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 solid_solution_sqs(structure, elem_frac_site, elem_frac_comp, sqs_scaling):
'''
Use pymatgen SQSTransformation (which call ATAT mcsqs program)
to generate disordered structure.
Args:
structure: pymatgen structure
elem_frac_site: the factional occupy site in the original structure, e.g. 'Ti'
elem_frac_comp: solid solution composition, e.g. 'Ti0.25Zr0.25Hf0.25Nb0.25'
sqs_scaling (int or list): (same as pymatgen scaling in SQSTransformation)
Scaling factor to determine supercell. Two options are possible:
a. (preferred) Scales number of atoms, e.g., for a structure with 8 atoms,
scaling=4 would lead to a 32 atom supercell
b. A sequence of three scaling factors, e.g., [2, 1, 1], which
specifies that the supercell should have dimensions 2a x b x c
Return:
pymatgen structure, SQS
'''
# build another pymatgen structure
structure[elem_frac_site] = elem_frac_comp
ts = TransformedStructure(structure, [])
# the directory must be set in SQSTransformation, otherwise the work dir
# will be changed by this function
workdir = os.getcwd()
ts.append_transformation(
SQSTransformation(scaling=sqs_scaling,
search_time=1,
directory=workdir,
reduction_algo=False))
return ts.structures[-1]
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_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_snl(self):
self.trans.set_parameter("author", "will")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([("will", "*****@*****.**")])
self.assertEqual(len(w), 1, "Warning not raised on type conversion " "with other_parameters")
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]["@class"], "SubstitutionTransformation")
h = ("testname", "testURL", {"test": "testing"})
snl = StructureNL(ts.final_structure, [("will", "*****@*****.**")], history=[h])
snl = TransformedStructure.from_snl(snl).to_snl([("notwill", "*****@*****.**")])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [("notwill", "*****@*****.**")])
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 run_task(self, fw_spec):
transformations = []
transformation_params = self.get(
"transformation_params",
[{} for i in range(len(self["transformations"]))])
for t in self["transformations"]:
for m in [
"advanced_transformations", "defect_transformations",
"site_transformations", "standard_transformations"
]:
mod = __import__("pymatgen.transformations." + m, globals(),
locals(), [t], -1)
try:
t_cls = getattr(mod, t)
except AttributeError:
continue
t_obj = t_cls(**transformation_params.pop(0))
transformations.append(t_obj)
structure = self['structure'] if 'prev_calc_dir' not in self else \
Poscar.from_file(os.path.join(self['prev_calc_dir'], 'POSCAR')).structure
ts = TransformedStructure(structure)
transmuter = StandardTransmuter([ts], transformations)
final_structure = transmuter.transformed_structures[
-1].final_structure.copy()
vis_orig = self["vasp_input_set"]
vis_dict = vis_orig.as_dict()
vis_dict["structure"] = final_structure.as_dict()
vis_dict.update(self.get("override_default_vasp_params", {}) or {})
vis = vis_orig.__class__.from_dict(vis_dict)
vis.write_input(".")
def __init__(self,
cif_string,
transformations=None,
primitive=True,
extend_collection=False):
"""
Generates a Transmuter from a cif string, possibly
containing multiple structures.
Args:
cif_string: A string containing a cif or a series of cifs
transformations: New transformations to be applied to all
structures
primitive: Whether to generate the primitive cell from the cif.
extend_collection: Whether to use more than one output structure
from one-to-many transformations. extend_collection can be a
number, which determines the maximum branching for each
transformation.
"""
transformed_structures = []
lines = cif_string.split("\n")
structure_data = []
read_data = False
for line in lines:
if re.match("^\s*data", line):
structure_data.append([])
read_data = True
if read_data:
structure_data[-1].append(line)
for data in structure_data:
tstruct = TransformedStructure.from_cif_string(
"\n".join(data), [], primitive)
transformed_structures.append(tstruct)
super(CifTransmuter, self).__init__(transformed_structures,
transformations, extend_collection)
def __init__(self, cif_string, transformations=None, primitive=True,
extend_collection=False):
"""
Generates a Transmuter from a cif string, possibly
containing multiple structures.
Args:
cif_string:
A string containing a cif or a series of cifs
transformations:
New transformations to be applied to all structures
primitive:
Whether to generate the primitive cell from the cif.
extend_collection:
Whether to use more than one output structure from one-to-many
transformations.
