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):
t = RemoveSpeciesTransformation(["Li+"])
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, "O2")
d = t.as_dict()
self.assertEqual(type(RemoveSpeciesTransformation.from_dict(d)),
RemoveSpeciesTransformation)
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 metal_envt(struc):
# Counts the Me environment in a structure
# argument : pymatgen.core.structure
# return : 2elt list of (7 element list) : the i th element is the
# propotion of A with i A neighbors
A = "Mg"
B = "Mn"
C = "Na"
# neighborA = [0 for i in range(0, 7, 1)]
# neighborB = [0 for i in range(0, 7, 1)]
# A_indices = struc.indices_from_symbol(A)
nbC = len(struc.indices_from_symbol(C))
neighborC = np.zeros(6)
#print("{0} {1}".format(nbC,C))
sCopy = RemoveSpeciesTransformation(["O"]).apply_transformation(struc)
# print(sCopy)
mini = MinimumDistanceNN(tol=0.3)
for i, site in enumerate(sCopy):
# print(str(i))
# print(site)
siteName = site.species_string
if siteName == C:
neigList = mini.get_nn_info(sCopy, i)
#print("{0} closest neighboors \n".format(len(neigList)))
coordA = 0
coordB = 0
for neighbor in neigList:
# index = neighbor['site_index']
neighborName = neighbor['site'].species_string
#print(" ( {0} at {1} ) ".format(neighborName,index))
if neighborName == A:
coordA += 1
if neighborName == B:
coordB += 1
neighborC[coordA] += 1
#print("neighborC list :" , neighborC)
if nbC == 0:
normalizedNeighborC = [0 for i in neighborC]
else:
normalizedNeighborC = [i / nbC for i in neighborC]
print("coordination {0} : {1} \nnormalized by {2} : {3} \n".format(
C, neighborC, nbC, normalizedNeighborC))
return normalizedNeighborC
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)
def metalBondCount(struc):
# Counts the metal metal bounds in a structure
# argument pymatgen.core.structure
# return [ normalizedBonds, normalizedAA, normalizedBB]
# normalizedBond = 3 elt list =[ nb AA bonds, nb BB bonds, nb AB bonds ] / nb of metal
# normalizedAA = 7 element list : the ith elt is the number of A with i A-neighbor
# (ie if there are 1/4 of the Mg surrounded by 3 Mg, normalizedAA[3] = 0.25)
# normalizedBB = idem for B
# Logically, if nbA = nbB, normalizedAA = normalizedBB
A = "Mg"
B = "Mn"
countAA = 0
countAB = 0
countBB = 0
neighborAA = [0 for i in range(7)]
neighborBB = [0 for i in range(7)]
A_indices = struc.indices_from_symbol(A)
B_indices = struc.indices_from_symbol(B)
nbA = len(A_indices)
nbB = len(B_indices)
nbTot = nbA + nbB
print("{0} {1}, {2} {3} : {4}".format(nbA, A, nbB, B, nbTot))
sCopy = RemoveSpeciesTransformation(["Na",
"O"]).apply_transformation(struc)
# struc.remove_species('O')
mini = MinimumDistanceNN(tol=0.3)
for i, site in enumerate(sCopy):
# print(str(i))
neigList = mini.get_nn_info(sCopy, i)
#print("{0} closest neighboors \n".format(len(neigList)))
coord = 0
for neighbor in neigList:
index = neighbor['site_index']
name = neighbor['site'].species_string
# print(" ( {0} at {1} ) ".format(name,index))
if site.species_string == A:
if name == A:
countAA += 1
coord += 1
if name == B:
countAB += 1
if site.species_string == B:
if name == A:
countAB += 1
if name == B:
countBB += 1
coord += 1
if site.species_string == A:
neighborAA[coord] += 1
elif site.species_string == B:
neighborBB[coord] += 1
bonds = [countAA // 2, countBB // 2, countAB // 2]
normalizedBonds = [i / nbTot for i in bonds]
print("[{3}{3}, {4}{4}, {3}{4}] = {0} normalized by {1} : {2} \n".format(
bonds, nbTot, normalizedBonds, A, B))
if nbA > 0:
normalizedAA = [i / nbA for i in neighborAA]
print(
"[Nombre de {0} avec [1-6] {0} autour] = {1} normalized by {2} : {3} \n"
.format(A, neighborAA, nbA, normalizedAA))
else:
normalizedAA = neighborAA
if nbB > 0:
normalizedBB = [i / nbB for i in neighborBB]
print(
"[Nombre de {0} avec [1-6] {0} autour] = {1} normalized by {2} : {3} \n"
.format(B, neighborBB, nbB, normalizedBB))
else:
normalizedBB = neighborBB
return [normalizedBonds, normalizedAA, normalizedBB]