def prepare_for_md(dir):
'''
Filters out cif files with disorder.
Creates new directories:
./md-ready
./disordered
'''
if not dir.endswith('/'):
dir+='/'
files = get_cif_files(dir)
ordered, disordered = [], []
for file in files:
s = mg.read_structure(dir+file)
if s.is_ordered:
ordered.append(file)
else:
disordered.append(file)
for subd in ['md-ready', 'disordered']:
# This creates subdirectories if not already present
if not os.path.isdir('{0}/{1}'.format(dir, subd)):
os.makedirs('{0}/{1}'.format(dir, subd))
for file in ordered:
shutil.copy(dir + file, dir + 'md-ready/')
for file in disordered:
shutil.copy(dir + file, dir + 'disordered/')
return
def csvbuilder(target_dir, output, column_functions, nary=10):
"""
Build a csv from structure files.
Keyword arguments:
target_dir -- string of path to look for structure files
output -- filename for output csv
column_functions -- list of function that build the csv. Each function should take a pymatgen Structure as a parmeter and return a single numeric or categorical (string or character) value
nary -- limit to n-ary structures (e.g. nary = 2 only binary and elemental, nary = 3 only ternary, binary, and elemental, etc.)
"""
structure_files = os.listdir(target_dir)
with open(output, 'wb') as csvfile:
next_row = 0
structurewriter = csv.writer(csvfile)
for structure_filename in structure_files:
a = pm.read_structure(os.path.join(target_dir, structure_filename))
if next_row > 0 and numberOfSpecies(a) < nary:
row = [structure_filename] + [f(a) for f in column_functions]
structurewriter.writerow(row)
next_row += 1
elif numberOfSpecies(a) < nary:
colnames = ['fileNames'] + [f.func_name for f in column_functions]
structurewriter.writerow(colnames)
row = [structure_filename] + [f(a) for f in column_functions]
structurewriter.writerow(row)
next_row += 2
def coordinationcsv(element, target_dir, output, column_functions):
"""
Build a coordination csv from structure files. Writes a csv file, one line per structure, containing the average coordination value for 'element' in that sturcture. Any other data columns can be included, per csvbuilder
Keyword arguments:
element -- string for the element symbol
target_dir -- string of path to look for structure files
output -- filename for output csv
column_functions -- list of function that build the csv. Each function should take a pymatgen Structure as a parmeter and return a single numeric or categorical (string or character) value
"""
structure_files = os.listdir(target_dir)
with open(output, 'wb') as csvfile:
next_row = 0
structurewriter = csv.writer(csvfile)
for structure_filename in structure_files:
a = pm.read_structure(os.path.join(target_dir, structure_filename))
elements = [x.symbol for x in a.species]
coordination = a.site_properties['coordination_no']
if next_row > 0 and element in elements:
avg_coordination = np.mean([x[1] for x in zip(elements, coordination) if x[0]==element])
row = [f(a) for f in column_functions] + [avg_coordination]
structurewriter.writerow(row)
next_row += 1
elif element in elements:
avg_coordination = np.mean([x[1] for x in zip(elements, coordination) if x[0]==element])
colnames = [f.func_name for f in column_functions] + ['avgCoordination']
structurewriter.writerow(colnames)
row = [f(a) for f in column_functions] + [avg_coordination]
structurewriter.writerow(row)
next_row += 2
def get_structure(self, directory):
"""Returns the final structure from an optimization"""
abspath = '%s/%s/' % (self.directory, directory)
if ('vasprun.xml' in os.listdir(abspath)):
return Vasprun('%s/vasprun.xml' % abspath).final_structure
elif ('CONTCAR' in os.listdir(abspath)):
return pmg.read_structure('%s/CONTCAR' % abspath)
def get_structure(self, directory):
"""Returns the final structure from an optimization"""
abspath = '%s/%s/' % (self.directory, directory)
if ('vasprun.xml' in os.listdir(abspath)):
return Vasprun('%s/vasprun.xml' % abspath).final_structure
elif ('CONTCAR' in os.listdir(abspath)):
return pmg.read_structure('%s/CONTCAR' % abspath)
def read(inp):
my_struct = {}
struct = mg.read_structure(inp)
for i in range(len(struct)):
try: my_struct[str(struct[i].specie)]
except: my_struct[str(struct[i].specie)] = [] # New element specie added
my_struct[str(struct[i].specie)].append([struct[i].a,struct[i].b,struct[i].c])
return my_struct
def read_CARs(self,GGA,HSE):
perfect = defaultdict(defaultdict)
defect = defaultdict(lambda:defaultdict(defaultdict))
PA = dict()
CARs = os.listdir('.')
