def complete_ordering(self, structure, num_remove_dict):
self.logger.debug("Performing complete ordering...")
all_structures = []
from pymatgen.symmetry.finder import SymmetryFinder
symprec = 0.2
s = SymmetryFinder(structure, symprec=symprec)
self.logger.debug("Symmetry of structure is determined to be {}."
.format(s.get_spacegroup_symbol()))
sg = s.get_spacegroup()
tested_sites = []
starttime = time.time()
self.logger.debug("Performing initial ewald sum...")
ewaldsum = EwaldSummation(structure)
self.logger.debug("Ewald sum took {} seconds."
.format(time.time() - starttime))
starttime = time.time()
allcombis = []
for ind, num in num_remove_dict.items():
allcombis.append(itertools.combinations(ind, num))
count = 0
for allindices in itertools.product(*allcombis):
sites_to_remove = []
indices_list = []
for indices in allindices:
sites_to_remove.extend([structure[i] for i in indices])
indices_list.extend(indices)
mod = StructureEditor(structure)
mod.delete_sites(indices_list)
s_new = mod.modified_structure
energy = ewaldsum.compute_partial_energy(indices_list)
already_tested = False
for i, tsites in enumerate(tested_sites):
tenergy = all_structures[i]["energy"]
if abs((energy - tenergy) / len(s_new)) < 1e-5 and \
sg.are_symmetrically_equivalent(sites_to_remove, tsites,
symm_prec=symprec):
already_tested = True
if not already_tested:
tested_sites.append(sites_to_remove)
all_structures.append({"structure": s_new, "energy": energy})
count += 1
if count % 10 == 0:
timenow = time.time()
self.logger.debug("{} structures, {:.2f} seconds."
.format(count, timenow - starttime))
self.logger.debug("Average time per combi = {} seconds"
.format((timenow - starttime) / count))
self.logger.debug("{} symmetrically distinct structures found."
.format(len(all_structures)))
self.logger.debug("Total symmetrically distinct structures found = {}"
.format(len(all_structures)))
all_structures = sorted(all_structures, key=lambda s: s["energy"])
return all_structures
def fast_ordering(self, structure, num_remove_dict, num_to_return=1):
"""
This method uses the matrix form of ewaldsum to calculate the ewald
sums of the potential structures. This is on the order of 4 orders of
magnitude faster when there are large numbers of permutations to
consider. There are further optimizations possible (doing a smarter
search of permutations for example), but this wont make a difference
until the number of permutations is on the order of 30,000.
"""
self.logger.debug("Performing fast ordering")
starttime = time.time()
self.logger.debug("Performing initial ewald sum...")
ewaldmatrix = EwaldSummation(structure).total_energy_matrix
self.logger.debug("Ewald sum took {} seconds."
.format(time.time() - starttime))
starttime = time.time()
m_list = []
for indices, num in num_remove_dict.items():
m_list.append([0, num, list(indices), None])
self.logger.debug("Calling EwaldMinimizer...")
minimizer = EwaldMinimizer(ewaldmatrix, m_list, num_to_return,
PartialRemoveSitesTransformation.ALGO_FAST)
self.logger.debug("Minimizing Ewald took {} seconds."
.format(time.time() - starttime))
all_structures = []
lowest_energy = minimizer.output_lists[0][0]
num_atoms = sum(structure.composition.values())
for output in minimizer.output_lists:
se = StructureEditor(structure)
del_indices = []
for manipulation in output[1]:
if manipulation[1] is None:
del_indices.append(manipulation[0])
else:
se.replace_site(manipulation[0], manipulation[1])
se.delete_sites(del_indices)
struct = se.modified_structure.get_sorted_structure()
all_structures.append({"energy": output[0],
"energy_above_minimum": (output[0]
- lowest_energy)
/ num_atoms,
"structure": struct})
return all_structures
def best_first_ordering(self, structure, num_remove_dict):
self.logger.debug("Performing best first ordering")
starttime = time.time()
self.logger.debug("Performing initial ewald sum...")
ewaldsum = EwaldSummation(structure)
self.logger.debug("Ewald sum took {} seconds."
