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 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 test_max_disordered_sites(self):
l = Lattice.cubic(4)
s_orig = Structure(l, [{"Li": 0.2, "Na": 0.2, "K": 0.6}, {"O": 1}],
[[0, 0, 0], [0.5, 0.5, 0.5]])
est = EnumerateStructureTransformation(max_cell_size=None,
max_disordered_sites=5)
s = est.apply_transformation(s_orig)
self.assertEqual(len(s), 8)
def test_max_disordered_sites(self):
l = Lattice.cubic(4)
s_orig = Structure(l, [{"Li": 0.2, "Na": 0.2, "K": 0.6}, {"O": 1}],
[[0, 0, 0], [0.5, 0.5, 0.5]])
est = EnumerateStructureTransformation(max_cell_size=None,
max_disordered_sites=5)
s = est.apply_transformation(s_orig)
self.assertEqual(len(s), 8)
def test_max_disordered_sites(self):
l = Lattice.cubic(4)
s_orig = Structure(l, [{"Li": 0.2, "Na": 0.2, "K": 0.6}, {"O": 1}],
[[0, 0, 0], [0.5, 0.5, 0.5]])
est = EnumerateStructureTransformation(max_cell_size=None,
max_disordered_sites=5)
dd = est.apply_transformation(s_orig, return_ranked_list=100)
self.assertEqual(len(dd), 9)
for d in dd:
self.assertEqual(len(d["structure"]), 10)
def test_max_disordered_sites(self):
l = Lattice.cubic(4)
s_orig = Structure(l, [{"Li": 0.2, "Na": 0.2, "K": 0.6}, {"O": 1}],
[[0, 0, 0], [0.5, 0.5, 0.5]])
est = EnumerateStructureTransformation(max_cell_size=None,
max_disordered_sites=5)
dd = est.apply_transformation(s_orig, return_ranked_list=100)
self.assertEqual(len(dd), 9)
for d in dd:
self.assertEqual(len(d["structure"]), 10)
def enumerate_ordering(self, structure):
# Generate the disordered structure first.
s = Structure.from_sites(structure.sites)
for indices, fraction in zip(self._indices, self._fractions):
for ind in indices:
new_sp = {sp: occu * fraction for sp, occu in structure[ind].species_and_occu.items()}
s[ind] = new_sp
# Perform enumeration
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
trans = EnumerateStructureTransformation()
return trans.apply_transformation(s, 10000)
def _enumerate_ordering(self, structure):
# Generate the disordered structure first.
s = structure.copy()
for indices, fraction in zip(self.indices, self.fractions):
for ind in indices:
new_sp = {sp: occu * fraction for sp, occu in structure[ind].species.items()}
s[ind] = new_sp
# Perform enumeration
from pymatgen.transformations.advanced_transformations import (
EnumerateStructureTransformation,
)
trans = EnumerateStructureTransformation()
return trans.apply_transformation(s, 10000)
def enumerate_ordering(self, structure):
# Generate the disordered structure first.
s = Structure.from_sites(structure.sites)
for indices, fraction in zip(self._indices, self._fractions):
for ind in indices:
new_sp = {
sp: occu * fraction
for sp, occu in structure[ind].species_and_occu.items()
}
s[ind] = new_sp
# Perform enumeration
from pymatgen.transformations.advanced_transformations import \
EnumerateStructureTransformation
trans = EnumerateStructureTransformation()
return trans.apply_transformation(s, 10000)
def enumerate_ordering(self, structure):
# Generate the disordered structure first.
editor = StructureEditor(structure)
for indices, fraction in zip(self._indices, self._fractions):
for ind in indices:
new_sp = {sp: occu * fraction
for sp, occu
in structure[ind].species_and_occu.items()}
editor.replace_site(ind, new_sp)
mod_s = editor.modified_structure
# Perform enumeration
from pymatgen.transformations.advanced_transformations import \
EnumerateStructureTransformation
trans = EnumerateStructureTransformation()
return trans.apply_transformation(mod_s, 10000)
def test_to_from_dict(self):
if not enumlib_present:
raise SkipTest("enumlib not present. Skipping "
"EnumerateStructureTransformationTest...")
trans = EnumerateStructureTransformation()
d = trans.to_dict
trans = EnumerateStructureTransformation.from_dict(d)
self.assertEqual(trans.symm_prec, 0.1)
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(PymatgenTest.TEST_FILES_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 std_enumeration(subbed_struct, cell_dim, coll_limit):
"""Performs an enumeration using the EnumLib library.
Args:
subbed_struct (Structure): A structure with some amount of disorder.
cell_dim (list): A list of maximum a, b, and c cell dimension limits.
coll_limit (int): Limit to the number of structures produced.
Returns:
A list of structure dictionaries? I need to test this.
