, will change sign of magnetic
moments such that the net magnetization is positive. Argument will be
ignored if mode "respect_sign" is used.
:param threshold (float): number (in Bohr magnetons) below which magmoms
will be rounded to zero, default of 0.1 can probably be increased for many
magnetic systems, depending on your application
"""
if default_magmoms:
self.default_magmoms = default_magmoms
else:
self.default_magmoms = DEFAULT_MAGMOMS
structure = structure.copy()
# check for disorder
if not structure.is_ordered:
raise NotImplementedError(
"Not implemented for disordered structures, "
"make ordered approximation first.")
if detect_valences:
trans = AutoOxiStateDecorationTransformation()
bva = BVAnalyzer()
try:
structure = trans.apply_transformation(structure)
except ValueError:
warnings.warn("Could not assign valences "
"for {}".format(
structure.composition.reduced_formula))
# check to see if structure has magnetic moments
# on site properties or species spin properties,
# prioritize site properties
has_magmoms = bool(structure.site_properties.get("magmom", False))
has_spin = False
for comp in structure.species_and_occu:
for sp, occu in comp.items():
if getattr(sp, "spin", False):
has_spin = True
# perform input sanitation ...
# rest of class will assume magnetic moments
# are stored on site properties:
# this is somewhat arbitrary, arguments can
# be made for both approaches
if has_magmoms and has_spin:
raise ValueError("Structure contains magnetic moments on both "
"magmom site properties and spin species "
"properties. This is ambiguous. Remove one or "
"the other.")
elif has_magmoms:
if None in structure.site_properties["magmom"]:
warnings.warn("Be careful with mixing types in your magmom "
"site properties. Any 'None' magmoms have been "
"replaced with zero.")
magmoms = [
m if m else 0 for m in structure.site_properties["magmom"]
]
elif has_spin:
magmoms = [getattr(sp, "spin", 0) for sp in structure.species]
structure.remove_spin()
else:
# no magmoms present, add zero magmoms for now
magmoms = [0] * len(structure)
# and overwrite magmoms with default magmoms later unless otherwise stated
if overwrite_magmom_mode == "replace_all_if_undefined":
overwrite_magmom_mode = "replace_all"
# test to see if input structure has collinear magmoms
self.is_collinear = Magmom.are_collinear(magmoms)
if not self.is_collinear:
warnings.warn(
"This class is not designed to be used with "
"non-collinear structures. If your structure is "
"only slightly non-collinear (e.g. canted) may still "
"give useful results, but use with caution.")
# this is for collinear structures only, make sure magmoms
# are all floats
magmoms = list(map(float, magmoms))
# set properties that should be done /before/ we process input magmoms
self.total_magmoms = sum(magmoms)
self.magnetization = sum(magmoms) / structure.volume
# round magmoms below threshold to zero
magmoms = [m if abs(m) > threshold else 0 for m in magmoms]
# overwrite existing magmoms with default_magmoms
if overwrite_magmom_mode not in (
"none",
"respect_sign",
"respect_zeros",
"replace_all",
"replace_all_if_undefined",
"normalize",
):
raise ValueError("Unsupported mode.")
for idx, site in enumerate(structure):
if site.species_string in self.default_magmoms:
# look for species first, e.g. Fe2+
default_magmom = self.default_magmoms[site.species_string]
elif (isinstance(site.specie, Specie)
and str(site.specie.element) in self.default_magmoms):
# look for element, e.g. Fe
default_magmom = self.default_magmoms[str(site.specie.element)]
else:
default_magmom = 0
# overwrite_magmom_mode = "respect_sign" will change magnitude of
# existing moments only, and keep zero magmoms as
# zero: it will keep the magnetic ordering intact
if overwrite_magmom_mode == "respect_sign":
set_net_positive = False
if magmoms[idx] > 0:
magmoms[idx] = default_magmom
elif magmoms[idx] < 0:
magmoms[idx] = -default_magmom
# overwrite_magmom_mode = "respect_zeros" will give a ferromagnetic
# structure but will keep zero magmoms as zero
elif overwrite_magmom_mode == "respect_zeros":
if magmoms[idx] != 0:
magmoms[idx] = default_magmom
# overwrite_magmom_mode = "replace_all" will ignore input magmoms
# and give a ferromagnetic structure with magnetic
# moments on *all* atoms it thinks could be magnetic
elif overwrite_magmom_mode == "replace_all":
magmoms[idx] = default_magmom
# overwrite_magmom_mode = "normalize" set magmoms magnitude to 1
elif overwrite_magmom_mode == "normalize":
if magmoms[idx] != 0:
magmoms[idx] = int(magmoms[idx] / abs(magmoms[idx]))
# round magmoms, used to smooth out computational data
magmoms = (self._round_magmoms(magmoms, round_magmoms)
if round_magmoms else magmoms)
if set_net_positive:
sign = np.sum(magmoms)
if sign < 0:
magmoms = -np.array(magmoms)
structure.add_site_property("magmom", magmoms)
if make_primitive:
structure = structure.get_primitive_structure(use_site_props=True)
self.structure = structure
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
def get_num_implicit_hydrogens(sym):
num_h = {"Wat": 2, "wat": 2, "O-H": 1}
return num_h.get(sym[:3], 0)
lattice = self.get_lattice(data)
# if magCIF, get magnetic symmetry moments and magmoms
# else standard CIF, and use empty magmom dict
if self.feature_flags["magcif_incommensurate"]:
raise NotImplementedError(
"Incommensurate structures not currently supported.")