perfect_tag = self.inputs['perfect']
defect_tag = self.inputs['defect']
for i in range(len(CARs)):
keywords = CARs[i].split('_')
if not 'CAR' in CARs[i]: continue
if 'HSE' in CARs[i]:
size = keywords[1]+'_HSE'
elif keywords[1] in ' '.join(HSE['size']):
size = keywords[1]+'_GGA'
else: size = keywords[1]
if keywords[0]==perfect_tag and keywords[-1]=='OSZICAR':
perfect[size]['energy'] = float(open(CARs[i]).readlines()[-1].split('E0=')[1].split()[0])
elif keywords[0]==defect_tag and keywords[-1]=='OSZICAR':
defect[keywords[2]][keywords[3]][size] = float(open(CARs[i]).readlines()[-1].split('E0=')[1].split()[0])
elif keywords[0]==perfect_tag and keywords[-1]=='OUTCAR':
PA[size] = self.pa.read_outcar(CARs[i])
elif keywords[0]==defect_tag and keywords[-1]=='OUTCAR':
PA['%s_%s_%s'%(keywords[2],keywords[3],size)] = self.pa.read_outcar(CARs[i])
elif keywords[0]==perfect_tag and (keywords[-1]=='POSCAR' or keywords[-1]=='CONTCAR'):
shutil.copy(CARs[i],'POSCAR')
Ele = pmg.read_structure('POSCAR').species
ele = dict()
for k in range(len(Ele)):
if not Ele[k] in ele.keys():
ele[Ele[k]] = 1
else: ele[Ele[k]] += 1
perfect[size]['ele'] = ele
elif keywords[0]==defect_tag and (keywords[-1]=='POSCAR' or keywords[-1]=='CONTCAR'):
shutil.copy(CARs[i],'POSCAR')
Ele = pmg.read_structure('POSCAR').species
ele = dict()
os.system('rm POSCAR')
for k in range(len(Ele)):
if not Ele[k] in ele.keys():
ele[Ele[k]] = 1
else: ele[Ele[k]] += 1
defect[keywords[2]]['ele'][size] = ele
return [perfect,defect,PA]
def get_latt(self,model,latt):
"""Obtaining lattice information from the POSCAR.
Args:
types <dict>: type of frequency model, types={'types':<int>}
lattice <str>: specific name of the POSCAR given by the user
"""
data={'a':0,'c':0,'No.':0}
os.system('cp '+latt+'_POSCAR POSCAR')
struct = mg.read_structure('POSCAR')
os.system('rm POSCAR')
reduced = mg.symmetry.finder.SymmetryFinder(struct,0.001).get_primitive_standard_structure()
data['No.'] = len(struct)
if model==5:
data['a'] = reduced.lattice.abc[0]*np.sqrt(2)*10**(-8)
if model==14:
data['a'] = reduced.lattice.abc[0]*np.sqrt(2)*10**(-8)
if model==8:
data['a'] = reduced.lattice.abc[0]*10**(-8)
data['c'] = reduced.lattice.abc[2]*10**(-8)
return data
def cif2cssr(cif, outfile = None, remove = ['Li+']):
'''
Converts cif files to Zeo++ CSSR files, deletes species specified in remove.
Must have pymatgen installed. Structure must be ordered and oxidation state decorated
'''
filename = cif.rsplit('.',1)[0]
s = mg.read_structure(cif)
if remove != None:
s.remove_species(remove)
if outfile == None:
outfile = filename + '.cssr'
try:
cssr = ZeoCssr(s)
cssr.write_file(outfile)
except:
cssr = None
return cssr
def corrctReflection(relax,defect):
# Correct periodically reflected atoms according to the perfect cell.
# relax is the CONTCAR (after relaxation) and defect is the POSCAR (before relaxation).
shift = array([0,0,0])
for ele in relax.keys():
for i in range(len(relax[ele])):
for j in range(3):
shift[j] = shift[j] + relax[ele][i][j] - defect[ele][i][j]
shift = shift/len(mg.read_structure('POSCAR_defect'))
for ele in relax.keys():
for i in range(len(relax[ele])):
for j in range(3):
relax[ele][i][j]=relax[ele][i][j]-shift[j]
if abs(relax[ele][i][j]-defect[ele][i][j])>abs(relax[ele][i][j]+defect[ele][i][j]-1):
if abs(relax[ele][i][j]-defect[ele][i][j]-1)<abs(relax[ele][i][j]-defect[ele][i][j]+1):
relax[ele][i][j] = relax[ele][i][j] - 1
else:
relax[ele][i][j] = relax[ele][i][j] + 1
return relax
def make_super(size,filename):
shutil.copyfile('POSCAR_primitive', filename)
struct = mg.read_structure(filename)
Structure.make_supercell(struct,size)
mg.write_structure(struct,filename)
def loop_structures(self, mode='i'):
"""
reading the structures specified in spec, add special points, and excecute the specs
mode:
i: loop structures for input generation
o: loop structures for output parsing
w: print all results
"""
print('loop structures mode ', mode)
mp_key = os.environ['MP_KEY']
mp_list_vasp = ['mp-149', 'mp-2534', 'mp-8062', 'mp-2469', 'mp-1550', 'mp-830', 'mp-1986', 'mp-10695', 'mp-66',
'mp-1639', 'mp-1265', 'mp-1138', 'mp-23155', 'mp-111']
if self.data['source'] == 'mp-vasp':
items_list = mp_list_vasp
elif self.data['source'] in ['poscar', 'cif']:
files = os.listdir('.')