"""
est = EnumerateStructureTransformation(min_cell_size=1,
max_cell_size=max(cell_dim))
if coll_limit == 1:
return est.apply_transformation(subbed_struct, return_ranked_list=False)
else:
return est.apply_transformation(subbed_struct,
return_ranked_list=coll_limit)
def test_apply_transformation_mult(self):
# Test returning multiple structures from each transformation.
disord = Structure(np.eye(3) * 4.209, [{"Cs+": 0.5, "K+": 0.5}, "Cl-"],
[[0, 0, 0], [0.5, 0.5, 0.5]])
disord.make_supercell([2, 2, 1])
tl = [EnumerateStructureTransformation(),
OrderDisorderedStructureTransformation()]
t = SuperTransformation(tl, nstructures_per_trans=10)
self.assertEqual(len(t.apply_transformation(disord,
return_ranked_list=20)), 8)
t = SuperTransformation(tl)
self.assertEqual(len(t.apply_transformation(disord,
return_ranked_list=20)), 2)
def test_occu_tol(self):
s = PymatgenTest.get_structure("Li2O")
s["Li+"] = {"Li+": 0.48}
trans = EnumerateStructureTransformation(occu_tol=4)
ss = trans.apply_transformation(s, return_ranked_list=10)
self.assertEqual(len(ss), 1)
s = PymatgenTest.get_structure("Li2O")
s["Li+"] = {"Li+": 0.24}
trans = EnumerateStructureTransformation(max_cell_size=2, occu_tol=4)
ss = trans.apply_transformation(s, return_ranked_list=10)
self.assertEqual(len(ss), 3)
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
structure = Structure.from_file("1.cif")
structure[0] = {"Fe2+": 1 / 3, "Li+": 2 / 3}
structure[1] = {"Fe2+": 1 / 3, "Li+": 2 / 3}
structure[2] = {"Fe2+": 1 / 3, "Li+": 2 / 3}
structure[3] = {"S2-": 1}
structure[4] = {"O2-": 1}
structure
structure.make_supercell([2, 2, 2])
structure
enum = EnumerateStructureTransformation()
enumerated = enum.apply_transformation(
structure, return_ranked_list=100) # return no more than 100 structures
structures = [d["structure"] for d in enumerated]
print("%d structures returned." % len(structures))
from pymatgen.analysis.energy_models import EwaldElectrostaticModel
ed = EwaldElectrostaticModel()
#ed.get_energy(structures[1])
structures20 = structures[:20]
batch_write_input(elect_stru, vasp_input_set=MPRelaxSet)
# elect_stru=structures20=structures[::10]
# for st in elect_stru:
def test_to_from_dict(self):
trans = EnumerateStructureTransformation()
d = trans.as_dict()
trans = EnumerateStructureTransformation.from_dict(d)
self.assertEqual(trans.symm_prec, 0.1)
def disorder_in_supercell(
pristineStruct,
# substSpecies={},
supercell_size=[1, 1, 1],
number_of_struct=10):
ini = pristineStruct.copy()
# substitued structures =====================================
# substitued_1 = sd.SubstitutionTransformation(
# {"Na": {"Li": 0.5, "Fe": 0.5}}).apply_transformation(ini)
substitued_2 = sd.SubstitutionTransformation({
"Ti": {
"Ti": 7 / 8,
"Fe": 1 / 8
}
}).apply_transformation(ini)
ini = substitued_2
# GENERATE OXIDATION STATES ================================
try:
oxi = sd.AutoOxiStateDecorationTransformation().apply_transformation(
ini)
except ValueError:
print("Forced to set oxidation by hand")
oxi.add_oxidation_state_by_element({
"Na": 1,
"Mg": 2,
"Mn": 3.5,
"O": -2,
"Li": 1,
"Ca": 2,
"Fe": 3,
"Zn": 4,
"Co": 2.5,
"Ti": 4,
"S": -2,
'P': 5
})
print(oxi)
# transform into 2,2,2 supercell
[x, y, z] = supercell_size
megaOxi = sd.SupercellTransformation.from_scaling_factors(
x, y, z).apply_transformation(oxi)
# dictPri = {'structure': megaOxi, 'id': "pristine"}
print(megaOxi)