elif self.feature_flags["magcif"]:
self.symmetry_operations = self.get_magsymops(data)
magmoms = self.parse_magmoms(data, lattice=lattice)
else:
self.symmetry_operations = self.get_symops(data)
magmoms = {}
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
coord_to_magmoms = OrderedDict()
def get_matching_coord(coord):
keys = list(coord_to_species.keys())
coords = np.array(keys)
for op in self.symmetry_operations:
c = op.operate(coord)
inds = find_in_coord_list_pbc(coords,
c,
atol=self._site_tolerance)
# cant use if inds, because python is dumb and np.array([0]) evaluates
# to False
if len(inds):
return keys[inds[0]]
return False
label_el_dict = {}
for i in range(len(data["_atom_site_label"])):
try:
# If site type symbol exists, use it. Otherwise, we use the
# label.
symbol = self._parse_symbol(data["_atom_site_type_symbol"][i])
label = data["_atom_site_label"][i]
num_h = get_num_implicit_hydrogens(
data["_atom_site_type_symbol"][i])
except KeyError:
symbol = self._parse_symbol(data["_atom_site_label"][i])
label = data["_atom_site_label"][i]
num_h = get_num_implicit_hydrogens(data["_atom_site_label"][i])
if not symbol:
continue
if oxi_states is not None:
o_s = oxi_states.get(symbol, 0)
# use _atom_site_type_symbol if possible for oxidation state
if "_atom_site_type_symbol" in data.data.keys():
oxi_symbol = data["_atom_site_type_symbol"][i]
o_s = oxi_states.get(oxi_symbol, o_s)
try:
el = Specie(symbol, o_s)
except:
el = DummySpecie(symbol, o_s)
else:
el = get_el_sp(symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
magmom = magmoms.get(data["_atom_site_label"][i],
np.array([0, 0, 0]))
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
comp_d = {el: occu}
if num_h > 0:
comp_d["H"] = num_h
comp = Composition(comp_d)
if not match:
coord_to_species[coord] = comp
coord_to_magmoms[coord] = magmom
else:
coord_to_species[match] += comp
# disordered magnetic not currently supported
coord_to_magmoms[match] = None
label_el_dict[coord] = label
sum_occu = [
sum(c.values()) for c in coord_to_species.values() if
not set(c.elements) == {Element("O"), Element("H")}
]
if any([o > 1 for o in sum_occu]):
msg = "Some occupancies (%s) sum to > 1! If they are within " \
"the tolerance, they will be rescaled." % str(sum_occu)
warnings.warn(msg)
self.errors.append(msg)
allspecies = []
allcoords = []
allmagmoms = []
allhydrogens = []
alllabels = []
# check to see if magCIF file is disordered
if self.feature_flags["magcif"]:
for k, v in coord_to_magmoms.items():
if v is None:
# Proposed solution to this is to instead store magnetic
# moments as Specie 'spin' property, instead of site
# property, but this introduces ambiguities for end user
# (such as unintended use of `spin` and Specie will have
# fictious oxidation state).
raise NotImplementedError(
'Disordered magnetic structures not currently supported.'
)
if coord_to_species.items():
for comp, group in groupby(sorted(list(coord_to_species.items()),
key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
#print(tmp_coords)
labels = []
for i in tmp_coords:
labels.append(label_el_dict[i])
#print(labels)
tmp_magmom = [
coord_to_magmoms[tmp_coord] for tmp_coord in tmp_coords
]
if self.feature_flags["magcif"]:
coords, magmoms, coords_num = self._unique_coords(
tmp_coords, magmoms_in=tmp_magmom, lattice=lattice)
else:
coords, magmoms, coords_num = self._unique_coords(
tmp_coords)
if set(comp.elements) == {Element("O"), Element("H")}:
# O with implicit hydrogens
im_h = comp["H"]
species = Composition({"O": comp["O"]})
else:
im_h = 0
species = comp
allhydrogens.extend(len(coords) * [im_h])
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
allmagmoms.extend(magmoms)
for i in range(len(coords_num)):
alllabels.extend(coords_num[i] * [labels[i]])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords) \
and len(allspecies) == len(allmagmoms):
site_properties = dict()
if any(allhydrogens):
assert len(allhydrogens) == len(allcoords)
site_properties["implicit_hydrogens"] = allhydrogens
if self.feature_flags["magcif"]:
site_properties["magmom"] = allmagmoms
if len(site_properties) == 0:
site_properties = None
struct = Structure(lattice,
allspecies,
allcoords,
site_properties=site_properties)
#struct = struct.get_sorted_structure()
if primitive and self.feature_flags['magcif']:
struct = struct.get_primitive_structure(use_site_props=True)
elif primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
struct.add_site_property("_atom_site_label", alllabels)
return struct
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
# common representations for elements/water in cif files
special_symbols = {
"D": "D",
"Hw": "H",
"Ow": "O",
"Wat": "O",
"wat": "O"
}
elements = [el.symbol for el in Element]
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0),
atol=self._site_tolerance):
return tuple(k)
return False
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except (KeyError, ValueError):
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(
special_symbols.get(symbol) + str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol, symbol))
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
else:
coord_to_species[match] += {el: occu}
if any([sum(c.values()) > 1 for c in coord_to_species.values()]):
warnings.warn("Some occupancies sum to > 1! If they are within "
"the tolerance, they will be rescaled.")
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(sorted(list(
coord_to_species.items()),
key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
lengths = [float_from_str(data["_cell_length_" + i])
for i in ["a", "b", "c"]]
angles = [float_from_str(data["_cell_angle_" + i])
for i in ["alpha", "beta", "gamma"]]
lattice = Lattice.from_lengths_and_angles(lengths, angles)
try:
sympos = data["_symmetry_equiv_pos_as_xyz"]
except KeyError:
try:
sympos = data["_symmetry_equiv_pos_as_xyz_"]
except KeyError:
warnings.warn("No _symmetry_equiv_pos_as_xyz type key found. "
"Defaulting to P1.")