items_list = files
elif self.data['source'] == 'mar_exp':
items_list = []
local_serv = pymongo.Connection("marilyn.pcpm.ucl.ac.be")
local_db_gaps = local_serv.band_gaps
pwd = os.environ['MAR_PAS']
local_db_gaps.authenticate("setten", pwd)
for c in local_db_gaps.exp.find():
name = Structure.from_dict(c['icsd_data']['structure']).composition.reduced_formula, c['icsd_id'],\
c['MP_id']
print(name)
#Structure.from_dict(c['icsd_data']['structure']).to(fmt='cif',filename=name)
items_list.append({'name': 'mp-' + c['MP_id'], 'icsd': c['icsd_id'], 'mp': c['MP_id']})
else:
items_list = [line.strip() for line in open(self.data['source'])]
for item in items_list:
print('\n')
# special case, this should be encaptulated
if self.data['source'] == 'mar_exp':
print('structure from marilyn', item['name'], item['icsd'], item['mp'])
exp = local_db_gaps.exp.find({'MP_id': item['mp']})[0]
structure = Structure.from_dict(exp['icsd_data']['structure'])
structure = refine_structure(structure)
structure.to(fmt='cif', filename=item['name'])
try:
kpts = local_db_gaps.GGA_BS.find({'transformations.history.0.id': item['icsd']})[0]['calculations']\
[-1]['band_structure']['kpoints']
except (IndexError, KeyError):
kpts = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
structure.kpts = kpts
print('kpoints:', structure.kpts[0], structure.kpts[1])
structure.item = item['name']
else:
if item.startswith('POSCAR_'):
structure = pmg.read_structure(item)
comment = Poscar.from_file(item).comment
# print comment
if comment.startswith("gap"):
structure.vbm_l = comment.split(" ")[1]
structure.vbm = (comment.split(" ")[2], comment.split(" ")[3], comment.split(" ")[4])
structure.cbm_l = comment.split(" ")[5]
structure.cbm = (comment.split(" ")[6], comment.split(" ")[7], comment.split(" ")[8])
else:
# print "no bandstructure information available, adding GG as 'gap'"
structure = add_gg_gap(structure)
elif 'xyz' in item:
structure = pmg.read_structure(item)
raise NotImplementedError
elif item.startswith('mp-'):
with MPRester(mp_key) as mp_database:
print('structure from mp database', item)
structure = mp_database.get_structure_by_material_id(item, final=True)
try:
bandstructure = mp_database.get_bandstructure_by_material_id(item)
structure.vbm_l = bandstructure.kpoints[bandstructure.get_vbm()['kpoint_index'][0]].label
structure.cbm_l = bandstructure.kpoints[bandstructure.get_cbm()['kpoint_index'][0]].label
structure.cbm = tuple(bandstructure.kpoints[bandstructure.get_cbm()['kpoint_index'][0]].frac_coords)
structure.vbm = tuple(bandstructure.kpoints[bandstructure.get_vbm()['kpoint_index'][0]].frac_coords)
except (MPRestError, IndexError, KeyError) as err:
print(err.message)
structure = add_gg_gap(structure)
else:
continue
structure.kpts = [list(structure.cbm), list(structure.vbm)]
structure.item = item
print(item, s_name(structure))
if mode == 'i':
self.excecute_flow(structure)
elif mode == 'w':
self.print_results(structure)
elif mode == 's':
self.insert_in_database(structure)
elif mode == 'o':
# if os.path.isdir(s_name(structure)) or os.path.isdir(s_name(structure)+'.conv'):
self.process_data(structure)
if 'ceci' in self.data['mode'] and mode == 'i':
os.chmod("job_collection", stat.S_IRWXU)
for i in range(len(relax)):
r_coords = relax[i].frac_coords
index = find_atom(r_coords,supercell)
supercell.replace(index,supercell[index].specie,r_coords)
if __name__=='__main__':
center = [0.0, 0.0, 0.0]
argv = sys.argv[1:]
opts, args = getopt.getopt(argv,"s")
Size = int(args[0])
size = int(args[1])
shift_vector = get_shift_vector(center,size,size)
Shift_Vector = get_shift_vector(center,size,Size)
supercell = mg.read_structure('POSCAR_super')
shift_coords(supercell,Shift_Vector)
relax = mg.read_structure('POSCAR_relax')
shift_coords(relax,shift_vector)
defect = mg.read_structure('POSCAR_defect')
shift_coords(defect,shift_vector)
compare_coords(relax,defect,supercell,size,Size)
rescale(relax,size,Size)
replace(supercell,relax)
shift_coords(supercell,-Shift_Vector)
mg.write_structure(supercell,'POSCAR_final')
def prepare_for_zeo(dir, remove_duplicates = False, separator = None):
'''
Takes a directory of cif files and filters zeo++ usable files. Requires pymatgen.