# chosenList.append(dictPri) #uncomment to compute pistine !
#
# generation of a list of ordered structures from the disordered one
chosenList = []
try:
type_of_algo = input(
"type of algo : [E]numlib (more efficient but weaker) // [P]ymatgen built-in (slower but safer)"
)[0]
except BaseException:
type_of_algo = "E"
print("trying E then P sequence")
try:
if type_of_algo == "E":
enum = EnumerateStructureTransformation(
min_cell_size=1,
max_cell_size=None,
symm_prec=0.1,
refine_structure=False,
enum_precision_parameter=0.1,
check_ordered_symmetry=True,
max_disordered_sites=100,
sort_criteria='ewald',
timeout=None)
enumList = enum.apply_transformation(
megaOxi, return_ranked_list=number_of_struct)
except Exception as ex:
print(
"error during enumlib method : {} \n fall back to [P]ymatgen buit-in method",
ex)
type_of_algo = "P"
try:
if type_of_algo == "P":
enumList = sd.OrderDisorderedStructureTransformation(
).apply_transformation(megaOxi,
return_ranked_list=number_of_struct)
except Exception as ex:
print(
"error during [P]ymatgen buit-in method {} \n You're on your own beyond this point...",
ex)
for i, chosenStruct in enumerate(enumList):
chosenList.append({
'structure': chosenStruct['structure'],
"id": str(i + 1)
})
print(chosenList)
return chosenList
from pymatgen import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
structure = Structure.from_file(
"D:/Program Files (x86)/python2/file/EntryWithCollCode418490.cif")
# （获取文件 当在自己电脑中调试的时候需要将相关的路径统一一下）
print(structure)
# loop over all sites in the structure
for i, site in enumerate(structure):
# （enumerate 为枚举类型）
# change the occupancy of Li+ disordered sites to 0.5
if not site.is_ordered:
structure[i] = {"Li+": 0.5}
print("The composition after adjustments is %s." %
structure.composition.reduced_formula)
analyzer = SpacegroupAnalyzer(structure)
# SpacegroupAnalyzer为空间群分析 晶体内部结构中全部对称要素的集合称为 “空间群” 。
# 一切晶体结构中总共只能有230种不同的对称要素组合方式，即230个空间群所谓点空间群，
# 是由一个平移群和一个点群对称操作组合而成的，它的一般对称操作可以写成（R | t (αβγ)），其中R表示点群对称操作，t(αβγ)表示平移操作
# 为了保持可排序的顺序，我们只在原始单元上执行枚举。原始单元可以使用*空间群分析器*获得
prim_cell = analyzer.find_primitive()
print(prim_cell)
enum = EnumerateStructureTransformation() # 变换枚举结构
enumerated = enum.apply_transformation(prim_cell, 90) # 在这需要限制返回结构的数量
structures = [d["structure"] for d in enumerated]
print("%d structures returned." % len(structures))
from pymatgen import Structure
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer #空间群分析器
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation #枚举结构转换
from pymatgen.io.vasp.sets import batch_write_input, MPRelaxSet
structure = Structure.from_file("G:/Python/files/EntryWithCollCode418490.cif"
) #我们将首先从ICSD中发现的晶体学信息文件（CIF）中读取结构
print(structure)
#目前我们所拥有的结构是无序的，并且Li的占有率跟成分不符合，需要调整
# loop over all sites in the structure在结构中所有的位置进行循环
for i, site in enumerate(structure):
# change the occupancy of Li+ disordered sites to 0.5（也就是把结构矩阵中的Li+那一列调整为0.5）
if not site.is_ordered:
structure[i] = {"Li+": 0.5}
print("The composition after adjustments is %s." %
structure.composition.reduced_formula) #获得化学计量电荷平衡的L6PS5Cl。
#为了保持可排序的顺序，我们只在原始单元上执行枚举。原始单元可以使用空间群分析器获得
analyzer = SpacegroupAnalyzer(structure)
prim_cell = analyzer.find_primitive() #找到原始单元，并且作为Structure对象返回
print(prim_cell)
#我们将使用枚举结构结构转换类来枚举所有对称的不同结构。枚举结构是一个围绕枚举库的用户友好包装器，它是由HART等人编写的派生结构的FORTRAN库
enum = EnumerateStructureTransformation()
enumerated = enum.apply_transformation(
prim_cell, 100) # return no more than 100 structures
#structures = [d["structure"] for d in enumerated]
#print("%d structures returned." % len(structures))
#!/usr/bin/python3
from pymatgen import Structure
from pymatgen.transformations.standard_transformations import OrderDisorderedStructureTransformation
from pymatgen.transformations.advanced_transformations import EnumerateStructureTransformation
from pymatgen.io.cif import CifWriter
import sys
try:
cif_filename = sys.argv[1]
except ValueError:
print("please give the cif file name as the 1st argument!")
s = Structure.from_file(cif_filename)
prim = s.get_primitive_structure()
# Merge sites that are less than 1A apart.
prim.merge_sites(1)
prim = prim.get_sorted_structure()
t = EnumerateStructureTransformation()
ordered = t.apply_transformation(prim, 5)
print(len(ordered))
for i in range(len(ordered)):
c = CifWriter(ordered[i]['structure'], symprec=0.01)
c.write_file("LLZO_ordered-" + str(i) + ".cif")
def test_to_from_dict(self):
trans = EnumerateStructureTransformation()
d = trans.to_dict
trans = EnumerateStructureTransformation.from_dict(d)
self.assertEqual(trans.symm_prec, 0.1)