sympos = ['x, y, z']
self.symmetry_operations = parse_symmetry_operations(sympos)
def parse_symbol(sym):
m = re.search("([A-Z][a-z]*)", sym)
if m:
return m.group(1)
return ""
try:
oxi_states = {data["_atom_type_symbol"][i]:
float_from_str(data["_atom_type_oxidation_number"][i])
for i in xrange(len(data["_atom_type_symbol"]))}
except (ValueError, KeyError):
oxi_states = None
coord_to_species = OrderedDict()
for i in xrange(len(data["_atom_site_type_symbol"])):
symbol = parse_symbol(data["_atom_site_type_symbol"][i])
if oxi_states is not None:
el = Specie(symbol,
oxi_states[data["_atom_site_type_symbol"][i]])
else:
el = Element(symbol)
x = float_from_str(data["_atom_site_fract_x"][i])
y = float_from_str(data["_atom_site_fract_y"][i])
z = float_from_str(data["_atom_site_fract_z"][i])
try:
occu = float_from_str(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
if coord not in coord_to_species:
coord_to_species[coord] = {el: occu}
else:
coord_to_species[coord][el] = occu
allspecies = []
allcoords = []
for coord, species in coord_to_species.items():
coords = self._unique_coords(coord)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
#rescale occupancies if necessary
for species in allspecies:
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
for key, value in species.iteritems():
species[key] = value / totaloccu
struct = Structure(lattice, allspecies, allcoords)
if primitive:
struct = struct.get_primitive_structure()
return struct.get_sorted_structure()
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
#common representations for elements/water in cif files
special_symbols = {
"D": "D",
"Hw": "H",
"Ow": "O",
"Wat": "O",
"wat": "O"
}
elements = [el.symbol for el in Element]
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except (KeyError, ValueError):
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(
special_symbols.get(symbol) + str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol) if \
symbol in special_symbols else symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
if coord not in coord_to_species:
coord_to_species[coord] = {el: occu}
else:
coord_to_species[coord][el] = occu
coord_to_species = {
k: Composition(v)
for k, v in coord_to_species.items()
}
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(sorted(list(
coord_to_species.items()),
key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
#rescale occupancies if necessary
for species in allspecies:
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
for key, value in six.iteritems(species):
species[key] = value / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def get_slab(self, shift=0, tol=0.1, energy=None):
"""
This method takes in shift value for the c lattice direction and
generates a slab based on the given shift. You should rarely use this
method. Instead, it is used by other generation algorithms to obtain
all slabs.
Arg:
shift (float): A shift value in Angstrom that determines how much a
slab should be shifted.
tol (float): Tolerance to determine primitive cell.
energy (float): An energy to assign to the slab.
Returns:
(Slab) A Slab object with a particular shifted oriented unit cell.
"""
h = self._proj_height
nlayers_slab = int(math.ceil(self.min_slab_size / h))
nlayers_vac = int(math.ceil(self.min_vac_size / h))
nlayers = nlayers_slab + nlayers_vac
species = self.oriented_unit_cell.species_and_occu
props = self.oriented_unit_cell.site_properties
props = {k: v * nlayers_slab for k, v in props.items()}
frac_coords = self.oriented_unit_cell.frac_coords
frac_coords = np.array(frac_coords) +\
np.array([0, 0, -shift])[None, :]
frac_coords = frac_coords - np.floor(frac_coords)
a, b, c = self.oriented_unit_cell.lattice.matrix
new_lattice = [a, b, nlayers * c]
frac_coords[:, 2] = frac_coords[:, 2] / nlayers
all_coords = []
for i in range(nlayers_slab):
fcoords = frac_coords.copy()
fcoords[:, 2] += i / nlayers
all_coords.extend(fcoords)
slab = Structure(new_lattice, species * nlayers_slab, all_coords,
site_properties=props)
scale_factor = self.slab_scale_factor
# Whether or not to orthogonalize the structure
if self.lll_reduce:
lll_slab = slab.copy(sanitize=True)
mapping = lll_slab.lattice.find_mapping(slab.lattice)
scale_factor = np.dot(mapping[2], scale_factor)
slab = lll_slab
# Whether or not to center the slab layer around the vacuum
if self.center_slab:
avg_c = np.average([c[2] for c in slab.frac_coords])
slab.translate_sites(list(range(len(slab))), [0, 0, 0.5 - avg_c])
if self.primitive:
prim = slab.get_primitive_structure(tolerance=tol)
if energy is not None:
energy = prim.volume / slab.volume * energy
slab = prim
return Slab(slab.lattice, slab.species_and_occu,
slab.frac_coords, self.miller_index,
self.oriented_unit_cell, shift,
scale_factor, site_properties=slab.site_properties,
energy=energy)
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
def parse_symbol(sym):
# Common representations for elements/water in cif files
# TODO: fix inconsistent handling of water
special = {"D": "D", "Hw": "H", "Ow": "O", "Wat": "O",
"wat": "O", "OH": "", "OH2": ""}
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
if sym in special:
v = special[sym]
else:
v = special.get(m[0], m[0])
if len(m) > 1 or (m[0] in special):
warnings.warn("{} parsed as {}".format(sym, v))
return v
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def get_matching_coord(coord):
keys = list(coord_to_species.keys())
coords = np.array(keys)
for op in self.symmetry_operations:
c = op.operate(coord)
inds = find_in_coord_list_pbc(coords, c, atol=self._site_tolerance)
# cant use if inds, because python is dumb and np.array([0]) evaluates
# to False
if len(inds):
return keys[inds[0]]
return False
############################################################
"""
This part of the code deals with handling formats of data as found in
CIF files extracted from the Springer Materials/Pauling File
databases, and that are different from standard ICSD formats.
"""
# Check to see if "_atom_site_type_symbol" exists, as some test CIFs do
# not contain this key.
if "_atom_site_type_symbol" in data.data.keys():
# Keep a track of which data row needs to be removed.
# Example of a row: Nb,Zr '0.8Nb + 0.2Zr' .2a .m-3m 0 0 0 1 14
# 'rhombic dodecahedron, Nb<sub>14</sub>'
# Without this code, the above row in a structure would be parsed
# as an ordered site with only Nb (since
# CifParser would try to parse the first two characters of the
# label "Nb,Zr") and occupancy=1.
# However, this site is meant to be a disordered site with 0.8 of
# Nb and 0.2 of Zr.
idxs_to_remove = []
for idx, el_row in enumerate(data["_atom_site_label"]):