"""
transformed_structures = []
lines = cif_string.split("\n")
structure_data = []
read_data = False
for line in lines:
if re.match("^\s*data", line):
structure_data.append([])
read_data = True
if read_data:
structure_data[-1].append(line)
for data in structure_data:
tstruct = TransformedStructure.from_cif_string("\n".join(data), [],
primitive)
transformed_structures.append(tstruct)
StandardTransmuter.__init__(self, transformed_structures,
transformations, extend_collection)
def run_task(self, fw_spec):
transformations = []
transformation_params = self.get("transformation_params",
[{} for i in range(len(self["transformations"]))])
for t in self["transformations"]:
found = False
for m in ["advanced_transformations", "defect_transformations",
"site_transformations", "standard_transformations"]:
mod = import_module("pymatgen.transformations.{}".format(m))
try:
t_cls = getattr(mod, t)
except AttributeError:
continue
t_obj = t_cls(**transformation_params.pop(0))
transformations.append(t_obj)
found = True
if not found:
raise ValueError("Could not find transformation: {}".format(t))
# TODO: @matk86 - should prev_calc_dir use CONTCAR instead of POSCAR? Note that if
# current dir, maybe it is POSCAR indeed best ... -computron
structure = self['structure'] if not self.get('prev_calc_dir', None) else \
Poscar.from_file(os.path.join(self['prev_calc_dir'], 'POSCAR')).structure
ts = TransformedStructure(structure)
transmuter = StandardTransmuter([ts], transformations)
final_structure = transmuter.transformed_structures[-1].final_structure.copy()
vis_orig = self["vasp_input_set"]
vis_dict = vis_orig.as_dict()
vis_dict["structure"] = final_structure.as_dict()
vis_dict.update(self.get("override_default_vasp_params", {}) or {})
vis = vis_orig.__class__.from_dict(vis_dict)
vis.write_input(".")
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 convert_fmt(args):
iformat = args.input_format[0]
oformat = args.output_format[0]
filename = args.input_filename[0]
out_filename = args.output_filename[0]
try:
if iformat == "smart":
structure = read_structure(filename)
if iformat == "POSCAR":
p = Poscar.from_file(filename)
structure = p.structure
elif iformat == "CIF":
r = CifParser(filename)
structure = r.get_structures()[0]
elif iformat == "CSSR":
structure = Cssr.from_file(filename).structure
if oformat == "smart":
write_structure(structure, out_filename)
elif oformat == "POSCAR":
p = Poscar(structure)
p.write_file(out_filename)
elif oformat == "CIF":
w = CifWriter(structure)
w.write_file(out_filename)
elif oformat == "CSSR":
c = Cssr(structure)
c.write_file(out_filename)
elif oformat == "VASP":
input_set = MPVaspInputSet()
ts = TransformedStructure(structure, [],
history=[{
"source":
"file",
"datetime":
str(datetime.datetime.now()),
"original_file":
open(filename).read()
}])
ts.write_vasp_input(input_set, output_dir=out_filename)
elif oformat == "MITVASP":
input_set = MITVaspInputSet()
ts = TransformedStructure(structure, [],
history=[{
"source":
"file",
"datetime":
str(datetime.datetime.now()),
"original_file":
open(filename).read()
}])
ts.write_vasp_input(input_set, output_dir=out_filename)
except Exception as ex:
print "Error converting file. Are they in the right format?"
print str(ex)
def __init__(self,
poscar_string,
transformations=None,
extend_collection=False):
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
StandardTransmuter.__init__(self, [tstruct],
transformations,
extend_collection=extend_collection)
def test_snl(self):
self.trans.set_parameter('author', 'will')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([('will', '*****@*****.**')])
self.assertEqual(len(w), 1, 'Warning not raised on type conversion '
'with other_parameters')
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]['@class'], 'SubstitutionTransformation')
h = ('testname', 'testURL', {'test' : 'testing'})
snl = StructureNL(ts.final_structure,[('will', '*****@*****.**')],
history = [h])
snl = TransformedStructure.from_snl(snl).to_snl([('notwill',
'*****@*****.**')])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [('notwill', '*****@*****.**')])
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.set_parameter('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 __init__(self,
poscar_string,
transformations=None,
extend_collection=False):
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
super().__init__([tstruct],
transformations,
extend_collection=extend_collection)
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_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_snl(self):
self.trans.set_parameter("author", "will")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([("will", "*****@*****.**")])
self.assertEqual(
len(w),
1,
"Warning not raised on type conversion " "with other_parameters",
)
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]["@class"], "SubstitutionTransformation")