Creates new directories:
./ready - oxidation state decorated files for zeo++ to use.
./no-rad - structures with species having no corresponding ionic radii in pymatgen database
./fails - structures which cannot be assigned oxidation states
If remove_duplicates is set to true, it runs the files through remove_duplicates. Files pulled out of the database are of the format <id><speparator><formula>. The separator is usually '_' or '-'.
'''
# TODO maybe let the user specify the location of the new directories
files = [file for file in os.listdir(dir) if file.endswith('cif')]
d = {} # Dictionary of the form d[<filename>]['struct'], d[<filename>]['mass'], d[<filename>]['radius']
fails, no_rad = [], []
for file in files:
d[file] = {}
# reading to pymatgen structure object
s = mg.read_structure('{1}/{0}'.format(file, dir))
# AutoOxidationStateDecorationObject
ox = oxi()
try:
# Oxidation state decorated structure
s_ox = ox.apply_transformation(s)
# Saving structure to dictionary
d[file]['struct'] = s_ox
# List of unique elements in the structure
species = set(s_ox.species)
radii = dict((str(sp), float(sp.ionic_radius)) for sp in species)
masses = dict((str(sp), float(sp.atomic_mass)) for sp in species)
d[file]['radii'] = radii
d[file]['masses'] = masses
for sp in species:
if sp.ionic_radius == None:
# These are charge decorated files which have an assigned oxidation state but no radius in pymatgen corresponding to that state
# These files will have to be analyzed later, possibly using the crystal radius from the shannon table
no_rad.append(file)
break
except:
# These are files that cannot be assigned oxidation states - bond valence fails either due to disorder or is unable to find a charge neutral state
fails.append(file)
d[file]['struct'] = s
d[file]['radii'] = None
d[file]['masses'] = None
# This is a list of usable files for zeo++
ready = list(set(files).difference(set(no_rad)).difference(set(fails)))
for subd in ['zeo-ready', 'zeo-no-rad', 'zeo-fails']:
# This creates subdirectories if not already present
if not os.path.isdir('{0}/{1}'.format(dir, subd)):
os.makedirs('{0}/{1}'.format(dir, subd))
if remove_duplicates:
d_ready = remove_duplicates(ready, separator)
ready = [d_ready[key] for key in d_ready]
d_no_rad = remove_duplicates(no_rad, separator)
no_rad = [d_no_rad[key] for key in d_no_rad]
d_fails = remove_duplicates(fails, separator)
fails = [d_fails[key] for key in d_fails]
# Writing files into respective sub-directories
for file in ready:
mg.write_structure(d[file]['struct'], '{0}/zeo-ready/{1}'.format(dir, file))
# write the radius and the mass files also
filename = file.rsplit('.cif',1)[0]
write_rad_file(d[file]['radii'],'{0}/zeo-ready'.format(dir), filename)
write_mass_file(d[file]['masses'],'{0}/zeo-ready'.format(dir), filename)
for file in no_rad:
# These files do not have a radius for atleast one of the species
mg.write_structure(d[file]['struct'], '{0}/zeo-no-rad/{1}'.format(dir,file))
for file in fails:
# Note: these files are not charge decorated and are simply the original cif files rewritten
mg.write_structure(d[file]['struct'], '{0}/zeo-fails/{1}'.format(dir,file))
return len(ready), len(no_rad), len(fails)
def main(filename = 'mp-1368.mson', beta = 1.0, initial_temp = 1.0, precomputed_structure = False):
def temperature(phi_, beta_, initial_temp_):
#cooling profile. returns a temperature according to initial_temp*exp(-beta*phi), where phi is the current packing fraction, and beta is a free parameter
return initial_temp_*np.exp(-beta_*phi_)
#Filename of starting structure
#Cutoff for tetrahedron distortion -- higher numbers will accept more distorted tetrahedra
std_dev_cutoff = 0.50
#Initial factor for increasing all axes
initial_increase_factor = 2.5
#Compression increment -- compress by fixed percentage:
compression_factor = -0.01
#Tetrahedra become distorted due compounding numerical error. Re-regularize every n steps:
normalization_frequency = 1
#Save structure every n steps:
save_frequency = 1
#Controls how much tetrahedra can jostle during packing
temp = 1.
