def test_apply_transformation_fast(self):
t = PartialRemoveSitesTransformation([tuple(range(4)), tuple(range(4, 8))], [0.5, 0.5], PartialRemoveSitesTransformation.ALGO_FAST)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
t = PartialRemoveSitesTransformation([tuple(range(8))], [0.5], PartialRemoveSitesTransformation.ALGO_FAST)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
def apply_transformation(self, structure, return_ranked_list=False):
"""
Apply the transformation.
Args:
structure:
input structure
return_ranked_list:
Boolean stating whether or not multiple structures are
returned. If return_ranked_list is an int, that number of
structures is returned.
Returns:
Depending on returned_ranked list, either a transformed structure
or
a list of dictionaries, where each dictionary is of the form
{"structure" = .... , "other_arguments"}
the key "transformation" is reserved for the transformation that
was actually applied to the structure.
This transformation is parsed by the alchemy classes for generating
a more specific transformation history. Any other information will
be stored in the transformation_parameters dictionary in the
transmuted structure class.
"""
sp = smart_element_or_specie(self._specie)
specie_indices = [i for i in xrange(len(structure)) if structure[i].species_and_occu == Composition({sp: 1})]
trans = PartialRemoveSitesTransformation([specie_indices], [self._frac], algo=self._algo)
return trans.apply_transformation(structure, return_ranked_list)
def apply_transformation(self, structure, return_ranked_list=False):
"""
Apply the transformation.
Args:
structure: input structure
return_ranked_list (bool/int): Boolean stating whether or not
multiple structures are returned. If return_ranked_list is
an int, that number of structures is returned.
Returns:
Depending on returned_ranked list, either a transformed structure
or a list of dictionaries, where each dictionary is of the form
{"structure" = .... , "other_arguments"}
the key "transformation" is reserved for the transformation that
was actually applied to the structure.
This transformation is parsed by the alchemy classes for generating
a more specific transformation history. Any other information will
be stored in the transformation_parameters dictionary in the
transmuted structure class.
"""
sp = get_el_sp(self.specie_to_remove)
specie_indices = [i for i in range(len(structure)) if structure[i].species == Composition({sp: 1})]
trans = PartialRemoveSitesTransformation([specie_indices], [self.fraction_to_remove], algo=self.algo)
return trans.apply_transformation(structure, return_ranked_list)
def test_apply_transformation_enumerate(self):
t = PartialRemoveSitesTransformation(
[tuple(range(4)), tuple(range(4, 8))], [0.5, 0.5],
PartialRemoveSitesTransformation.ALGO_ENUMERATE)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
s = t.apply_transformation(self.struct, 12)
self.assertEqual(len(s), 12)
def test_apply_transformation_best_first(self):
t = PartialRemoveSitesTransformation(
[tuple(range(4)), tuple(range(4, 8))],
[0.5, 0.5],
PartialRemoveSitesTransformation.ALGO_BEST_FIRST,
)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
def test_apply_transformation_enumerate(self):
t = PartialRemoveSitesTransformation(
[tuple(range(4)), tuple(range(4, 8))],
[0.5, 0.5],
PartialRemoveSitesTransformation.ALGO_ENUMERATE
)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
s = t.apply_transformation(self.struct, 12)
self.assertEqual(len(s), 12)
def test_apply_transformation_enumerate(self):
if not enumlib_present:
raise SkipTest("enumlib not present. "
"Skipping ALGO.ENUMERATE "
"PartialRemoveSitesTransformationTest...")
t = PartialRemoveSitesTransformation(
[tuple(range(4)), tuple(range(4, 8))],
[0.5, 0.5],
PartialRemoveSitesTransformation.ALGO_ENUMERATE
)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O2")
s = t.apply_transformation(self.struct, 12)
self.assertEqual(len(s), 12)
def test_str(self):
d = PartialRemoveSitesTransformation([tuple(range(4))],
[0.5]).as_dict()
self.assertIsNotNone(str(d))
def test_to_from_dict(self):
d = PartialRemoveSitesTransformation([tuple(range(4))],
[0.5]).as_dict()
t = PartialRemoveSitesTransformation.from_dict(d)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O4")
def test_to_from_dict(self):
d = PartialRemoveSitesTransformation([tuple(range(4))], [0.5]).as_dict()
t = PartialRemoveSitesTransformation.from_dict(d)
s = t.apply_transformation(self.struct)
self.assertEqual(s.formula, "Li2 O4")
from pymatgen import Structure
from pymatgen.transformations.site_transformations import PartialRemoveSitesTransformation
estrutura = Structure.from_file("POSCAR")
remove = PartialRemoveSitesTransformation([range(0, 63)], [1 / 63], algo=1)
result = remove.apply_transformation(estrutura, return_ranked_list=63)
for i, item in enumerate(result):
item["structure"].to(filename="POSCAR{:02d}".format(i), tmp="POSCAR")