h = ("testname", "testURL", {"test": "testing"})
snl = StructureNL(ts.final_structure, [("will", "*****@*****.**")], history=[h])
snl = TransformedStructure.from_snl(snl).to_snl([("notwill", "*****@*****.**")])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [("notwill", "*****@*****.**")])
def test_undo_last_transformation_and_redo(self):
trans = []
trans.append(SubstitutionTransformation({"Li":"Na"}))
trans.append(SubstitutionTransformation({"Fe":"Mn"}))
ts = TransformedStructure(self.structure, trans)
self.assertEqual("NaMnPO4", ts.final_structure.composition.reduced_formula)
ts.undo_last_transformation()
self.assertEqual("NaFePO4", ts.final_structure.composition.reduced_formula)
ts.undo_last_transformation()
self.assertEqual("LiFePO4", ts.final_structure.composition.reduced_formula)
self.assertRaises(IndexError, ts.undo_last_transformation)
ts.redo_next_transformation()
self.assertEqual("NaFePO4", ts.final_structure.composition.reduced_formula)
ts.redo_next_transformation()
self.assertEqual("NaMnPO4", ts.final_structure.composition.reduced_formula)
self.assertRaises(IndexError, ts.redo_next_transformation)
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 convert_fmt(args):
iformat = args.input_format[0]
oformat = args.output_format[0]
filename = args.input_filename[0]
out_filename = args.output_filename[0]
try:
if iformat == "POSCAR":
p = Poscar.from_file(filename)
structure = p.structure
elif iformat == "CIF":
r = CifParser(filename)
structure = r.get_structures()[0]
elif iformat == "CONVENTIONAL_CIF":
r = CifParser(filename)
structure = r.get_structures(primitive=False)[0]
elif iformat == "CSSR":
structure = Cssr.from_file(filename).structure
else:
structure = Structure.from_file(filename)
if oformat == "smart":
structure.to(filename=out_filename)
elif oformat == "POSCAR":
p = Poscar(structure)
p.write_file(out_filename)
elif oformat == "CIF":
w = CifWriter(structure)
w.write_file(out_filename)
elif oformat == "CSSR":
c = Cssr(structure)
c.write_file(out_filename)
elif oformat == "VASP":
ts = TransformedStructure(
structure, [],
history=[{"source": "file",
"datetime": str(datetime.datetime.now()),
"original_file": open(filename).read()}])
ts.write_vasp_input(MPRelaxSet, output_dir=out_filename)
elif oformat == "MITVASP":
ts = TransformedStructure(
structure, [],
history=[{"source": "file",
"datetime": str(datetime.datetime.now()),
"original_file": open(filename).read()}])
ts.write_vasp_input(MITRelaxSet, output_dir=out_filename)
except Exception as ex:
print("Error converting file. Are they in the right format?")
print(str(ex))
def __init__(self, poscar_string, transformations=None, extend_collection=False):
"""
Args:
poscar_string: List of POSCAR strings
transformations: New transformations to be applied to all
structures.
extend_collection: Whether to use more than one output structure
from one-to-many transformations.
"""
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
super().__init__(
[tstruct], transformations, extend_collection=extend_collection
)
def run_task(self, fw_spec):
transformations = []
transformation_params = self.get(
"transformation_params",
[{} for _ in range(len(self["transformations"]))],
)
for t in self["transformations"]:
found = False
t_cls = None
for m in [
"advanced_transformations",
"defect_transformations",
"site_transformations",
"standard_transformations",
]:
mod = import_module(f"pymatgen.transformations.{m}")
try:
t_cls = getattr(mod, t)
found = True
continue
except AttributeError:
pass
if not found:
raise ValueError(f"Could not find transformation: {t}")
t_obj = t_cls(**transformation_params.pop(0))
transformations.append(t_obj)
# TODO: @matk86 - should prev_calc_dir use CONTCAR instead of POSCAR?
# Note that if current dir, maybe POSCAR is indeed best ... -computron
structure = (self["structure"] if not self.get("prev_calc_dir", None)
else Poscar.from_file(
os.path.join(self["prev_calc_dir"],
"POSCAR")).structure)
ts = TransformedStructure(structure)
transmuter = StandardTransmuter([ts], transformations)
final_structure = transmuter.transformed_structures[
-1].final_structure.copy()
vis_orig = self["vasp_input_set"]
vis_dict = vis_orig.as_dict()
vis_dict["structure"] = final_structure.as_dict()
vis_dict.update(self.get("override_default_vasp_params", {}) or {})
vis = vis_orig.__class__.from_dict(vis_dict)
potcar_spec = self.get("potcar_spec", False)
vis.write_input(".", potcar_spec=potcar_spec)
dumpfn(transmuter.transformed_structures[-1], "transformations.json")
def __init__(self, poscar_string, transformations=None,
extend_collection=False):
"""
Args:
poscar_string:
List of POSCAR strings
transformations:
New transformations to be applied to all structures.
extend_collection:
Whether to use more than one output structure from one-to-many
transformations.