# CIF files from the Springer Materials/Pauling File have
# switched the label and symbol. Thus, in the
# above shown example row, '0.8Nb + 0.2Zr' is the symbol.
# Below, we split the strings on ' + ' to
# check if the length (or number of elements) in the label and
# symbol are equal.
if len(data["_atom_site_type_symbol"][idx].split(' + ')) > \
len(data["_atom_site_label"][idx].split(' + ')):
# Dictionary to hold extracted elements and occupancies
els_occu = {}
# parse symbol to get element names and occupancy and store
# in "els_occu"
symbol_str = data["_atom_site_type_symbol"][idx]
symbol_str_lst = symbol_str.split(' + ')
for elocc_idx in range(len(symbol_str_lst)):
# Remove any bracketed items in the string
symbol_str_lst[elocc_idx] = re.sub(r'\([0-9]*\)', '',
symbol_str_lst[elocc_idx].strip())
# Extract element name and its occupancy from the
# string, and store it as a
# key-value pair in "els_occ".
els_occu[str(re.findall(r'\D+', symbol_str_lst[
elocc_idx].strip())[1]).replace('<sup>', '')] = \
float('0' + re.findall(r'\.?\d+', symbol_str_lst[
elocc_idx].strip())[1])
x = str2float(data["_atom_site_fract_x"][idx])
y = str2float(data["_atom_site_fract_y"][idx])
z = str2float(data["_atom_site_fract_z"][idx])
coord = (x, y, z)
# Add each partially occupied element on the site coordinate
for et in els_occu:
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition(
{parse_symbol(et): els_occu[parse_symbol(et)]})
else:
coord_to_species[match] += {
parse_symbol(et): els_occu[parse_symbol(et)]}
idxs_to_remove.append(idx)
# Remove the original row by iterating over all keys in the CIF
# data looking for lists, which indicates
# multiple data items, one for each row, and remove items from the
# list that corresponds to the removed row,
# so that it's not processed by the rest of this function (which
# would result in an error).
for cif_key in data.data:
if type(data.data[cif_key]) == list:
for id in sorted(idxs_to_remove, reverse=True):
del data.data[cif_key][id]
############################################################
for i in range(len(data["_atom_site_label"])):
try:
# If site type symbol exists, use it. Otherwise, we use the
# label.
symbol = parse_symbol(data["_atom_site_type_symbol"][i])
except KeyError:
symbol = parse_symbol(data["_atom_site_label"][i])
if not symbol:
continue
if oxi_states is not None:
o_s = oxi_states.get(symbol, 0)
# use _atom_site_type_symbol if possible for oxidation state
if "_atom_site_type_symbol" in data.data.keys():
oxi_symbol = data["_atom_site_type_symbol"][i]
o_s = oxi_states.get(oxi_symbol, o_s)
try:
el = Specie(symbol, o_s)
except:
el = DummySpecie(symbol, o_s)
else:
el = get_el_sp(symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
else:
coord_to_species[match] += {el: occu}
sum_occu = [sum(c.values()) for c in coord_to_species.values()]
if any([o > 1 for o in sum_occu]):
warnings.warn("Some occupancies (%s) sum to > 1! If they are within "
"the tolerance, they will be rescaled." % str(sum_occu))
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(
sorted(list(coord_to_species.items()), key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
lengths = [
str2float(data["_cell_length_" + i]) for i in ["a", "b", "c"]
]
angles = [
str2float(data["_cell_angle_" + i])
for i in ["alpha", "beta", "gamma"]
]
lattice = Lattice.from_lengths_and_angles(lengths, angles)
try:
sympos = data["_symmetry_equiv_pos_as_xyz"]
except KeyError:
try:
sympos = data["_symmetry_equiv_pos_as_xyz_"]
except KeyError:
warnings.warn("No _symmetry_equiv_pos_as_xyz type key found. "
"Defaulting to P1.")
sympos = ['x, y, z']
self.symmetry_operations = parse_symmetry_operations(sympos)
def parse_symbol(sym):
m = re.search("([A-Z][a-z]*)", sym)
if m:
return m.group(1)
return ""
try:
oxi_states = {
data["_atom_type_symbol"][i]:
str2float(data["_atom_type_oxidation_number"][i])
for i in xrange(len(data["_atom_type_symbol"]))
}
except (ValueError, KeyError):
oxi_states = None
coord_to_species = OrderedDict()
for i in xrange(len(data["_atom_site_type_symbol"])):
symbol = parse_symbol(data["_atom_site_type_symbol"][i])
if oxi_states is not None:
el = Specie(symbol,
oxi_states[data["_atom_site_type_symbol"][i]])
else:
el = Element(symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
if coord not in coord_to_species:
coord_to_species[coord] = {el: occu}
else:
coord_to_species[coord][el] = occu
allspecies = []
allcoords = []
for coord, species in coord_to_species.items():
coords = self._unique_coords(coord)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
#rescale occupancies if necessary
for species in allspecies:
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
for key, value in species.iteritems():
species[key] = value / totaloccu
struct = Structure(lattice, allspecies, allcoords)
if primitive:
struct = struct.get_primitive_structure().get_reduced_structure()
return struct.get_sorted_structure()
def __init__(
self,
structure: Structure,
overwrite_magmom_mode: Union[OverwriteMagmomMode, str] = "none",
round_magmoms: bool = False,
detect_valences: bool = False,
make_primitive: bool = True,
default_magmoms: bool = None,
set_net_positive: bool = True,
threshold: float = 0.1,
):
"""
A class which provides a few helpful methods to analyze
collinear magnetic structures.
If magnetic moments are not defined, moments will be
taken either from default_magmoms.yaml (similar to the
default magmoms in MPRelaxSet, with a few extra definitions)
or from a specie:magmom dict provided by the default_magmoms
kwarg.
Input magmoms can be replaced using the 'overwrite_magmom_mode'
kwarg. This can be:
* "none" to do nothing,
* "respect_sign" which will overwrite existing magmoms with
those from default_magmoms but will keep sites with positive magmoms
positive, negative magmoms negative and zero magmoms zero,
* "respect_zeros", which will give a ferromagnetic structure
(all positive magmoms from default_magmoms) but still keep sites with
zero magmoms as zero,
* "replace_all" which will try to guess initial magmoms for
all sites in the structure irrespective of input structure
(this is most suitable for an initial DFT calculation),
* "replace_all_if_undefined" is the same as "replace_all" but only if
no magmoms are defined in input structure, otherwise it will respect
existing magmoms.