.format(time.time() - starttime))
starttime = time.time()
ematrix = ewaldsum.total_energy_matrix
to_delete = []
totalremovals = sum(num_remove_dict.values())
removed = {k: 0 for k in num_remove_dict.keys()}
for i in xrange(totalremovals):
maxindex = None
maxe = float("-inf")
maxindices = None
for indices in num_remove_dict.keys():
if removed[indices] < num_remove_dict[indices]:
for ind in indices:
if ind not in to_delete:
energy = sum(ematrix[:, ind]) + \
sum(ematrix[:, ind]) - ematrix[ind, ind]
if energy > maxe:
maxindex = ind
maxe = energy
maxindices = indices
removed[maxindices] += 1
to_delete.append(maxindex)
ematrix[:, maxindex] = 0
ematrix[maxindex, :] = 0
mod = StructureEditor(structure)
mod.delete_sites(to_delete)
self.logger.debug("Minimizing Ewald took {} seconds."
.format(time.time() - starttime))
return [{"energy": sum(sum(ematrix)),
"structure": mod.modified_structure.get_sorted_structure()}]
def apply_transformation(self, structure, return_ranked_list=False):
"""
For this transformation, the apply_transformation method will return
only the ordered structure with the lowest Ewald energy, to be
consistent with the method signature of the other transformations.
However, all structures are stored in the all_structures attribute in
the transformation object for easy access.
Args:
structure:
Oxidation state decorated disordered structure to order
return_ranked_list:
Boolean stating whether or not multiple structures are
returned. If return_ranked_list is a number, that number of
structures is returned.
Returns:
Depending on returned_ranked list, either a transformed structure
or
a list of dictionaries, where each dictionary is of the form
{"structure" = .... , "other_arguments"}
the key "transformation" is reserved for the transformation that
was actually applied to the structure.
This transformation is parsed by the alchemy classes for generating
a more specific transformation history. Any other information will
be stored in the transformation_parameters dictionary in the
transmuted structure class.
"""
try:
num_to_return = int(return_ranked_list)
except ValueError:
num_to_return = 1
num_to_return = max(1, num_to_return)
equivalent_sites = []
exemplars = []
#generate list of equivalent sites to order
#equivalency is determined by sp_and_occu and symmetry
#if symmetrized structure is true
for i, site in enumerate(structure):
if site.is_ordered:
continue
found = False
for j, ex in enumerate(exemplars):
sp = ex.species_and_occu
if not site.species_and_occu.almost_equals(sp):
continue
if self._symmetrized:
sym_equiv = structure.find_equivalent_sites(ex)
sym_test = site in sym_equiv
else:
sym_test = True
if sym_test:
equivalent_sites[j].append(i)
found = True
if not found:
equivalent_sites.append([i])
exemplars.append(site)
#generate the list of manipulations and input structure
se = StructureEditor(structure)
m_list = []
for g in equivalent_sites:
total_occupancy = sum([structure[i].species_and_occu for i in g],
Composition())
total_occupancy = dict(total_occupancy.items())
#round total occupancy to possible values
for k, v in total_occupancy.items():
if abs(v - round(v)) > 0.25:
raise ValueError("Occupancy fractions not consistent "
"with size of unit cell")
total_occupancy[k] = int(round(v))
#start with an ordered structure
initial_sp = max(total_occupancy.keys(),
key=lambda x: abs(x.oxi_state))
for i in g:
se.replace_site(i, initial_sp)
#determine the manipulations
for k, v in total_occupancy.items():
if k == initial_sp:
continue
m = [k.oxi_state / initial_sp.oxi_state, v, list(g), k]
m_list.append(m)
#determine the number of empty sites
empty = len(g) - sum(total_occupancy.values())
if empty > 0.5:
m_list.append([0, empty, list(g), None])
structure = se.modified_structure
matrix = EwaldSummation(structure).total_energy_matrix
ewald_m = EwaldMinimizer(matrix, m_list, num_to_return, self._algo)
self._all_structures = []
lowest_energy = ewald_m.output_lists[0][0]
num_atoms = sum(structure.composition.values())
for output in ewald_m.output_lists:
se = StructureEditor(structure)
# do deletions afterwards because they screw up the indices of the
# structure
del_indices = []
for manipulation in output[1]:
if manipulation[1] is None:
del_indices.append(manipulation[0])
else:
se.replace_site(manipulation[0], manipulation[1])
se.delete_sites(del_indices)
self._all_structures.append(
{"energy": output[0],
"energy_above_minimum":
(output[0] - lowest_energy) / num_atoms,
"structure": se.modified_structure.get_sorted_structure()})
if return_ranked_list:
return self._all_structures
else:
return self._all_structures[0]["structure"]
def apply_transformation(self, structure):
editor = StructureEditor(structure)
editor.delete_sites(self._indices)
return editor.modified_structure
def apply_transformation(self, structure, return_ranked_list=False):
"""
For this transformation, the apply_transformation method will return
only the ordered structure with the lowest Ewald energy, to be
consistent with the method signature of the other transformations.