"""
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
StandardTransmuter.__init__(self, [tstruct], transformations,
extend_collection=extend_collection)
def __init__(self,
queryengine,
criteria,
transformations,
extend_collection=0,
ncores=None):
"""Constructor.
Args:
queryengine:
QueryEngine object for database access
criteria:
A criteria to search on, which is passed to queryengine's
get_entries method.
transformations:
New transformations to be applied to all structures
extend_collection:
Whether to use more than one output structure from one-to-many
transformations. extend_collection can be a number, which
determines the maximum branching for each transformation.
ncores:
Number of cores to use for applying transformations.
Uses multiprocessing.Pool
"""
entries = queryengine.get_entries(criteria, inc_structure=True)
source = "{}:{}/{}/{}".format(queryengine.host, queryengine.port,
queryengine.database_name,
queryengine.collection_name)
def get_history(entry):
return [{
"source": source,
"criteria": criteria,
"entry": entry.as_dict(),
"datetime": datetime.datetime.utcnow()
}]
transformed_structures = [
TransformedStructure(entry.structure, [],
history=get_history(entry))
for entry in entries
]
StandardTransmuter.__init__(self,
transformed_structures,
transformations=transformations,
extend_collection=extend_collection,
ncores=ncores)
def _add_metadata(structure):
"""
For book-keeping, store useful metadata with the Structure
object for later database ingestion including workflow
version and a UUID for easier querying of all tasks generated
from the workflow.
Args:
structure: Structure
Returns: TransformedStructure
"""
# this could be further improved by storing full transformation
# history, but would require an improved transformation pipeline
return TransformedStructure(
structure, other_parameters={"wf_meta": self.wf_meta})
def convert_fmt(args):
iformat = args.input_format[0]
oformat = args.output_format[0]
filename = args.input_filename[0]
out_filename = args.output_filename[0]
try:
if iformat == "smart":
structure = read_structure(filename)
if iformat == "POSCAR":
p = Poscar.from_file(filename)
structure = p.structure
elif iformat == "CIF":
r = CifParser(filename)
structure = r.get_structures()[0]
elif iformat == "CSSR":
structure = Cssr.from_file(filename).structure
if oformat == "smart":
write_structure(structure, out_filename)
elif oformat == "POSCAR":
p = Poscar(structure)
p.write_file(out_filename)
elif oformat == "CIF":
w = CifWriter(structure)
w.write_file(out_filename)
elif oformat == "CSSR":
c = Cssr(structure)
c.write_file(out_filename)
elif oformat == "VASP":
input_set = MPVaspInputSet()
ts = TransformedStructure(
structure,
[],
history=[
{"source": "file", "datetime": str(datetime.datetime.now()), "original_file": open(filename).read()}
],
)
ts.write_vasp_input(input_set, output_dir=out_filename)
elif oformat == "MITVASP":
input_set = MITVaspInputSet()
ts = TransformedStructure(
structure,
[],
history=[
{"source": "file", "datetime": str(datetime.datetime.now()), "original_file": open(filename).read()}
],
)
ts.write_vasp_input(input_set, output_dir=out_filename)
except Exception as ex:
print "Error converting file. Are they in the right format?"
print str(ex)
def __init__(self, poscar_string, transformations=[], extend_collection=False):
"""
Generates a transmuter from a sequence of POSCARs.
Args:
poscar_string:
List of POSCAR strings
transformations:
New transformations to be applied to all structures.
primitive:
Whether to generate the primitive cell from the cif.
extend_collection:
Whether to use more than one output structure from one-to-many
transformations.
"""
transformed_structures = []
transformed_structures.append(TransformedStructure.from_poscar_string(poscar_string, []))
StandardTransmuter.__init__(self, transformed_structures, transformations, extend_collection=extend_collection)
def from_filenames(poscar_filenames, transformations=None, extend_collection=False):
"""
Convenient constructor to generates a POSCAR transmuter from a list of
POSCAR filenames.
Args:
poscar_filenames: List of POSCAR filenames
transformations: New transformations to be applied to all
structures.
extend_collection:
Same meaning as in __init__.
"""
tstructs = []
for filename in poscar_filenames:
with open(filename, "r") as f:
tstructs.append(TransformedStructure.from_poscar_string(f.read(), []))
return StandardTransmuter(
tstructs, transformations, extend_collection=extend_collection
)
def from_structures(structures, transformations=None, extend_collection=0):
"""
Alternative constructor from structures rather than
TransformedStructures.
Args:
structures: Sequence of structures
transformations: New transformations to be applied to all
structures
extend_collection: Whether to use more than one output structure
from one-to-many transformations. extend_collection can be a
number, which determines the maximum branching for each
transformation.
