def test_apply_transformation(self):
enum_trans = EnumerateStructureTransformation(refine_structure=True)
p = Poscar.from_file(os.path.join(test_dir, 'POSCAR.LiFePO4'),
check_for_POTCAR=False)
struct = p.structure
expected_ans = [1, 3, 1]
for i, frac in enumerate([0.25, 0.5, 0.75]):
trans = SubstitutionTransformation({'Fe': {'Fe': frac}})
s = trans.apply_transformation(struct)
oxitrans = OxidationStateDecorationTransformation({'Li': 1,
'Fe': 2,
'P': 5,
'O': -2})
s = oxitrans.apply_transformation(s)
alls = enum_trans.apply_transformation(s, 100)
self.assertEquals(len(alls), expected_ans[i])
self.assertIsInstance(trans.apply_transformation(s), Structure)
for s in alls:
self.assertIn("energy", s)
#make sure it works for non-oxidation state decorated structure
trans = SubstitutionTransformation({'Fe': {'Fe': 0.5}})
s = trans.apply_transformation(struct)
alls = enum_trans.apply_transformation(s, 100)
self.assertEquals(len(alls), 3)
self.assertIsInstance(trans.apply_transformation(s), Structure)
for s in alls:
self.assertNotIn("energy", s)
def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError(
"MultipleSubstitutionTransformation has no single"
" best structure output. Must use"
" return_ranked_list."
)
outputs = []
for charge, el_list in self._substitution_dict.items():
mapping = {}
if charge > 0:
sign = "+"
else:
sign = "-"
dummy_sp = "X{}{}".format(str(charge), sign)
mapping[self._sp_to_replace] = {self._sp_to_replace: 1 - self._r_fraction, dummy_sp: self._r_fraction}
trans = SubstitutionTransformation(mapping)
dummy_structure = trans.apply_transformation(structure)
if self._charge_balance_species is not None:
cbt = ChargeBalanceTransformation(self._charge_balance_species)
dummy_structure = cbt.apply_transformation(dummy_structure)
if self._order:
trans = OrderDisorderedStructureTransformation()
dummy_structure = trans.apply_transformation(dummy_structure)
for el in el_list:
if charge > 0:
sign = "+"
else:
sign = "-"
st = SubstitutionTransformation({"X{}+".format(str(charge)): "{}{}{}".format(el, charge, sign)})
new_structure = st.apply_transformation(dummy_structure)
outputs.append({"structure": new_structure})
return outputs
def test_init(self):
if not enumlib_present:
raise SkipTest("enumlib not present. Skipping...")
test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files')
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(s.composition
.get_atomic_fraction(Element("Li")),
0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 1,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(s.composition
.get_atomic_fraction(Element("Li")),
0.25 / 6.25)
#Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{'Fe':0.5}],
[[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
#Make sure it works properly when symmetry is broken by ordered sites.
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
s = subtrans.apply_transformation(struct)
#REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]],
[{"Si": 0.5}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
def test_init(self):
test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "test_files")
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({"Li": {"Li": 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(s.composition.get_atomic_fraction(Element("Li")), 0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2, refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({"Li": {"Li": 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 1, refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(s.composition.get_atomic_fraction(Element("Li")), 0.25 / 6.25)
# Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{"Fe": 0.5}], [[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
# Make sure it works properly when symmetry is broken by ordered sites.
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({"Li": {"Li": 0.25}})
s = subtrans.apply_transformation(struct)
# REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{"Si": 0.5}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
struct = Structure.from_file(os.path.join(test_dir, "EnumerateTest.json"))
adaptor = EnumlibAdaptor(struct, 1, 1)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 2)
def apply_transformation(self, structure):
charge = structure.charge
specie = smart_element_or_specie(self._charge_balance_sp)
num_to_remove = charge / specie.oxi_state
num_in_structure = structure.composition[specie]
removal_fraction = num_to_remove / num_in_structure
if removal_fraction < 0:
raise ValueError("addition of specie not yet supported by " "ChargeBalanceTransformation")
trans = SubstitutionTransformation({self._charge_balance_sp: {self._charge_balance_sp: 1 - removal_fraction}})
return trans.apply_transformation(structure)
def apply_transformation(self, structure, return_ranked_list=False):
if not return_ranked_list:
raise ValueError("SubstitutionPredictorTransformation doesn't"
" support returning 1 structure")
preds = self._substitutor.pred_from_comp(structure.composition)
preds.sort(key=lambda x: x['probability'], reverse=True)
outputs = []
for pred in preds:
st = SubstitutionTransformation(pred['substitutions'])
output = {'structure': st.apply_transformation(structure),
'probability': pred['probability'],
'threshold': self._threshold, 'substitutions': {}}
#dictionary keys have to be converted to strings for JSON
for key, value in pred['substitutions'].items():
output['substitutions'][str(key)] = str(value)
outputs.append(output)
return outputs
def pred_from_structures(self, target_species, structures_list,
remove_duplicates=True):
"""
performs a structure prediction targeting compounds containing the
target_species and based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id
Returns:
a list of TransformedStructure objects.
