def test_undo_and_redo_last_change(self):
trans = [
SubstitutionTransformation({"Li": "Na"}),
SubstitutionTransformation({"Fe": "Mn"})
]
ts = TransformedStructure(self.structure, trans)
self.assertEqual("NaMnPO4",
ts.final_structure.composition.reduced_formula)
ts.undo_last_change()
self.assertEqual("NaFePO4",
ts.final_structure.composition.reduced_formula)
ts.undo_last_change()
self.assertEqual("LiFePO4",
ts.final_structure.composition.reduced_formula)
self.assertRaises(IndexError, ts.undo_last_change)
ts.redo_next_change()
self.assertEqual("NaFePO4",
ts.final_structure.composition.reduced_formula)
ts.redo_next_change()
self.assertEqual("NaMnPO4",
ts.final_structure.composition.reduced_formula)
self.assertRaises(IndexError, ts.redo_next_change)
#Make sure that this works with filters.
f3 = ContainsSpecieFilter(['O2-'], strict_compare=True, AND=False)
ts.append_filter(f3)
ts.undo_last_change()
ts.redo_next_change()
def test_transmuter(self):
tsc = PoscarTransmuter.from_filenames(
[os.path.join(self.TEST_FILES_DIR, "POSCAR")])
tsc.append_transformation(RemoveSpeciesTransformation("O"))
self.assertEqual(len(tsc[0].final_structure), 8)
tsc.append_transformation(
SubstitutionTransformation({
"Fe": {
"Fe2+": 0.25,
"Mn3+": 0.75
},
"P": "P5+"
}))
tsc.append_transformation(OrderDisorderedStructureTransformation(),
extend_collection=50)
self.assertEqual(len(tsc), 4)
t = SuperTransformation([
SubstitutionTransformation({"Fe2+": "Mg2+"}),
SubstitutionTransformation({"Fe2+": "Zn2+"}),
SubstitutionTransformation({"Fe2+": "Be2+"}),
])
tsc.append_transformation(t, extend_collection=True)
self.assertEqual(len(tsc), 12)
for x in tsc:
# should be 4 trans + starting structure
self.assertEqual(
len(x),
5,
"something might be wrong with the number of transformations in the history",
)
# test the filter
tsc.apply_filter(
ContainsSpecieFilter(["Zn2+", "Be2+", "Mn4+"],
strict_compare=True,
AND=False))
self.assertEqual(len(tsc), 8)
self.assertEqual(
tsc.transformed_structures[0].as_dict()["history"][-1]["@class"],
"ContainsSpecieFilter",
)
tsc.apply_filter(ContainsSpecieFilter(["Be2+"]))
self.assertEqual(len(tsc), 4)
# Test set_parameter and add_tag.
tsc.set_parameter("para1", "hello")
self.assertEqual(
tsc.transformed_structures[0].as_dict()["other_parameters"]
["para1"],
"hello",
)
tsc.add_tags(["world", "universe"])
self.assertEqual(
tsc.transformed_structures[0].as_dict()["other_parameters"]
["tags"],
["world", "universe"],
)
def test_apply_transformation(self):
tl = [SubstitutionTransformation({"Li+": "Na+"}),
SubstitutionTransformation({"Li+": "K+"})]
t = SuperTransformation(tl)
coords = list()
coords.append([0, 0, 0])
coords.append([0.375, 0.375, 0.375])
coords.append([.5, .5, .5])
coords.append([0.875, 0.875, 0.875])
coords.append([0.125, 0.125, 0.125])
coords.append([0.25, 0.25, 0.25])
coords.append([0.625, 0.625, 0.625])
coords.append([0.75, 0.75, 0.75])
lattice = Lattice([[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603]])
struct = Structure(lattice, ["Li+", "Li+", "Li+", "Li+", "Li+", "Li+",
"O2-", "O2-"], coords)
s = t.apply_transformation(struct, return_ranked_list=True)
for s_and_t in s:
self.assertEqual(s_and_t['transformation']
.apply_transformation(struct),
s_and_t['structure'])
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(PymatgenTest.TEST_FILES_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 test_apply_transformation(self):
enum_trans = EnumerateStructureTransformation(refine_structure=True)
enum_trans2 = EnumerateStructureTransformation(refine_structure=True,
sort_criteria="nsites")
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.assertEqual(len(alls), expected_ans[i])
self.assertIsInstance(trans.apply_transformation(s), Structure)
for ss in alls:
self.assertIn("energy", ss)
alls = enum_trans2.apply_transformation(s, 100)
self.assertEqual(len(alls), expected_ans[i])
self.assertIsInstance(trans.apply_transformation(s), Structure)
for ss in alls:
self.assertIn("num_sites", ss)
# 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.assertEqual(len(alls), 3)
self.assertIsInstance(trans.apply_transformation(s), Structure)