Returns:
StandardTransmuter
"""
tstruct = [TransformedStructure(s, []) for s in structures]
return StandardTransmuter(tstruct, transformations, extend_collection)
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 setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
class TransformedStructureTest(PymatgenTest):
def setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
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_append_filter(self):
f3 = ContainsSpecieFilter(['O2-'], strict_compare=True, AND=False)
self.trans.append_filter(f3)
def test_get_vasp_input(self):
SETTINGS["PMG_VASP_PSP_DIR"] = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "..", "..",
"test_files"))
potcar = self.trans.get_vasp_input(MPRelaxSet)['POTCAR']
self.assertEqual("Na_pv\nFe_pv\nP\nO",
"\n".join([p.symbol for p in potcar]))
self.assertEqual(len(self.trans.structures), 2)
def test_final_structure(self):
self.assertEqual(
"NaFePO4", self.trans.final_structure.composition.reduced_formula)
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_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_as_dict(self):
self.trans.set_parameter('author', 'will')
d = self.trans.as_dict()
self.assertIn('last_modified', d)
self.assertIn('history', d)
self.assertIn('version', d)
self.assertIn('author', d['other_parameters'])
self.assertEqual(Structure.from_dict(d).formula, 'Na4 Fe4 P4 O16')
def test_snl(self):
self.trans.set_parameter('author', 'will')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([('will', '*****@*****.**')])
self.assertEqual(
len(w), 1, 'Warning not raised on type conversion '
'with other_parameters')
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]['@class'],
'SubstitutionTransformation')
h = ('testname', 'testURL', {'test': 'testing'})
snl = StructureNL(ts.final_structure, [('will', '*****@*****.**')],
history=[h])
snl = TransformedStructure.from_snl(snl).to_snl([('notwill',
'*****@*****.**')])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [('notwill', '*****@*****.**')])
def prep(ctx, archive, authors):
"""prep structures from an archive for submission"""
run = ctx.obj["RUN"]
collections = ctx.obj["COLLECTIONS"]
snl_collection = ctx.obj["CLIENT"].db.snls
handler = ctx.obj["MONGO_HANDLER"]
nmax = ctx.obj["NMAX"]
skip = ctx.obj["SKIP"]
# TODO no_dupe_check flag
fname, ext = os.path.splitext(os.path.basename(archive))
tag, sec_ext = fname.rsplit(".", 1) if "." in fname else [fname, ""]
logger.info(click.style(f"tag: {tag}", fg="cyan"))
if sec_ext:
ext = "".join([sec_ext, ext])
exts = ["tar.gz", ".tgz", "bson.gz", ".zip"]
if ext not in exts:
raise EmmetCliError(
f"{ext} not supported (yet)! Please use one of {exts}.")
meta = {"authors": [Author.parse_author(a) for a in authors]}
references = meta.get("references", "").strip()
source_ids_scanned = handler.collection.distinct("source_id",
{"tags": tag})
# TODO add archive of StructureNL files
input_structures, source_total = [], None
if ext == "bson.gz":
input_bson = gzip.open(archive)
source_total = count_file_documents(input_bson)
for doc in bson.decode_file_iter(input_bson):
if len(input_structures) >= nmax:
break
if skip and doc["db_id"] in source_ids_scanned:
continue
elements = set([
specie["element"] for site in doc["structure"]["sites"]
for specie in site["species"]
])
for l in SETTINGS.skip_labels:
if l in elements:
logger.log(
logging.ERROR if run else logging.INFO,
f'Skip structure {doc["db_id"]}: unsupported element {l}!',
extra={
"tags": [tag],
"source_id": doc["db_id"]
},
)
break
else:
s = TransformedStructure.from_dict(doc["structure"])
s.source_id = doc["db_id"]
input_structures.append(s)
elif ext == ".zip":
input_zip = ZipFile(archive)
namelist = input_zip.namelist()
source_total = len(namelist)
for fname in namelist:
if len(input_structures) >= nmax:
break
if skip and fname in source_ids_scanned:
continue
contents = input_zip.read(fname)
fmt = get_format(fname)
s = Structure.from_str(contents, fmt=fmt)
s.source_id = fname
input_structures.append(s)
else:
tar = tarfile.open(archive, "r:gz")
members = tar.getmembers()
source_total = len(members)
for member in members:
if os.path.basename(member.name).startswith("."):
continue
if len(input_structures) >= nmax:
break
fname = member.name.lower()
if skip and fname in source_ids_scanned:
continue
f = tar.extractfile(member)
if f:
contents = f.read().decode("utf-8")
fmt = get_format(fname)
s = Structure.from_str(contents, fmt=fmt)
s.source_id = fname
input_structures.append(s)
total = len(input_structures)
logger.info(
f"{total} of {source_total} structure(s) loaded "
f"({len(source_ids_scanned)} unique structures already scanned).")