"""
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) \
!= len(target_species):
raise ValueError("the species in target_species are not allowed"
+ "for the probability model you are using")
for permut in itertools.permutations(target_species):
for s in structures_list:
#check if: species are in the domain,
#and the probability of subst. is above the threshold
els = s['structure'].composition.elements
if len(els) == len(permut) and \
len(list(set(els) & set(self.get_allowed_species()))) == \
len(els) and self._sp.cond_prob_list(permut, els) > \
self._threshold:
clean_subst = {els[i]: permut[i]
for i in xrange(0, len(els))
if els[i] != permut[i]}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(
transf.apply_transformation(s['structure'])):
ts = TransformedStructure(
s['structure'], [transf], history=[s['id']],
other_parameters={
'type': 'structure_prediction',
'proba': self._sp.cond_prob_list(permut, els)}
)
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(RemoveDuplicatesFilter(
symprec=self._symprec))
return transmuter.transformed_structures
def test_init(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1,
2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")),
0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
2,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
1,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")),
0.25 / 6.25)
# Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{
'Fe': 0.5
}], [[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
# Make sure it works properly when symmetry is broken by ordered sites.
struct = self.get_structure("LiFePO4")
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
s = subtrans.apply_transformation(struct)
# REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{
"Si": 0.5
}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct,
1,
3,
enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
struct = Structure.from_file(
os.path.join(test_dir, "EnumerateTest.json"))
adaptor = EnumlibAdaptor(struct, 1, 1)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 2)
def test_init(self):
test_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..",
'test_files')
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.5}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct), 1, 2)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 86)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")), 0.5 / 6.5)
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
2,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 52)
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
adaptor = EnumlibAdaptor(subtrans.apply_transformation(struct),
1,
1,
refine_structure=True)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 1)
for s in structures:
self.assertAlmostEqual(
s.composition.get_atomic_fraction(Element("Li")), 0.25 / 6.25)
#Make sure it works for completely disordered structures.
struct = Structure([[10, 0, 0], [0, 10, 0], [0, 0, 10]], [{
'Fe': 0.5
}], [[0, 0, 0]])
adaptor = EnumlibAdaptor(struct, 1, 2)
adaptor.run()
self.assertEqual(len(adaptor.structures), 3)
#Make sure it works properly when symmetry is broken by ordered sites.
parser = CifParser(os.path.join(test_dir, "LiFePO4.cif"))
struct = parser.get_structures(False)[0]
subtrans = SubstitutionTransformation({'Li': {'Li': 0.25}})
s = subtrans.apply_transformation(struct)
#REmove some ordered sites to break symmetry.
removetrans = RemoveSitesTransformation([4, 7])
s = removetrans.apply_transformation(s)
adaptor = EnumlibAdaptor(s, 1, 1, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 4)
struct = Structure([[3, 0, 0], [0, 3, 0], [0, 0, 3]], [{
"Si": 0.5
}] * 2, [[0, 0, 0], [0.5, 0.5, 0.5]])
adaptor = EnumlibAdaptor(struct, 1, 3, enum_precision_parameter=0.01)
adaptor.run()
structures = adaptor.structures
self.assertEqual(len(structures), 10)
def pred_from_structures(self,
target_species,
structures_list,
remove_duplicates=True,
remove_existing=False):
"""
performs a structure prediction targeting compounds containing all of
the target_species, based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Notes:
If the default probability model is used, input structures must
be oxidation state decorated. See AutoOxiStateDecorationTransformation
This method does not change the number of species in a structure. i.e
if the number of target species is 3, only input structures containing
3 species will be considered.
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id.
remove_duplicates:
if True, the duplicates in the predicted structures will
be removed
remove_existing:
if True, the predicted structures that already exist in the
structures_list will be removed
Returns:
a list of TransformedStructure objects.
"""
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) \
!= len(target_species):
raise ValueError("the species in target_species are not allowed " +
"for the probability model you are using")
for permut in itertools.permutations(target_species):
for s in structures_list:
#check if: species are in the domain,
#and the probability of subst. is above the threshold
els = s['structure'].composition.elements
if len(els) == len(permut) and \
len(list(set(els) & set(self.get_allowed_species()))) == \
len(els) and self._sp.cond_prob_list(permut, els) > \
self._threshold:
clean_subst = {
els[i]: permut[i]
for i in range(0, len(els)) if els[i] != permut[i]
}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(
transf.apply_transformation(s['structure'])):
ts = TransformedStructure(s['structure'], [transf],
history=[{
"source": s['id']
}],
other_parameters={
'type':
'structure_prediction',
'proba':
self._sp.cond_prob_list(
permut, els)
})
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(
RemoveDuplicatesFilter(symprec=self._symprec))
if remove_existing:
#Make the list of structures from structures_list that corresponds to the
#target species
chemsys = list(set([sp.symbol for sp in target_species]))
structures_list_target = [
st['structure'] for st in structures_list
if Substitutor._is_from_chemical_system(
chemsys, st['structure'])
]
transmuter.apply_filter(
RemoveExistingFilter(structures_list_target,
symprec=self._symprec))
return transmuter.transformed_structures
def pred_from_structures(self,
target_species,
structures_list,
remove_duplicates=True):
"""
performs a structure prediction targeting compounds containing the
target_species and based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id
Returns:
a list of TransformedStructure objects.