for s in alls:
self.assertNotIn("energy", s)
def test_transmuter(self):
tsc = PoscarTransmuter.from_filenames(
[os.path.join(test_dir, "POSCAR")])
tsc.append_transformation(RemoveSpeciesTransformation('O'))
self.assertEqual(len(tsc[0].final_structure), 8)
tsc.append_transformation(
SubstitutionTransformation({
"Fe": {
"Fe2+": 0.25,
"Mn3+": .75
},
"P": "P5+"
}))
tsc.append_transformation(OrderDisorderedStructureTransformation(),
extend_collection=50)
self.assertEqual(len(tsc), 4)
t = SuperTransformation([
SubstitutionTransformation({"Fe2+": "Mg2+"}),
SubstitutionTransformation({"Fe2+": "Zn2+"}),
SubstitutionTransformation({"Fe2+": "Be2+"})
])
tsc.append_transformation(t, extend_collection=True)
self.assertEqual(len(tsc), 12)
for x in tsc:
self.assertEqual(
len(x), 5,
'something might be wrong with the number of transformations in the history'
) #should be 4 trans + starting structure
#test the filter
tsc.apply_filter(
ContainsSpecieFilter(['Zn2+', 'Be2+', 'Mn4+'],
strict_compare=True,
AND=False))
self.assertEqual(len(tsc), 8)
self.assertEqual(
tsc.get_transformed_structures()[0].as_dict()['history'][-1]
['@class'], 'ContainsSpecieFilter')
tsc.apply_filter(ContainsSpecieFilter(['Be2+']))
self.assertEqual(len(tsc), 4)
#Test set_parameter and add_tag.
tsc.set_parameter("para1", "hello")
self.assertEqual(
tsc.transformed_structures[0].as_dict()['other_parameters']
['para1'], 'hello')
tsc.add_tags(["world", "universe"])
self.assertEqual(
tsc.transformed_structures[0].as_dict()['other_parameters']
['tags'], ["world", "universe"])
def apply_substitution(orig_struct, sub):
"""Applies a substitution transformation to the structure.
Args:
orig_struct (Structure): A Pymatgen Structure of the original structure.
sub (dict): {thing_to_sub: {thing_to_sub: new_am1, new_thing: new_amt2}}
Returns:
A now-substituted structure prior to ordering.
"""
subber = SubstitutionTransformation(sub)
return subber.apply_transformation(orig_struct)
def apply_transformation(self, structure):
charge = structure.charge
specie = get_el_sp(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 test_apply_transformation(self):
t = SubstitutionTransformation({"Li+": "Na+", "O2-": "S2-"})
coords = []
coords.append([0, 0, 0])
coords.append([0.75, 0.75, 0.75])
coords.append([0.5, 0.5, 0.5])
coords.append([0.25, 0.25, 0.25])
lattice = Lattice([
[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603],
])
struct = Structure(lattice, ["Li+", "Li+", "O2-", "O2-"], coords)
s = t.apply_transformation(struct)
self.assertEqual(s.composition.formula, "Na2 S2")
def run_task(self, fw_spec):
db = SPStructuresMongoAdapter.auto_load()
tstructs = []
species = fw_spec['species']
t = fw_spec['threshold']
for p in SubstitutionPredictor(threshold=t).list_prediction(species):
subs = p['substitutions']
if len(set(subs.values())) < len(species):
continue
st = SubstitutionTransformation(subs)
target = map(str, subs.keys())
for snl in db.get_snls(target):
ts = TransformedStructure.from_snl(snl)
ts.append_transformation(st)
if ts.final_structure.charge == 0:
tstructs.append(ts)
transmuter = StandardTransmuter(tstructs)
f = RemoveDuplicatesFilter(structure_matcher=StructureMatcher(
comparator=ElementComparator(), primitive_cell=False))
transmuter.apply_filter(f)
results = []
for ts in transmuter.transformed_structures:
results.append(ts.to_snl([]).to_dict)
submissions = SPSubmissionsMongoAdapter.auto_load()
submissions.insert_results(fw_spec['submission_id'], results)
def setUp(self):
structure_dict = {
"lattice": {"a": 4.754150115,
"volume": 302.935463898643,
"c": 10.462573348,
"b": 6.090300362,
"matrix": [[4.754150115, 0.0, 0.0],
[0.0, 6.090300362, 0.0],
[0.0, 0.0, 10.462573348]],
"alpha": 90.0,
"beta": 90.0,
"gamma": 90.0},
"sites": [{"occu": 1, "abc": [0.0, 0.0, 0.0],
"xyz": [0.0, 0.0, 0.0],
"species": [{"occu": 1, "element": "Li"}],
"label": "Li"}, {"occu": 1,
"abc": [0.5000010396179928, 0.0,
0.5000003178950235],
"xyz": [2.37708, 0.0, 5.23129],
"species": [{"occu": 1,
"element": "Li"}],
"label": "Li"},
{"occu": 1, "abc": [0.0,
0.49999997028061194,
0.0],