save_logs(ctx)
snls, index = [], None
for istruct in input_structures:
# number of log messages equals number of structures processed if --run
# only logger.warning goes to DB if --run
if run and len(handler.buffer) >= handler.buffer_size:
insert_snls(ctx, snls)
struct = (istruct.final_structure if isinstance(
istruct, TransformedStructure) else istruct)
struct.remove_oxidation_states()
struct = struct.get_primitive_structure()
formula = struct.composition.reduced_formula
sg = get_sg(struct)
if not (struct.is_ordered and struct.is_valid()):
logger.log(
logging.WARNING if run else logging.INFO,
f"Skip structure {istruct.source_id}: disordered or invalid!",
extra={
"formula": formula,
"spacegroup": sg,
"tags": [tag],
"source_id": istruct.source_id,
},
)
continue
for full_name, coll in collections.items():
# load canonical structures in collection for current formula and
# duplicate-check them against current structure
load_canonical_structures(ctx, full_name, formula)
for canonical_structure in canonical_structures[full_name][
formula].get(sg, []):
if structures_match(struct, canonical_structure):
logger.log(
logging.WARNING if run else logging.INFO,
f"Duplicate for {istruct.source_id} ({formula}/{sg}): {canonical_structure.id}",
extra={
"formula": formula,
"spacegroup": sg,
"tags": [tag],
"source_id": istruct.source_id,
"duplicate_dbname": full_name,
"duplicate_id": canonical_structure.id,
},
)
break
else:
continue # no duplicate found -> continue to next collection
break # duplicate found
else:
# no duplicates in any collection
prefix = snl_collection.database.name
if index is None:
# get start index for SNL id
snl_ids = snl_collection.distinct("snl_id")
index = max(
[int(snl_id[len(prefix) + 1:]) for snl_id in snl_ids])
index += 1
snl_id = "{}-{}".format(prefix, index)
kwargs = {"references": references, "projects": [tag]}
if isinstance(istruct, TransformedStructure):
snl = istruct.to_snl(meta["authors"], **kwargs)
else:
snl = StructureNL(istruct, meta["authors"], **kwargs)
snl_dct = snl.as_dict()
snl_dct.update(get_meta_from_structure(struct))
snl_dct["snl_id"] = snl_id
snls.append(snl_dct)
logger.log(
logging.WARNING if run else logging.INFO,
f"SNL {snl_id} created for {istruct.source_id} ({formula}/{sg})",
extra={
"formula": formula,
"spacegroup": sg,
"tags": [tag],
"source_id": istruct.source_id,
},
)
# final save
if run:
insert_snls(ctx, snls)
excluded_bonding_elements = args.exclude_bonding[0].split(',') if args.exclude_bonding else []
file_format = args.format
filename = args.input_file[0]
s = None
if filename.endswith(".cif"):
file_format = "cif"
elif filename.startswith("POSCAR"):
file_format = "poscar"
elif re.search('\.json', filename):
file_format = 'mpjson'
if file_format == 'poscar':
p = Poscar.from_file(filename)
s = p.struct
elif file_format == 'cif':
r = CifParser(filename)
s = r.get_structures(False)[0]
else:
d = json.load(file_open_zip_aware(filename))
ts = TransformedStructure.from_dict(d)
s = ts.final_structure
if s:
vis = StructureVis(excluded_bonding_elements=excluded_bonding_elements)
vis.set_structure(s)
vis.show()
def from_filenames(poscar_filenames, transformations=[], extend_collection=False):
transformed_structures = []
for filename in poscar_filenames:
with open(filename, "r") as f:
transformed_structures.append(TransformedStructure.from_poscar_string(f.read(), []))
return StandardTransmuter(transformed_structures, transformations, extend_collection=extend_collection)
def setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
def __init__(self, poscar_string, transformations=None,
extend_collection=False):
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
StandardTransmuter.__init__(self, [tstruct], transformations,
extend_collection=extend_collection)
def list_TransformedStructure(list_struc):
return [TransformedStructure(structure) for structure in list_struc]
class TransformedStructureTest(unittest.TestCase):
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_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_append_filter(self):