* "normalize" will normalize magmoms to unity, but will respect sign
(used for comparing orderings), magmoms < theshold will be set to zero
:param structure: Structure object
:param overwrite_magmom_mode (str): default "none"
:param round_magmoms (int or bool): will round input magmoms to
specified number of decimal places if integer is supplied, if set
to a float will try and group magmoms together using a kernel density
estimator of provided width, and extracting peaks of the estimator
:param detect_valences (bool): if True, will attempt to assign valences
to input structure
:param make_primitive (bool): if True, will transform to primitive
magnetic cell
:param default_magmoms (dict): (optional) dict specifying default magmoms
:param set_net_positive (bool): if True, will change sign of magnetic
moments such that the net magnetization is positive. Argument will be
ignored if mode "respect_sign" is used.
:param threshold (float): number (in Bohr magnetons) below which magmoms
will be rounded to zero, default of 0.1 can probably be increased for many
magnetic systems, depending on your application
"""
if default_magmoms:
self.default_magmoms = default_magmoms
else:
self.default_magmoms = DEFAULT_MAGMOMS
structure = structure.copy()
# check for disorder
if not structure.is_ordered:
raise NotImplementedError(
"Not implemented for disordered structures, "
"make ordered approximation first."
)
if detect_valences:
trans = AutoOxiStateDecorationTransformation()
bva = BVAnalyzer()
try:
structure = trans.apply_transformation(structure)
except ValueError:
warnings.warn(
"Could not assign valences "
"for {}".format(structure.composition.reduced_formula)
)
# check to see if structure has magnetic moments
# on site properties or species spin properties,
# prioritize site properties
has_magmoms = bool(structure.site_properties.get("magmom", False))
has_spin = False
for comp in structure.species_and_occu:
for sp, occu in comp.items():
if getattr(sp, "spin", False):
has_spin = True
# perform input sanitation ...
# rest of class will assume magnetic moments
# are stored on site properties:
# this is somewhat arbitrary, arguments can
# be made for both approaches
if has_magmoms and has_spin:
raise ValueError(
"Structure contains magnetic moments on both "
"magmom site properties and spin species "
"properties. This is ambiguous. Remove one or "
"the other."
)
elif has_magmoms:
if None in structure.site_properties["magmom"]:
warnings.warn(
"Be careful with mixing types in your magmom "
"site properties. Any 'None' magmoms have been "
"replaced with zero."
)
magmoms = [m if m else 0 for m in structure.site_properties["magmom"]]
elif has_spin:
magmoms = [getattr(sp, "spin", 0) for sp in structure.species]
structure.remove_spin()
else:
# no magmoms present, add zero magmoms for now
magmoms = [0] * len(structure)
# and overwrite magmoms with default magmoms later unless otherwise stated
if overwrite_magmom_mode == "replace_all_if_undefined":
overwrite_magmom_mode = "replace_all"
# test to see if input structure has collinear magmoms
self.is_collinear = Magmom.are_collinear(magmoms)
if not self.is_collinear:
warnings.warn(
"This class is not designed to be used with "
"non-collinear structures. If your structure is "
"only slightly non-collinear (e.g. canted) may still "
"give useful results, but use with caution."
)
# this is for collinear structures only, make sure magmoms
# are all floats
magmoms = list(map(float, magmoms))
# set properties that should be done /before/ we process input magmoms
self.total_magmoms = sum(magmoms)
self.magnetization = sum(magmoms) / structure.volume
# round magmoms below threshold to zero
magmoms = [m if abs(m) > threshold else 0 for m in magmoms]
# overwrite existing magmoms with default_magmoms
if overwrite_magmom_mode not in (
"none",
"respect_sign",
"respect_zeros",
"replace_all",
"replace_all_if_undefined",
"normalize",
):
raise ValueError("Unsupported mode.")
for idx, site in enumerate(structure):
if site.species_string in self.default_magmoms:
# look for species first, e.g. Fe2+
default_magmom = self.default_magmoms[site.species_string]
elif (
isinstance(site.specie, Specie)
and str(site.specie.element) in self.default_magmoms
):
# look for element, e.g. Fe
default_magmom = self.default_magmoms[str(site.specie.element)]
else:
default_magmom = 0
# overwrite_magmom_mode = "respect_sign" will change magnitude of
# existing moments only, and keep zero magmoms as
# zero: it will keep the magnetic ordering intact
if overwrite_magmom_mode == "respect_sign":
set_net_positive = False
if magmoms[idx] > 0:
magmoms[idx] = default_magmom
elif magmoms[idx] < 0:
magmoms[idx] = -default_magmom
# overwrite_magmom_mode = "respect_zeros" will give a ferromagnetic
# structure but will keep zero magmoms as zero
elif overwrite_magmom_mode == "respect_zeros":
if magmoms[idx] != 0:
magmoms[idx] = default_magmom
# overwrite_magmom_mode = "replace_all" will ignore input magmoms
# and give a ferromagnetic structure with magnetic
# moments on *all* atoms it thinks could be magnetic
elif overwrite_magmom_mode == "replace_all":
magmoms[idx] = default_magmom
# overwrite_magmom_mode = "normalize" set magmoms magnitude to 1
elif overwrite_magmom_mode == "normalize":
if magmoms[idx] != 0:
magmoms[idx] = int(magmoms[idx] / abs(magmoms[idx]))
# round magmoms, used to smooth out computational data
magmoms = (
self._round_magmoms(magmoms, round_magmoms) if round_magmoms else magmoms
)
if set_net_positive:
sign = np.sum(magmoms)
if sign < 0:
magmoms = -np.array(magmoms)
structure.add_site_property("magmom", magmoms)
if make_primitive:
structure = structure.get_primitive_structure(use_site_props=True)
self.structure = structure
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
lengths = [
str2float(data["_cell_length_" + i]) for i in ["a", "b", "c"]
]
angles = [
str2float(data["_cell_angle_" + i])
for i in ["alpha", "beta", "gamma"]
]
lattice = Lattice.from_lengths_and_angles(lengths, angles)
try:
sympos = data["_symmetry_equiv_pos_as_xyz"]
except KeyError:
try:
sympos = data["_symmetry_equiv_pos_as_xyz_"]
except KeyError:
warnings.warn("No _symmetry_equiv_pos_as_xyz type key found. "
"Defaulting to P1.")