"""
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) \
!= len(target_species):
return ValueError("the species in target_species are not allowed" +
"for the probability model you are using")
for permut in itertools.permutations(target_species):
for s in structures_list:
#check if: species are in the domain,
#and the probability of subst. is above the threshold
els = s['structure'].composition.elements
if len(els) == len(permut) and \
len(list(set(els) & set(self.get_allowed_species()))) == \
len(els) and self._sp.cond_prob_list(permut, els) > \
self._threshold:
clean_subst = {
els[i]: permut[i]
for i in xrange(0, len(els)) if els[i] != permut[i]
}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(
transf.apply_transformation(s['structure'])):
ts = TransformedStructure(s['structure'], [transf],
history=[s['id']],
other_parameters={
'type':
'structure_prediction',
'proba':
self._sp.cond_prob_list(
permut, els)
})
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(
RemoveDuplicatesFilter(symprec=self._symprec))
return transmuter.transformed_structures
def pred_from_structures(self, target_species, structures_list, remove_duplicates=True, remove_existing=False):
"""
performs a structure prediction targeting compounds containing all of
the target_species, based on a list of structure (those structures
can for instance come from a database like the ICSD). It will return
all the structures formed by ionic substitutions with a probability
higher than the threshold
Notes:
If the default probability model is used, input structures must
be oxidation state decorated.
This method does not change the number of species in a structure. i.e
if the number of target species is 3, only input structures containing
3 species will be considered.
Args:
target_species:
a list of species with oxidation states
e.g., [Specie('Li',1),Specie('Ni',2), Specie('O',-2)]
structures_list:
a list of dictionnary of the form {'structure':Structure object
,'id':some id where it comes from}
the id can for instance refer to an ICSD id.
remove_duplicates:
if True, the duplicates in the predicted structures will
be removed
remove_existing:
if True, the predicted structures that already exist in the
structures_list will be removed
Returns:
a list of TransformedStructure objects.
"""
result = []
transmuter = StandardTransmuter([])
if len(list(set(target_species) & set(self.get_allowed_species()))) != len(target_species):
raise ValueError(
"the species in target_species are not allowed" + "for the probability model you are using"
)
for permut in itertools.permutations(target_species):
for s in structures_list:
# check if: species are in the domain,
# and the probability of subst. is above the threshold
els = s["structure"].composition.elements
if (
len(els) == len(permut)
and len(list(set(els) & set(self.get_allowed_species()))) == len(els)
and self._sp.cond_prob_list(permut, els) > self._threshold
):
clean_subst = {els[i]: permut[i] for i in xrange(0, len(els)) if els[i] != permut[i]}
if len(clean_subst) == 0:
continue
transf = SubstitutionTransformation(clean_subst)
if Substitutor._is_charge_balanced(transf.apply_transformation(s["structure"])):
ts = TransformedStructure(
s["structure"],
[transf],
history=[{"source": s["id"]}],
other_parameters={
"type": "structure_prediction",
"proba": self._sp.cond_prob_list(permut, els),
},
)
result.append(ts)
transmuter.append_transformed_structures([ts])
if remove_duplicates:
transmuter.apply_filter(RemoveDuplicatesFilter(symprec=self._symprec))
if remove_existing:
# Make the list of structures from structures_list that corresponds to the
# target species
chemsys = list(set([sp.symbol for sp in target_species]))
structures_list_target = [
st["structure"]
for st in structures_list
if Substitutor._is_from_chemical_system(chemsys, st["structure"])
]
transmuter.apply_filter(RemoveExistingFilter(structures_list_target, symprec=self._symprec))
return transmuter.transformed_structures
from pymatgen import Structure
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
from pymatgen.transformations.standard_transformations import OrderDisorderedStructureTransformation
structure = Structure.from_file("POSCAR")
substitution = SubstitutionTransformation({"Nb3+": {"Nb3+":0.5, "Fe3+":0.5}})
result = substitution.apply_transformation(structure)
order = OrderDisorderedStructureTransformation(algo=2)
ResultOrder = order.apply_transformation(result, return_ranked_list=True)
for i, item in enumerate(ResultOrder):
item['structure'].to(filename="POSCAR{:02d}".format(i))
#ResultOrder[0]['structure'].to(filename="POSCAR1")