"xyz": [0.0, 3.04515, 0.0],
"species": [{"occu": 1, "element": "Li"}], "label": "Li"}, {"occu": 1, "abc": [0.5000010396179928, 0.49999997028061194, 0.5000003178950235], "xyz": [2.37708, 3.04515, 5.23129], "species": [{"occu": 1, "element": "Li"}], "label": "Li"}, {"occu": 1, "abc": [0.7885825876997996, 0.5473161916279229, 0.3339168944194627], "xyz": [3.74904, 3.33332, 3.4936300000000005], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.2114173881108085, 0.452683748933301, 0.6660827855827808], "xyz": [1.00511, 2.75698, 6.968940000000001], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7114184277288014, 0.5473161916279229, 0.8339172123144861], "xyz": [3.38219, 3.33332, 8.72492], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7885825876997996, 0.9526820772587701, 0.3339168944194627], "xyz": [3.74904, 5.8021199999999995, 3.4936300000000005], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.28858365150718424, 0.047317863302453654, 0.16608342347556082], "xyz": [1.37197, 0.28818, 1.73766], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7440972443925447, 0.25000080611787734, 0.09613791622232937], "xyz": [3.537549999999999, 1.52258, 1.00585], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.28858365150718424, 0.452683748933301, 0.16608342347556082], "xyz": [1.37197, 2.75698, 1.73766], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.2114173881108085, 0.047317863302453654, 0.6660827855827808], "xyz": [1.00511, 0.28818, 6.968940000000001], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.2559006279926859, 0.7499991344433464, 0.9038627195677177], "xyz": [1.21659, 4.56772, 9.45673], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7559016676106785, 0.25000080611787734, 0.5961372783295493], "xyz": [3.5936699999999986, 1.52258, 6.2371300000000005], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7939989080466804, 0.7499991344433464, 0.5421304884886912], "xyz": [3.77479, 4.56772, 5.67208], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.24409830819992942, 0.7499991344433464, 0.40386240167269416], "xyz": [1.16048, 4.56772, 4.22544], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7060021073819206, 0.7499991344433464, 0.04213017059366761], "xyz": [3.35644, 4.56772, 0.44079000000000007], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.2939978684286875, 0.25000080611787734, 0.9578695094085758], "xyz": [1.3977099999999996, 1.52258, 10.02178], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.20600106776392774, 0.25000080611787734, 0.4578701473013559], "xyz": [0.9793599999999998, 1.52258, 4.7905], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.7114184277288014, 0.9526820772587701, 0.8339172123144861], "xyz": [3.38219, 5.8021199999999995, 8.72492], "species": [{"occu": 1, "element": "O"}], "label": "O"}, {"occu": 1, "abc": [0.5793611756830275, 0.7499991344433464, 0.9051119342269868], "xyz": [2.75437, 4.56772, 9.4698], "species": [{"occu": 1, "element": "P"}], "label": "P"}, {"occu": 1, "abc": [0.9206377363201961, 0.7499991344433464, 0.40511161633196324], "xyz": [4.37685, 4.56772, 4.23851], "species": [{"occu": 1, "element": "P"}], "label": "P"}, {"occu": 1, "abc": [0.42063880012758065, 0.25000080611787734, 0.09488774577525667], "xyz": [1.9997799999999994, 1.52258, 0.99277], "species": [{"occu": 1, "element": "P"}], "label": "P"}, {"occu": 1, "abc": [0.07936223949041206, 0.25000080611787734, 0.5948880636702801], "xyz": [0.3773, 1.52258, 6.22406], "species": [{"occu": 1, "element": "P"}], "label": "P"}, {"occu": 1, "abc": [0.021860899947623972, 0.7499991344433464, 0.7185507570598875], "xyz": [0.10393, 4.56772, 7.517890000000001], "species": [{"occu": 1, "element": "Fe"}], "label": "Fe"}, {"occu": 1, "abc": [0.478135932819614, 0.7499991344433464, 0.21855043916486389], "xyz": [2.27313, 4.56772, 2.2866], "species": [{"occu": 1, "element": "Fe"}], "label": "Fe"}, {"occu": 1, "abc": [0.9781369724376069, 0.25000080611787734, 0.2814489229423561], "xyz": [4.65021, 1.52258, 2.9446800000000004], "species": [{"occu": 1, "element": "Fe"}], "label": "Fe"}, {"occu": 1, "abc": [0.5218619395656168, 0.25000080611787734, 0.7814492408373795], "xyz": [2.48101, 1.52258, 8.17597], "species": [{"occu": 1, "element": "Fe"}], "label": "Fe"}]}