However, all structures are stored in the all_structures attribute in
the transformation object for easy access.
Args:
structure:
Oxidation state decorated disordered structure to order
return_ranked_list:
Boolean stating whether or not multiple structures are
returned. If return_ranked_list is a number, that number of
structures is returned.
Returns:
Depending on returned_ranked list, either a transformed structure
or
a list of dictionaries, where each dictionary is of the form
{'structure' = .... , 'other_arguments'}
the key 'transformation' is reserved for the transformation that
was actually applied to the structure.
This transformation is parsed by the alchemy classes for generating
a more specific transformation history. Any other information will
be stored in the transformation_parameters dictionary in the
transmuted structure class.
"""
ordered_sites = []
sites_to_order = {}
try:
num_to_return = int(return_ranked_list)
except:
num_to_return = 1
num_to_return = max(1, num_to_return)
sites = list(structure.sites)
for i in range(len(structure)):
site = sites[i]
if sum(site.species_and_occu.values()) == 1 and len(site.species_and_occu) == 1:
ordered_sites.append(site)
else:
species = tuple([sp for sp, occu in site.species_and_occu.items()])
#group the sites by the list of species on that site
for sp, occu in site.species_and_occu.items():
if species not in sites_to_order:
sites_to_order[species] = {}
if sp not in sites_to_order[species]:
sites_to_order[species][sp] = [[occu, i]]
else:
sites_to_order[species][sp].append([occu, i])
total_occu = sum(site.species_and_occu.values())
#if the total occupancy on a site is less than one, add
#a list with None as the species (for removal)
if total_occu < 1:
if None not in sites_to_order[species]:
sites_to_order[species][None] = [[1 - total_occu, i]]
else:
sites_to_order[species][None].append([1 - total_occu, i])
"""
Create a list of [multiplication fraction, number of replacements,
[indices], replacement species]
"""
m_list = []
se = StructureEditor(structure)
for species in sites_to_order.values():
initial_sp = None
sorted_keys = sorted(species.keys(), key=lambda x: x is not None and -abs(x.oxi_state) or 1000)
for sp in sorted_keys:
if initial_sp is None:
initial_sp = sp
for site in species[sp]:
se.replace_site(site[1], initial_sp)
else:
if sp is None:
oxi = 0
else:
oxi = float(sp.oxi_state)
manipulation = [oxi / initial_sp.oxi_state, 0, [], sp]
site_list = species[sp]
site_list.sort(key=itemgetter(0))
prev_fraction = site_list[0][0]
for site in site_list:
if site[0] - prev_fraction > .1:
"""
tolerance for creating a new group of sites.
if site occupancies are similar, they will be put
in a group where the fraction has to be consistent
over the whole.
"""
manipulation[1] = int(round(manipulation[1]))
m_list.append(manipulation)
manipulation = [oxi / initial_sp.oxi_state, 0, [], sp]
prev_fraction = site[0]
manipulation[1] += site[0]
manipulation[2].append(site[1])
if abs(manipulation[1] - round(manipulation[1])) > .25: #if the # of atoms to remove isn't within .25 of an integer
raise ValueError('Occupancy fractions not consistent with size of unit cell')
manipulation[1] = int(round(manipulation[1]))
m_list.append(manipulation)
structure = se.modified_structure
matrix = EwaldSummation(structure).total_energy_matrix
ewald_m = EwaldMinimizer(matrix, m_list, num_to_return, self._algo)
self._all_structures = []
lowest_energy = ewald_m.output_lists[0][0]
num_atoms = sum(structure.composition.values())
for output in ewald_m.output_lists:
se = StructureEditor(structure)
del_indices = [] #do deletions afterwards because they screw up the indices of the structure
for manipulation in output[1]:
if manipulation[1] is None:
del_indices.append(manipulation[0])
else:
se.replace_site(manipulation[0], manipulation[1])
se.delete_sites(del_indices)
self._all_structures.append({'energy':output[0], 'energy_above_minimum':(output[0] - lowest_energy) / num_atoms, 'structure': se.modified_structure.get_sorted_structure()})
if return_ranked_list:
return self._all_structures
else:
return self._all_structures[0]['structure']