sympos = ['x, y, z']
self.symmetry_operations = [SymmOp.from_xyz_string(s) for s in sympos]
def parse_symbol(sym):
# capitalization conventions are not strictly followed, eg Cu will be CU
m = re.search("([A-Za-z]*)", sym)
if m:
return m.group(1)[:2].capitalize()
return ""
try:
oxi_states = {
data["_atom_type_symbol"][i]:
str2float(data["_atom_type_oxidation_number"][i])
for i in range(len(data["_atom_type_symbol"]))
}
except (ValueError, KeyError):
oxi_states = None
coord_to_species = OrderedDict()
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
Element(symbol)
except KeyError:
symbol = parse_symbol(data["_atom_site_type_symbol"][i])
if oxi_states is not None:
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
k = data["_atom_site_type_symbol"][i]
else:
k = symbol
el = Specie(symbol, oxi_states[k])
else:
el = Element(symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
if coord not in coord_to_species:
coord_to_species[coord] = {el: occu}
else:
coord_to_species[coord][el] = occu
allspecies = []
allcoords = []
for coord, species in coord_to_species.items():
coords = self._unique_coords(coord)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
#rescale occupancies if necessary
for species in allspecies:
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
for key, value in six.iteritems(species):
species[key] = value / totaloccu
struct = Structure(lattice, allspecies, allcoords)
if primitive:
struct = struct.get_primitive_structure().get_reduced_structure()
return struct.get_sorted_structure()
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
# common representations for elements/water in cif files
special_symbols = {
"D": "D",
"Hw": "H",
"Ow": "O",
"Wat": "O",
"wat": "O"
}
elements = [el.symbol for el in Element]
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
elif sym in ['OH', 'OH2']:
warnings.warn("Symbol '{}' not recognized".format(sym))
return ""
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0),
atol=self._site_tolerance):
return tuple(k)
return False
############################################################
"""
This part of the code deals with handling formats of data as found in CIF files extracted from the
Springer Materials/Pauling File databases, and that are different from standard ICSD formats.
"""
# Check to see if "_atom_site_type_symbol" exists, as some test CIFs do not contain this key.
if "_atom_site_type_symbol" in data.data.keys():
# Keep a track of which data row needs to be removed.
# Example of a row: Nb,Zr '0.8Nb + 0.2Zr' .2a .m-3m 0 0 0 1 14 'rhombic dodecahedron, Nb<sub>14</sub>'
# Without this code, the above row in a structure would be parsed as an ordered site with only Nb (since
# CifParser would try to parse the first two characters of the label "Nb,Zr") and occupancy=1.
# However, this site is meant to be a disordered site with 0.8 of Nb and 0.2 of Zr.
idxs_to_remove = []
for idx, el_row in enumerate(data["_atom_site_label"]):
# CIF files from the Springer Materials/Pauling File have switched the label and symbol. Thus, in the
# above shown example row, '0.8Nb + 0.2Zr' is the symbol. Below, we split the strings on ' + ' to
# check if the length (or number of elements) in the label and symbol are equal.
if len(data["_atom_site_type_symbol"][idx].split(' + ')) > \
len(data["_atom_site_label"][idx].split(' + ')):
# Dictionary to hold extracted elements and occupancies
els_occu = {}
# parse symbol to get element names and occupancy and store in "els_occu"
symbol_str = data["_atom_site_type_symbol"][idx]
symbol_str_lst = symbol_str.split(' + ')
for elocc_idx in range(len(symbol_str_lst)):
# Remove any bracketed items in the string
symbol_str_lst[elocc_idx] = re.sub(
'\([0-9]*\)', '',
symbol_str_lst[elocc_idx].strip())
# Extract element name and its occupancy from the string, and store it as a
# key-value pair in "els_occ".
els_occu[str(re.findall('\D+', symbol_str_lst[elocc_idx].strip())[1]).replace('<sup>', '')] = \
float('0' + re.findall('\.?\d+', symbol_str_lst[elocc_idx].strip())[1])
x = str2float(data["_atom_site_fract_x"][idx])
y = str2float(data["_atom_site_fract_y"][idx])
z = str2float(data["_atom_site_fract_z"][idx])
coord = (x, y, z)
# Add each partially occupied element on the site coordinate
for et in els_occu:
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition(
{parse_symbol(et): els_occu[parse_symbol(et)]})
else:
coord_to_species[match] += {
parse_symbol(et): els_occu[parse_symbol(et)]
}
idxs_to_remove.append(idx)
# Remove the original row by iterating over all keys in the CIF data looking for lists, which indicates
# multiple data items, one for each row, and remove items from the list that corresponds to the removed row,
# so that it's not processed by the rest of this function (which would result in an error).
for cif_key in data.data:
if type(data.data[cif_key]) == list:
for id in sorted(idxs_to_remove, reverse=True):
del data.data[cif_key][id]
############################################################
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except (KeyError, ValueError):
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(
special_symbols.get(symbol) + str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol, symbol))
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
else:
coord_to_species[match] += {el: occu}
if any([sum(c.values()) > 1 for c in coord_to_species.values()]):
warnings.warn("Some occupancies sum to > 1! If they are within "
"the tolerance, they will be rescaled.")
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(sorted(list(
coord_to_species.items()),
key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
# common representations for elements/water in cif files
special_symbols = {"D": "D", "Hw": "H", "Ow": "O", "Wat": "O",
"wat": "O"}
elements = [el.symbol for el in Element]
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
elif sym in ['OH', 'OH2']:
warnings.warn("Symbol '{}' not recognized".format(sym))
return ""
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0),
atol=self._site_tolerance):
return tuple(k)
return False
############################################################
"""
This part of the code deals with handling formats of data as found in CIF files extracted from the
Springer Materials/Pauling File databases, and that are different from standard ICSD formats.