#How many tries to fit a tetrahedra before skipping
resolution_max = 5000
#How far a tet can travel randomly
distance_max = 1.0
#Initialize sturcture and tetrahedra
print '\nLoading initial structure...',
sys.stdout.flush()
initial_structure = pm.read_structure(filename)
if not precomputed_structure:
path = initial_structure.composition.alphabetical_formula.replace(' ', '') + '_beta_' + str(beta) + '_T_' + str(initial_temp)
if not os.path.exists(path):
os.mkdir(path)
print initial_structure.composition.alphabetical_formula + ' loaded.'
print '\nExtracting tetrahedra...',
sys.stdout.flush()
tet_str, tet_reg = tetpack.tetrahedra_from_structure(initial_structure, stdcutoff=std_dev_cutoff)
print str(len(tet_reg)) + ' initial tetrahedra extracted.'
#Expand structure initally
print '\nExpanding cell axes by factor of ' + str(initial_increase_factor) + '...',
sys.stdout.flush()
current_tet_str = tetpack.adjust_axes(tet_str, initial_increase_factor)
current_tet_reg = map(tetpack.tetrahedron, [current_tet_str[5*i:5*i+5] for i in range(len(current_tet_str)/5)])
print 'done: \na = ' + str(current_tet_str.lattice.a) + '\nb = ' + str(current_tet_str.lattice.b) + '\nc = ' + str(current_tet_str.lattice.c)
phi = tetpack.packing_density(current_tet_str)
print '\nRelaxing structure via Ewald summation...'
sys.stdout.flush()
current_tet_str = tetpack.ewald_relaxation(current_tet_str, max_steps = 1, motion_factor = temperature(phi, beta, initial_temp))
else:
path = filename.rstrip('.mson') + '_beta_' + str(beta) + '_T_' + str(initial_temp)
if not os.path.exists(path):
os.mkdir(path)
current_tet_str = initial_structure
current_tet_reg = map(tetpack.tetrahedron, [current_tet_str[5*i:5*i+5] for i in range(len(current_tet_str)/5)])
print '\nBeginning compression loop:'
#Loop until collision
collision = False
step = 0
while(not collision):
phi = tetpack.packing_density(current_tet_str)
if np.mod(step, normalization_frequency) == 0:
print 'Normalizing tetrahedra...',
sys.stdout.flush()
[tet.regularize() for tet in current_tet_reg]
print 'done.'
current_tet_str, current_tet_reg = compress(current_tet_str, current_tet_reg, temperature(phi, beta, initial_temp)*compression_factor)
print 'Step '+ str(step) + ' packing fraction: ' + str(phi) + ' T:' + str(temperature(phi, beta, initial_temp)) + '...',
sys.stdout.flush()
failed = check_and_resolve_collisions(current_tet_str, current_tet_reg, temperature(phi, beta, initial_temp), distance_max, int(1./temperature(phi, beta, initial_temp)*resolution_max))
if failed:
print 'Relaxing structure...',
sys.stdout.flush()
current_tet_str, current_tet_reg = compress(current_tet_str, current_tet_reg, -1.5* temperature(phi, beta, initial_temp)* compression_factor)
phi = tetpack.packing_density(current_tet_str)
print 'done. Packing fraction: ' + str(phi)
print 'Single-step Ewald relaxation...'
sys.stdout.flush()
current_tet_str = tetpack.ewald_relaxation(current_tet_str, max_steps = 1, motion_factor = temperature(phi, beta, initial_temp))
print 'done.'
failed = False
else:
if np.mod(step, save_frequency) == 0:
print 'Writing structure...',
sys.stdout.flush()
pm.write_structure(current_tet_str, os.path.join(path, str(step) + '.cif'))
tetpack.to_challenge_output(current_tet_str, os.path.join(path, str(step) + '.csv'))
print 'done.'
step += 1
def main(filename='mp-1368.mson',
beta=1.0,
initial_temp=1.0,
precomputed_structure=False):
def temperature(phi_, beta_, initial_temp_):
#cooling profile. returns a temperature according to initial_temp*exp(-beta*phi), where phi is the current packing fraction, and beta is a free parameter
return initial_temp_ * np.exp(-beta_ * phi_)
#Filename of starting structure
#Cutoff for tetrahedron distortion -- higher numbers will accept more distorted tetrahedra
std_dev_cutoff = 0.50
#Initial factor for increasing all axes
initial_increase_factor = 2.5
#Compression increment -- compress by fixed percentage:
compression_factor = -0.01
#Tetrahedra become distorted due compounding numerical error. Re-regularize every n steps:
normalization_frequency = 1
#Save structure every n steps:
save_frequency = 1
#Controls how much tetrahedra can jostle during packing
temp = 1.