f3 = ContainsSpecieFilter(['O2-'], strict_compare=True, AND=False)
self.trans.append_filter(f3)
def test_get_vasp_input(self):
vaspis = MPVaspInputSet()
self.assertEqual(
"Na_pv\nO\nP\nFe_pv",
self.trans.get_vasp_input(vaspis, False)['POTCAR.spec'])
self.assertEqual(len(self.trans.structures), 2)
def test_final_structure(self):
self.assertEqual(
"NaFePO4", self.trans.final_structure.composition.reduced_formula)
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.set_parameter('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_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_to_dict(self):
d = self.trans.to_dict
self.assertIn('last_modified', d)
self.assertIn('history', d)
self.assertIn('version', d)
self.assertEqual(Structure.from_dict(d).formula, 'Na4 Fe4 P4 O16')
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 generate_ewald_orderings(path, choose_file, oxidation_states,
num_structures):
"""
DESCRIPTION: Given a disordered CIF structure with at least one crystallographic site that is shared by more than one element, all permutations
will have their electrostatic energy calculated via an Ewald summation, given that all ion charges are specified. Ordered CIF structures will
be generated, postpended with a number that indicates the stability ranking of the structure. For example, if a CIF file called
"Na2Mn2Fe(VO4)3.cif" is inputted with num_structures=3, then the function will generate 3 ordered output files,
"Na2Mn2Fe(VO4)3-ewald-1", "Na2Mn2Fe(VO4)3-ewald-2", and "Na2Mn2Fe(VO4)3-ewald-3", with "Na2Mn2Fe(VO4)3-ewald-1" being the most stable and
"Na2Mn2Fe(VO4)3-ewald-3" being the least stable. Note that this function does not take into account the symmetry of the crystal, and thus
it may give several structures which are symmetrically identical under a space group. Use "find_unique_structures" to isolate unique orderings.
PARAMETERS:
path: string
The file path to the CIF file. The CIF file must be a disordered structure, or else an error will occur. A disordered structure will have one
site that is occupied by more than one element, with occupancies less than 1: i.e. Fe at 0,0,0.5 with an occupancy of 0.75, and Mn at the same
site 0,0,0.5 with an occupancy of 0.25. This function cannot handle multivalent elements, for example it cannot handle a structure that has Mn
in both the 2+ and 3+ redox state.
choose_file: boolean
Setting this parameter to True brings up the file explorer dialog for the user to manually select the CIF file
oxidation_states: dictionary
A dictionary that maps each element in the structure to a particular oxidation state. E.g. {"Fe": 3, "Mn": 2, "O": -2, "V": 5, "Na": 1, "Al":3}
Make sure that all elements in the structure is assigned an oxidation state. It is ok to add more elements than needed.
num_structures: int
The number of strcutures to be outputted by the function. There can be hundreds or thousands of candidate structures, however in practice only
the first few (or even only the first, most stable) structures are needed.
RETURNS: None
"""
# open file dialog if file is to be manually chosen
if choose_file:
root = tk.Tk()
root.withdraw()
path = filedialog.askopenfilename()
#Read cif file
cryst = Structure.from_file(path)
analyzer = SpacegroupAnalyzer(cryst)
space_group = analyzer.get_space_group_symbol()
print(space_group)
symm_struct = analyzer.get_symmetrized_structure()
cryst = TransformedStructure(symm_struct)
#Create Oxidation State Transform
oxidation_transform = OxidationStateDecorationTransformation(
oxidation_states)
#Create Ewald Ordering Transform object which will be passed into the transmuter
ordering_transform = OrderDisorderedStructureTransformation(
symmetrized_structures=True)
# apply the order-disorder transform on the structure for any site that has fractional occupancies
transmuter = StandardTransmuter([cryst],
[oxidation_transform, ordering_transform],
extend_collection=num_structures)
print("Ewald optimization successful!")