structure = Structure.from_dict(structure_dict)
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
def test_transmute(self):
if QeTransmuterTest.qe is None:
self.skipTest("No MongoDB present")
crit = {}
trans = [
SubstitutionTransformation({"Zn": "Mg"}),
OxidationStateDecorationTransformation({
"B": 3,
"O": -2,
"Mg": 2,
"Tb": 3
}),
PartialRemoveSpecieTransformation(
"Mg2+",
0.5,
algo=PartialRemoveSpecieTransformation.ALGO_COMPLETE)
]
self.qep = QeTransmuter(QeTransmuterTest.qe,
crit,
trans,
extend_collection=10)
trans_structures = self.qep.transformed_structures
self.assertEqual(len(trans_structures), 3)
for s in trans_structures:
self.assertEqual(s.final_structure.composition.reduced_formula,
"Tb2Mg(BO2)10")
def test_append_transformation(self):
t = SubstitutionTransformation({"Fe": "Mn"})
self.trans.append_transformation(t)
self.assertEqual(
"NaMnPO4", self.trans.final_structure.composition.reduced_formula)
self.assertEqual(len(self.trans.structures), 3)
coords = list()
coords.append([0, 0, 0])
coords.append([0.75, 0.5, 0.75])
lattice = [
[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603],
]
struct = Structure(lattice, ["Si4+", "Si4+"], coords)
ts = TransformedStructure(struct, [])
ts.append_transformation(
SupercellTransformation.from_scaling_factors(2, 1, 1))
alt = ts.append_transformation(
PartialRemoveSpecieTransformation(
"Si4+",
0.5,
algo=PartialRemoveSpecieTransformation.ALGO_COMPLETE),
5,
)
self.assertEqual(len(alt), 2)
def test_from_dict(self):
d = json.load(open(os.path.join(PymatgenTest.TEST_FILES_DIR, "transformations.json"), "r"))
d["other_parameters"] = {"tags": ["test"]}
ts = TransformedStructure.from_dict(d)
ts.other_parameters["author"] = "Will"
ts.append_transformation(SubstitutionTransformation({"Fe": "Mn"}))
self.assertEqual("MnPO4", ts.final_structure.composition.reduced_formula)
self.assertEqual(ts.other_parameters, {"author": "Will", "tags": ["test"]})
def test_apply_transformation(self):
enum_trans = EnumerateStructureTransformation(refine_structure=True)
enum_trans2 = EnumerateStructureTransformation(refine_structure=True,
sort_criteria="nsites")
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.assertEqual(len(alls), expected_ans[i])
self.assertIsInstance(trans.apply_transformation(s), Structure)
for ss in alls:
self.assertIn("energy", ss)
alls = enum_trans2.apply_transformation(s, 100)
self.assertEqual(len(alls), expected_ans[i])
self.assertIsInstance(trans.apply_transformation(s), Structure)
for ss in alls:
self.assertIn("num_sites", ss)
# 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.assertEqual(len(alls), 3)
self.assertIsInstance(trans.apply_transformation(s), Structure)
for s in alls:
self.assertNotIn("energy", s)
def test_init(self):
trans = []
trans.append(SubstitutionTransformation({"Fe": "Mn", "Fe2+": "Mn2+"}))
tsc = CifTransmuter.from_filenames([os.path.join(self.TEST_FILES_DIR, "MultiStructure.cif")], trans)
self.assertEqual(len(tsc), 2)
expected_ans = set(["Mn", "O", "Li", "P"])
for s in tsc:
els = set([el.symbol for el in s.final_structure.composition.elements])
self.assertEqual(expected_ans, els)
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 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 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.composition_prediction(
structure.composition, to_this_composition=False)
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 test_from_dict(self):
d = json.load(open(os.path.join(test_dir, 'transformations.json'),
'r'))
d['other_parameters'] = {'tags': ['test']}
ts = TransformedStructure.from_dict(d)
ts.other_parameters['author'] = 'Will'
ts.append_transformation(SubstitutionTransformation({"Fe": "Mn"}))
self.assertEqual("MnPO4",
ts.final_structure.composition.reduced_formula)
self.assertEqual(ts.other_parameters, {'author': 'Will',
'tags': ['test']})
def test_init(self):
trans = []
trans.append(SubstitutionTransformation({"Fe": "Mn"}))
tsc = PoscarTransmuter.from_filenames([