#How many tries to fit a tetrahedra before skipping
resolution_max = 5000
#How far a tet can travel randomly
distance_max = 1.0
#Initialize sturcture and tetrahedra
print '\nLoading initial structure...',
sys.stdout.flush()
initial_structure = pm.read_structure(filename)
if not precomputed_structure:
path = initial_structure.composition.alphabetical_formula.replace(
' ', '') + '_beta_' + str(beta) + '_T_' + str(initial_temp)
if not os.path.exists(path):
os.mkdir(path)
print initial_structure.composition.alphabetical_formula + ' loaded.'
print '\nExtracting tetrahedra...',
sys.stdout.flush()
tet_str, tet_reg = tetpack.tetrahedra_from_structure(
initial_structure, stdcutoff=std_dev_cutoff)
print str(len(tet_reg)) + ' initial tetrahedra extracted.'
#Expand structure initally
print '\nExpanding cell axes by factor of ' + str(
initial_increase_factor) + '...',
sys.stdout.flush()
current_tet_str = tetpack.adjust_axes(tet_str, initial_increase_factor)
current_tet_reg = map(tetpack.tetrahedron, [
current_tet_str[5 * i:5 * i + 5]
for i in range(len(current_tet_str) / 5)
])
print 'done: \na = ' + str(current_tet_str.lattice.a) + '\nb = ' + str(
current_tet_str.lattice.b) + '\nc = ' + str(
current_tet_str.lattice.c)
phi = tetpack.packing_density(current_tet_str)
print '\nRelaxing structure via Ewald summation...'
sys.stdout.flush()
current_tet_str = tetpack.ewald_relaxation(current_tet_str,
max_steps=1,
motion_factor=temperature(
phi, beta,
initial_temp))
else:
path = filename.rstrip('.mson') + '_beta_' + str(beta) + '_T_' + str(
initial_temp)
if not os.path.exists(path):
os.mkdir(path)
current_tet_str = initial_structure
current_tet_reg = map(tetpack.tetrahedron, [
current_tet_str[5 * i:5 * i + 5]
for i in range(len(current_tet_str) / 5)
])
print '\nBeginning compression loop:'
#Loop until collision
collision = False
step = 0
while (not collision):
phi = tetpack.packing_density(current_tet_str)
if np.mod(step, normalization_frequency) == 0:
print 'Normalizing tetrahedra...',
sys.stdout.flush()
[tet.regularize() for tet in current_tet_reg]
print 'done.'
current_tet_str, current_tet_reg = compress(
current_tet_str, current_tet_reg,
temperature(phi, beta, initial_temp) * compression_factor)
print 'Step ' + str(step) + ' packing fraction: ' + str(
phi) + ' T:' + str(temperature(phi, beta, initial_temp)) + '...',
sys.stdout.flush()
failed = check_and_resolve_collisions(
current_tet_str, current_tet_reg,
temperature(phi, beta, initial_temp), distance_max,
int(1. / temperature(phi, beta, initial_temp) * resolution_max))
if failed:
print 'Relaxing structure...',
sys.stdout.flush()
current_tet_str, current_tet_reg = compress(
current_tet_str, current_tet_reg, -1.5 *
temperature(phi, beta, initial_temp) * compression_factor)
phi = tetpack.packing_density(current_tet_str)
print 'done. Packing fraction: ' + str(phi)
print 'Single-step Ewald relaxation...'
sys.stdout.flush()
current_tet_str = tetpack.ewald_relaxation(
current_tet_str,
max_steps=1,
motion_factor=temperature(phi, beta, initial_temp))
print 'done.'
failed = False
else:
if np.mod(step, save_frequency) == 0:
print 'Writing structure...',
sys.stdout.flush()
pm.write_structure(current_tet_str,
os.path.join(path,
str(step) + '.cif'))
tetpack.to_challenge_output(
current_tet_str, os.path.join(path,
str(step) + '.csv'))
print 'done.'
step += 1
def loop_structures(self, mode='i'):
"""
reading the structures specified in spec, add special points, and excecute the specs
mode:
i: loop structures for input generation
o: loop structures for output parsing
w: print all results
"""
print('loop structures mode ', mode)
mp_key = os.environ['MP_KEY']
mp_list_vasp = ['mp-149', 'mp-2534', 'mp-8062', 'mp-2469', 'mp-1550', 'mp-830', 'mp-1986', 'mp-10695', 'mp-66',
'mp-1639', 'mp-1265', 'mp-1138', 'mp-23155', 'mp-111']
if self.data['source'] == 'mp-vasp':
items_list = mp_list_vasp
elif self.data['source'] == 'poscar':
files = os.listdir('.')