"""
# Check to see if "_atom_site_type_symbol" exists, as some test CIFs do not contain this key.
if "_atom_site_type_symbol" in data.data.keys():
# Keep a track of which data row needs to be removed.
# Example of a row: Nb,Zr '0.8Nb + 0.2Zr' .2a .m-3m 0 0 0 1 14 'rhombic dodecahedron, Nb<sub>14</sub>'
# Without this code, the above row in a structure would be parsed as an ordered site with only Nb (since
# CifParser would try to parse the first two characters of the label "Nb,Zr") and occupancy=1.
# However, this site is meant to be a disordered site with 0.8 of Nb and 0.2 of Zr.
idxs_to_remove = []
for idx, el_row in enumerate(data["_atom_site_label"]):
# CIF files from the Springer Materials/Pauling File have switched the label and symbol. Thus, in the
# above shown example row, '0.8Nb + 0.2Zr' is the symbol. Below, we split the strings on ' + ' to
# check if the length (or number of elements) in the label and symbol are equal.
if len(data["_atom_site_type_symbol"][idx].split(' + ')) > \
len(data["_atom_site_label"][idx].split(' + ')):
# Dictionary to hold extracted elements and occupancies
els_occu = {}
# parse symbol to get element names and occupancy and store in "els_occu"
symbol_str = data["_atom_site_type_symbol"][idx]
symbol_str_lst = symbol_str.split(' + ')
for elocc_idx in range(len(symbol_str_lst)):
# Remove any bracketed items in the string
symbol_str_lst[elocc_idx] = re.sub('\([0-9]*\)', '', symbol_str_lst[elocc_idx].strip())
# Extract element name and its occupancy from the string, and store it as a
# key-value pair in "els_occ".
els_occu[str(re.findall('\D+', symbol_str_lst[elocc_idx].strip())[1]).replace('<sup>', '')] = \
float('0' + re.findall('\.?\d+', symbol_str_lst[elocc_idx].strip())[1])
x = str2float(data["_atom_site_fract_x"][idx])
y = str2float(data["_atom_site_fract_y"][idx])
z = str2float(data["_atom_site_fract_z"][idx])
coord = (x, y, z)
# Add each partially occupied element on the site coordinate
for et in els_occu:
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({parse_symbol(et): els_occu[parse_symbol(et)]})
else:
coord_to_species[match] += {parse_symbol(et): els_occu[parse_symbol(et)]}
idxs_to_remove.append(idx)
# Remove the original row by iterating over all keys in the CIF data looking for lists, which indicates
# multiple data items, one for each row, and remove items from the list that corresponds to the removed row,
# so that it's not processed by the rest of this function (which would result in an error).
for cif_key in data.data:
if type(data.data[cif_key]) == list:
for id in sorted(idxs_to_remove, reverse=True):
del data.data[cif_key][id]
############################################################
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except (KeyError, ValueError):
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(special_symbols.get(symbol) +
str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol, symbol))
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
else:
coord_to_species[match] += {el: occu}
if any([sum(c.values()) > 1 for c in coord_to_species.values()]):
warnings.warn("Some occupancies sum to > 1! If they are within "
"the tolerance, they will be rescaled.")
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(
sorted(list(coord_to_species.items()), key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def _get_structure(self, data, primitive):
"""
Generate structure from part of the cif.
"""
def parse_symbol(sym):
# Common representations for elements/water in cif files
# TODO: fix inconsistent handling of water
special = {
"D": "D",
"Hw": "H",
"Ow": "O",
"Wat": "O",
"wat": "O",
"OH": "",
"OH2": ""
}
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
if sym in special:
v = special[sym]
else:
v = special.get(m[0], m[0])
if len(m) > 1 or (m[0] in special):
warnings.warn("{} parsed as {}".format(sym, v))
return v
lattice = self.get_lattice(data)
# if magCIF, get magnetic symmetry moments and magmoms
# else standard CIF, and use empty magmom dict
if self.feature_flags["magcif_incommensurate"]:
raise NotImplementedError(
"Incommensurate structures not currently supported.")
elif self.feature_flags["magcif"]:
self.symmetry_operations = self.get_magsymops(data)
magmoms = self.parse_magmoms(data, lattice=lattice)
else:
self.symmetry_operations = self.get_symops(data)
magmoms = {}
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
coord_to_magmoms = OrderedDict()
def get_matching_coord(coord):
keys = list(coord_to_species.keys())
coords = np.array(keys)
for op in self.symmetry_operations:
c = op.operate(coord)
inds = find_in_coord_list_pbc(coords,
c,
atol=self._site_tolerance)
# cant use if inds, because python is dumb and np.array([0]) evaluates
# to False
if len(inds):
return keys[inds[0]]
return False
for i in range(len(data["_atom_site_label"])):
try:
# If site type symbol exists, use it. Otherwise, we use the
# label.
symbol = parse_symbol(data["_atom_site_type_symbol"][i])
except KeyError:
symbol = parse_symbol(data["_atom_site_label"][i])
if not symbol:
continue
if oxi_states is not None:
o_s = oxi_states.get(symbol, 0)
# use _atom_site_type_symbol if possible for oxidation state
if "_atom_site_type_symbol" in data.data.keys():
oxi_symbol = data["_atom_site_type_symbol"][i]
o_s = oxi_states.get(oxi_symbol, o_s)
try:
el = Specie(symbol, o_s)
except:
el = DummySpecie(symbol, o_s)
else:
el = get_el_sp(symbol)
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
magmom = magmoms.get(data["_atom_site_label"][i], Magmom(0))
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
coord_to_magmoms[coord] = magmom
else:
coord_to_species[match] += {el: occu}
coord_to_magmoms[
match] = None # disordered magnetic not currently supported
sum_occu = [sum(c.values()) for c in coord_to_species.values()]
if any([o > 1 for o in sum_occu]):
warnings.warn(
"Some occupancies (%s) sum to > 1! If they are within "
"the tolerance, they will be rescaled." % str(sum_occu))
allspecies = []
allcoords = []
allmagmoms = []
# check to see if magCIF file is disordered
if self.feature_flags["magcif"]:
for k, v in coord_to_magmoms.items():
if v is None:
# Proposed solution to this is to instead store magnetic moments
# as Specie 'spin' property, instead of site property, but this
# introduces ambiguities for end user (such as unintended use of
# `spin` and Specie will have fictious oxidation state).
raise NotImplementedError(
'Disordered magnetic structures not currently supported.'