num_structures = len(transmuter.transformed_structures)
for i in range(num_structures):
newCryst = transmuter.transformed_structures[i].final_structure
#Save to CIF
structure_name = os.path.splitext(os.path.basename(path))[0]
save_directory = structure_name
if not os.path.isdir(save_directory):
os.mkdir(save_directory)
filename = structure_name + '/' + structure_name + '-ewald' + '-%i' % (
i + 1)
w = CifWriter(newCryst)
w.write_file(filename + '.cif')
print("Cif file saved to {}.cif".format(filename))
#Save to POSCAR
poscar = Poscar(newCryst)
poscar.write_file(filename)
print("POSCAR file saved to {}".format(filename))
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
class TransformedStructureTest(unittest.TestCase):
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_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_append_filter(self):
f3 = ContainsSpecieFilter(["O2-"], strict_compare=True, AND=False)
self.trans.append_filter(f3)
def test_get_vasp_input(self):
SETTINGS["VASP_PSP_DIR"] = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "..", "..", "test_files")
)
potcar = self.trans.get_vasp_input(MPRelaxSet)["POTCAR"]
self.assertEqual("Na_pv\nFe_pv\nP\nO", "\n".join([p.symbol for p in potcar]))
self.assertEqual(len(self.trans.structures), 2)
def test_final_structure(self):
self.assertEqual("NaFePO4", self.trans.final_structure.composition.reduced_formula)
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_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_as_dict(self):
self.trans.set_parameter("author", "will")
d = self.trans.as_dict()
self.assertIn("last_modified", d)
self.assertIn("history", d)
self.assertIn("version", d)
self.assertIn("author", d["other_parameters"])
self.assertEqual(Structure.from_dict(d).formula, "Na4 Fe4 P4 O16")
def test_snl(self):
self.trans.set_parameter("author", "will")
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([("will", "*****@*****.**")])
self.assertEqual(len(w), 1, "Warning not raised on type conversion " "with other_parameters")
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]["@class"], "SubstitutionTransformation")
h = ("testname", "testURL", {"test": "testing"})
snl = StructureNL(ts.final_structure, [("will", "*****@*****.**")], history=[h])
snl = TransformedStructure.from_snl(snl).to_snl([("notwill", "*****@*****.**")])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [("notwill", "*****@*****.**")])
class TransformedStructureTest(unittest.TestCase):
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 = []
trans.append(SubstitutionTransformation({"Li": "Na"}))
self.trans = TransformedStructure(structure, trans)
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_append_filter(self):
f3 = ContainsSpecieFilter(['O2-'], strict_compare=True, AND=False)
self.trans.append_filter(f3)
def test_get_vasp_input(self):
vaspis = MaterialsProjectVaspInputSet()
self.assertEqual("Na_pv\nO\nP\nFe_pv",
self.trans.get_vasp_input(vaspis,
False)['POTCAR.spec'])
self.assertEqual(len(self.trans.structures), 2)
def test_final_structure(self):
self.assertEqual("NaFePO4", self.trans.final_structure.composition
.reduced_formula)
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.set_parameter('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_undo_and_redo_last_change(self):
trans = []
trans.append(SubstitutionTransformation({"Li": "Na"}))
trans.append(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_to_dict(self):
d = self.trans.to_dict
self.assertIn('last_modified', d)
self.assertIn('history', d)
self.assertIn('version', d)
self.assertEqual(Structure.from_dict(d).formula, 'Na4 Fe4 P4 O16')
def __init__(self, poscar_string, transformations=None,
extend_collection=False):
tstruct = TransformedStructure.from_poscar_string(poscar_string, [])
super(PoscarTransmuter, self).__init__([tstruct], transformations,
extend_collection=extend_collection)
class TransformedStructureTest(PymatgenTest):
def setUp(self):
structure = PymatgenTest.get_structure("LiFePO4")
self.structure = structure
trans = [SubstitutionTransformation({"Li": "Na"})]
self.trans = TransformedStructure(structure, trans)
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_append_filter(self):
f3 = ContainsSpecieFilter(['O2-'], strict_compare=True, AND=False)
self.trans.append_filter(f3)
def test_get_vasp_input(self):
SETTINGS["PMG_VASP_PSP_DIR"] = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "..", "..",
"test_files"))
potcar = self.trans.get_vasp_input(MPRelaxSet)['POTCAR']
self.assertEqual("Na_pv\nFe_pv\nP\nO",
"\n".join([p.symbol for p in potcar]))
self.assertEqual(len(self.trans.structures), 2)
def test_final_structure(self):
self.assertEqual("NaFePO4", self.trans.final_structure.composition
.reduced_formula)
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_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_as_dict(self):
self.trans.set_parameter('author', 'will')
d = self.trans.as_dict()
self.assertIn('last_modified', d)
self.assertIn('history', d)
self.assertIn('version', d)
self.assertIn('author', d['other_parameters'])
self.assertEqual(Structure.from_dict(d).formula, 'Na4 Fe4 P4 O16')
def test_snl(self):
self.trans.set_parameter('author', 'will')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
snl = self.trans.to_snl([('will', '*****@*****.**')])
self.assertEqual(len(w), 1, 'Warning not raised on type conversion '
'with other_parameters')
ts = TransformedStructure.from_snl(snl)
self.assertEqual(ts.history[-1]['@class'], 'SubstitutionTransformation')
h = ('testname', 'testURL', {'test' : 'testing'})
snl = StructureNL(ts.final_structure,[('will', '*****@*****.**')],
history = [h])
snl = TransformedStructure.from_snl(snl).to_snl([('notwill',
'*****@*****.**')])
self.assertEqual(snl.history, [h])
self.assertEqual(snl.authors, [('notwill', '*****@*****.**')])
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()