items_list = files
elif self.data['source'] == 'mar_exp':
items_list = []
local_serv = pymongo.Connection("marilyn.pcpm.ucl.ac.be")
local_db_gaps = local_serv.band_gaps
pwd = os.environ['MAR_PAS']
local_db_gaps.authenticate("setten", pwd)
for c in local_db_gaps.exp.find():
print(Structure.from_dict(c['icsd_data']['structure']).composition.reduced_formula, c['icsd_id'],\
c['MP_id'])
items_list.append({'name': 'mp-' + c['MP_id'], 'icsd': c['icsd_id'], 'mp': c['MP_id']})
else:
items_list = [line.strip() for line in open(self.data['source'])]
for item in items_list:
print('\n')
# special case, this should be encaptulated
if self.data['source'] == 'mar_exp':
print('structure from marilyn', item['name'], item['icsd'], item['mp'])
exp = local_db_gaps.exp.find({'MP_id': item['mp']})[0]
structure = Structure.from_dict(exp['icsd_data']['structure'])
structure = refine_structure(structure)
try:
kpts = local_db_gaps.GGA_BS.find({'transformations.history.0.id': item['icsd']})[0]['calculations']\
[-1]['band_structure']['kpoints']
except (IndexError, KeyError):
kpts = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
structure.kpts = kpts
print('kpoints:', structure.kpts[0], structure.kpts[1])
structure.item = item['name']
else:
if item.startswith('POSCAR_'):
structure = pmg.read_structure(item)
comment = Poscar.from_file(item).comment
# print comment
if comment.startswith("gap"):
structure.vbm_l = comment.split(" ")[1]
structure.vbm = (comment.split(" ")[2], comment.split(" ")[3], comment.split(" ")[4])
structure.cbm_l = comment.split(" ")[5]
structure.cbm = (comment.split(" ")[6], comment.split(" ")[7], comment.split(" ")[8])
else:
# print "no bandstructure information available, adding GG as 'gap'"
structure = add_gg_gap(structure)
elif 'xyz' in item:
structure = pmg.read_structure(item)
raise NotImplementedError
elif item.startswith('mp-'):
with MPRester(mp_key) as mp_database:
print('structure from mp database', item)
structure = mp_database.get_structure_by_material_id(item, final=True)
try:
bandstructure = mp_database.get_bandstructure_by_material_id(item)
structure.vbm_l = bandstructure.kpoints[bandstructure.get_vbm()['kpoint_index'][0]].label
structure.cbm_l = bandstructure.kpoints[bandstructure.get_cbm()['kpoint_index'][0]].label
structure.cbm = tuple(bandstructure.kpoints[bandstructure.get_cbm()['kpoint_index'][0]].frac_coords)
structure.vbm = tuple(bandstructure.kpoints[bandstructure.get_vbm()['kpoint_index'][0]].frac_coords)
except (MPRestError, IndexError, KeyError) as err:
print(err.message)
structure = add_gg_gap(structure)
else:
continue
structure.kpts = [list(structure.cbm), list(structure.vbm)]
structure.item = item
print(item, s_name(structure))
if mode == 'i':
self.excecute_flow(structure)
elif mode == 'w':
self.print_results(structure)
elif mode == 's':
self.insert_in_database(structure)
elif mode == 'o':
# if os.path.isdir(s_name(structure)) or os.path.isdir(s_name(structure)+'.conv'):
self.process_data(structure)
if 'ceci' in self.data['mode'] and mode == 'i':
os.chmod("job_collection", stat.S_IRWXU)
Created on September 10, 2013 @author: Qimin ''' import sys import pymatgen import math from pymatgen.symmetry.finder import SymmetryFinder file = str(sys.argv[1]) spacegroup = str(sys.argv[2]) max = float(sys.argv[3]) min = float(sys.argv[4]) iter = float(sys.argv[5]) s = pymatgen.read_structure(file) def getLowSymPrec(s,max,min,spacegroup,iter): SG = str(spacegroup) if SG!=str(SymmetryFinder(s,symprec=max).get_spacegroup_number()): print "Not reasonable spacegroup or max too large" return -1 elif SG==str(SymmetryFinder(s,symprec=min).get_spacegroup_number()): return min else: upperbound = max lowerbound = min i=0 while i<iter: trial = 10**((math.log(upperbound,10)+math.log(lowerbound,10))/2) if SG==str(SymmetryFinder(s,symprec=trial).get_spacegroup_number()):
seg = np.concatenate([pts[:-1], pts[1:]], axis=1)
nseg = len(k)-1
r = [0.5*(red[i]+red[i+1]) for i in range(nseg)]
g = [0.5*(green[i]+green[i+1]) for i in range(nseg)]
b = [0.5*(blue[i]+blue[i+1]) for i in range(nseg)]
a = np.ones(nseg, np.float)*alpha
lc = LineCollection(seg, colors=zip(r,g,b,a), linewidth = 2)
ax.add_collection(lc)