)
if coord_to_species.items():
for species, group in groupby(sorted(list(
coord_to_species.items()),
key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
tmp_magmom = [
coord_to_magmoms[tmp_coord] for tmp_coord in tmp_coords
]
if self.feature_flags["magcif"]:
coords, magmoms = self._unique_coords(
tmp_coords, tmp_magmom)
else:
coords, magmoms = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
allmagmoms.extend(magmoms)
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords) and len(
allspecies) == len(allmagmoms):
if self.feature_flags["magcif"]:
struct = Structure(lattice,
allspecies,
allcoords,
site_properties={"magmom": allmagmoms})
else:
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
def get_slab(self, shift=0, tol=0.1, energy=None):
"""
This method takes in shift value for the c lattice direction and
generates a slab based on the given shift. You should rarely use this
method. Instead, it is used by other generation algorithms to obtain
all slabs.
Arg:
shift (float): A shift value in Angstrom that determines how much a
slab should be shifted.
tol (float): Tolerance to determine primitive cell.
energy (float): An energy to assign to the slab.
Returns:
(Slab) A Slab object with a particular shifted oriented unit cell.
"""
h = self._proj_height
nlayers_slab = int(math.ceil(self.min_slab_size / h))
nlayers_vac = int(math.ceil(self.min_vac_size / h))
nlayers = nlayers_slab + nlayers_vac
species = self.oriented_unit_cell.species_and_occu
props = self.oriented_unit_cell.site_properties
props = {k: v * nlayers_slab for k, v in props.items()}
frac_coords = self.oriented_unit_cell.frac_coords
frac_coords = np.array(frac_coords) +\
np.array([0, 0, -shift])[None, :]
frac_coords = frac_coords - np.floor(frac_coords)
a, b, c = self.oriented_unit_cell.lattice.matrix
new_lattice = [a, b, nlayers * c]
frac_coords[:, 2] = frac_coords[:, 2] / nlayers
all_coords = []
for i in range(nlayers_slab):
fcoords = frac_coords.copy()
fcoords[:, 2] += i / nlayers
all_coords.extend(fcoords)
slab = Structure(new_lattice,
species * nlayers_slab,
all_coords,
site_properties=props)
scale_factor = self.slab_scale_factor
# Whether or not to orthogonalize the structure
if self.lll_reduce:
lll_slab = slab.copy(sanitize=True)
mapping = lll_slab.lattice.find_mapping(slab.lattice)
scale_factor = np.dot(mapping[2], scale_factor)
slab = lll_slab
# Whether or not to center the slab layer around the vacuum
if self.center_slab:
avg_c = np.average([c[2] for c in slab.frac_coords])
slab.translate_sites(list(range(len(slab))), [0, 0, 0.5 - avg_c])
if self.primitive:
prim = slab.get_primitive_structure(tolerance=tol)
if energy is not None:
energy = prim.volume / slab.volume * energy
slab = prim
return Slab(slab.lattice,
slab.species_and_occu,
slab.frac_coords,
self.miller_index,
self.oriented_unit_cell,
shift,
scale_factor,
site_properties=slab.site_properties,
energy=energy)
def _get_structure(self, data, primitive, substitution_dictionary=None):
"""
Generate structure from part of the cif.
"""
# Symbols often representing
# common representations for elements/water in cif files
special_symbols = {"D": "D", "Hw": "H", "Ow": "O", "Wat": "O",
"wat": "O"}
elements = [el.symbol for el in Element]
lattice = self.get_lattice(data)
self.symmetry_operations = self.get_symops(data)
oxi_states = self.parse_oxi_states(data)
coord_to_species = OrderedDict()
def parse_symbol(sym):
if substitution_dictionary:
return substitution_dictionary.get(sym)
else:
m = re.findall(r"w?[A-Z][a-z]*", sym)
if m and m != "?":
return m[0]
return ""
def get_matching_coord(coord):
for op in self.symmetry_operations:
c = op.operate(coord)
for k in coord_to_species.keys():
if np.allclose(pbc_diff(c, k), (0, 0, 0),
atol=self._site_tolerance):
return tuple(k)
return False
for i in range(len(data["_atom_site_label"])):
symbol = parse_symbol(data["_atom_site_label"][i])
if symbol:
if symbol not in elements and symbol not in special_symbols:
symbol = symbol[:2]
else:
continue
# make sure symbol was properly parsed from _atom_site_label
# otherwise get it from _atom_site_type_symbol
try:
if symbol in special_symbols:
get_el_sp(special_symbols.get(symbol))
else:
Element(symbol)
except (KeyError, ValueError):
# sometimes the site doesn't have the type_symbol.
# we then hope the type_symbol can be parsed from the label
if "_atom_site_type_symbol" in data.data.keys():
symbol = data["_atom_site_type_symbol"][i]
if oxi_states is not None:
if symbol in special_symbols:
el = get_el_sp(special_symbols.get(symbol) +
str(oxi_states[symbol]))
else:
el = Specie(symbol, oxi_states.get(symbol, 0))
else:
el = get_el_sp(special_symbols.get(symbol, symbol))
x = str2float(data["_atom_site_fract_x"][i])
y = str2float(data["_atom_site_fract_y"][i])
z = str2float(data["_atom_site_fract_z"][i])
try:
occu = str2float(data["_atom_site_occupancy"][i])
except (KeyError, ValueError):
occu = 1
if occu > 0:
coord = (x, y, z)
match = get_matching_coord(coord)
if not match:
coord_to_species[coord] = Composition({el: occu})
else:
coord_to_species[match] += {el: occu}
if any([sum(c.values()) > 1 for c in coord_to_species.values()]):
warnings.warn("Some occupancies sum to > 1! If they are within "
"the tolerance, they will be rescaled.")
allspecies = []
allcoords = []
if coord_to_species.items():
for species, group in groupby(
sorted(list(coord_to_species.items()), key=lambda x: x[1]),
key=lambda x: x[1]):
tmp_coords = [site[0] for site in group]
coords = self._unique_coords(tmp_coords)
allcoords.extend(coords)
allspecies.extend(len(coords) * [species])
# rescale occupancies if necessary
for i, species in enumerate(allspecies):
totaloccu = sum(species.values())
if 1 < totaloccu <= self._occupancy_tolerance:
allspecies[i] = species / totaloccu
if allspecies and len(allspecies) == len(allcoords):
struct = Structure(lattice, allspecies, allcoords)
struct = struct.get_sorted_structure()
if primitive:
struct = struct.get_primitive_structure()
struct = struct.get_reduced_structure()
return struct