os.path.join(self.TEST_FILES_DIR, "POSCAR"),
os.path.join(self.TEST_FILES_DIR, "POSCAR")
], trans)
self.assertEqual(len(tsc), 2)
expected_ans = {"Mn", "O", "P"}
for s in tsc:
els = {el.symbol for el in s.final_structure.composition.elements}
self.assertEqual(expected_ans, els)
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_fractional_substitution(self):
t = SubstitutionTransformation({
"Li+": "Na+",
"O2-": {
"S2-": 0.5,
"Se2-": 0.5
}
})
# test the to and from dict on the nested dictionary
t = SubstitutionTransformation.from_dict(t.as_dict())
coords = []
coords.append([0, 0, 0])
coords.append([0.75, 0.75, 0.75])
coords.append([0.5, 0.5, 0.5])
coords.append([0.25, 0.25, 0.25])
lattice = Lattice([
[3.8401979337, 0.00, 0.00],
[1.9200989668, 3.3257101909, 0.00],
[0.00, -2.2171384943, 3.1355090603],
])
struct = Structure(lattice, ["Li+", "Li+", "O2-", "O2-"], coords)
s = t.apply_transformation(struct)
self.assertEqual(s.composition.formula, "Na2 Se1 S1")
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, 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_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.assertEqual(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.assertEqual(len(alls), 3)
self.assertIsInstance(trans.apply_transformation(s), Structure)
for s in alls:
self.assertNotIn("energy", s)
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
key=lambda item: item[1])
}
tar_d = {
k: v
for k, v in sorted(Composition(composition).as_dict().items(),
key=lambda item: item[1])
}
ori_t = tuple(ori_d)
tar_t = tuple(tar_d)
# print('original composition: ' + str(ori_t))
# print('target composition: ' + str(tar_t))
replace_syntax = find_diff(ori_t, tar_t)
trans = []
for syn in replace_syntax:
trans.append(SubstitutionTransformation(syn))
# os.system("cd ./CIF")
# print(os.getcwd())
try:
transmuter = CifTransmuter.from_filenames(
['./renamed_cif/' + str(mpid) + ".cif"], trans)
structures = transmuter.transformed_structures
#print(structures[0].final_structure)
w = CifWriter(structures[0].final_structure)
w.write_file("./inverse_design_target/" + name)
except:
continue
print('success!!')
# parser = CifParser('./file_name.cif')
from matplotlib.testing.compare import compare_images
from pymatgen.core import Lattice, Structure
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
from pymatviz.struct_vis import plot_structure_2d
from .conftest import save_reference_img
os.makedirs(fixt_dir := "tests/fixtures/struct_vis", exist_ok=True)
latt = Lattice.cubic(5)
struct = Structure(latt, ["Fe", "O"], [[0, 0, 0], [0.5, 0.5, 0.5]])
disord_struct: Structure = SubstitutionTransformation({
"Fe": {
"Fe": 0.75,
"C": 0.25
}
}).apply_transformation(struct)
@pytest.mark.parametrize("radii", [0.5, 1.2])
@pytest.mark.parametrize("rot", ["0x,0y,0z", "10x,-10y,0z"])
@pytest.mark.parametrize("labels", [True, False, {"P": "Phosphor"}])
def test_plot_structure_2d(radii, rot, labels, tmpdir):
# set explicit size to avoid ImageComparisonFailure in CI: sizes do not match
# expected (700, 1350, 3), actual (480, 640, 3)
plt.figure(figsize=(5, 5))
ax = plot_structure_2d(disord_struct,
atomic_radii=radii,
rotation=rot,
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.
"""
target_species = get_el_sp(target_species)
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 setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
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):
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)
from pymatgen.io.cif import CifParser
from pymatgen.transformations.standard_transformations import RemoveSpeciesTransformation
from pymatgen.transformations.standard_transformations import SubstitutionTransformation
if __name__ == '__main__':
# Read in a LiFePO4 structure from a cif.
parser = CifParser('/Users/derek/Downloads/LiFePO4_mp-19017_computed.cif')
struct = parser.get_structures()[0]
t = RemoveSpeciesTransformation(["Li"])
modified_structure = t.apply_transformation(struct)
t2 = SubstitutionTransformation({"Li", "Na"})
print(modified_structure)
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")