if __name__ == "__main__":
# read data
# ---------
# kpoints labels
path = HighSymmKpath(mg.read_structure("./CONTCAR")).kpath["path"]
labels = [r"$%s$" % lab for lab in path[0][0:4]]
# bands
bands = Vasprun("./vasprun.xml").get_band_structure("./KPOINTS", line_mode = True)
# projected bands
data = Procar("./PROCAR").data
# density of states
dosrun = Vasprun("./vasprun.xml")
# set up matplotlib plot
# ----------------------
# general options for plot
r_coords = relax[i].frac_coords
index = find_atom(r_coords, supercell)
supercell.replace(index, supercell[index].specie, r_coords)
if __name__ == '__main__':
center = [0.0, 0.0, 0.0]
argv = sys.argv[1:]
opts, args = getopt.getopt(argv, "s")
Size = int(args[0])
size = int(args[1])
shift_vector = get_shift_vector(center, size, size)
Shift_Vector = get_shift_vector(center, size, Size)
supercell = mg.read_structure('POSCAR_super')
shift_coords(supercell, Shift_Vector)
relax = mg.read_structure('POSCAR_relax')
shift_coords(relax, shift_vector)
defect = mg.read_structure('POSCAR_defect')
shift_coords(defect, shift_vector)
compare_coords(relax, defect, supercell, size, Size)
rescale(relax, size, Size)
replace(supercell, relax)
shift_coords(supercell, -Shift_Vector)
mg.write_structure(supercell, 'POSCAR_final')
import pymatgen
from pymatgen.symmetry.finder import SymmetryFinder
s = pymatgen.read_structure('POSCAR')
#print SymmetryFinder(s).find_primitive()
structure = SymmetryFinder(s).find_primitive()
pymatgen.write_structure(structure, "POSCAR")
import pymatgen as mg
from pymatgen.core.structure import Structure
filename = raw_input('file name:')
l = raw_input('x scale:')
m = raw_input('y scale:')
n = raw_input('z scale:')
struct = mg.read_structure(filename)
Structure.make_supercell(struct, [l, m, n])
mg.write_structure(struct, "poscar_temp")
f = open("poscar_temp")
w = open("mast.inp", "w")
def getinfo(line):
line = line.strip('\n')
data = line.split(' ')
while 1:
try:
data.remove('')
except:
break
return data
line = []
elenum = []
line.append(f.readline())
# read args
if len(sys.argv) > 1:
if len(sys.argv) == 2:
fstruct = sys.argv[1]
else:
fstruct = sys.argv[-1]
if "-d" in sys.argv:
try:
ndiv = int(sys.argv[sys.argv.index("-d") + 1])
except ValueError, IndexError:
print("-d must be followed by an integer")
exit(1)
# read structure
if os.path.exists(fstruct):
struct = mg.read_structure(fstruct)
else:
print("File %s does not exist" % fstruct)
exit(1)
# symmetry information
struct_sym = SymmetryFinder(struct)
print("lattice type : {0}".format(struct_sym.get_lattice_type()))
print("space group : {0} ({1})".format(struct_sym.get_spacegroup_symbol(),
struct_sym.get_spacegroup_number()))
# Compute first brillouin zone
ibz = HighSymmKpath(struct)
ibz.get_kpath_plot(savefig="path.png")
print("ibz type : {0}".format(ibz.name))
def loop_structures(self, mode="i"):
"""
reading the structures specified in spec, add special points, and excecute the specs
mode:
i: loop structures for input generation
o: loop structures for output parsing
w: print all results
"""
print("loop structures mode ", mode)
mp_key = os.environ["MP_KEY"]
mp_list_vasp = [
"mp-149",
"mp-2534",
"mp-8062",
"mp-2469",
"mp-1550",
"mp-830",
"mp-1986",
"mp-10695",
"mp-66",
"mp-1639",
"mp-1265",
"mp-1138",
"mp-23155",
"mp-111",
]
if self.data["source"] == "mp-vasp":
items_list = mp_list_vasp
elif self.data["source"] in ["poscar", "cif"]:
files = os.listdir(".")
items_list = files
elif self.data["source"] == "mar_exp":
items_list = []
local_serv = pymongo.Connection("marilyn.pcpm.ucl.ac.be")
local_db_gaps = local_serv.band_gaps
pwd = os.environ["MAR_PAS"]
local_db_gaps.authenticate("setten", pwd)
for c in local_db_gaps.exp.find():
name = (
Structure.from_dict(c["icsd_data"]["structure"]).composition.reduced_formula,
c["icsd_id"],
c["MP_id"],
)
print(name)
# Structure.from_dict(c['icsd_data']['structure']).to(fmt='cif',filename=name)
items_list.append({"name": "mp-" + c["MP_id"], "icsd": c["icsd_id"], "mp": c["MP_id"]})
else:
items_list = [line.strip() for line in open(self.data["source"])]
for item in items_list:
print("\n")
# special case, this should be encaptulated
if self.data["source"] == "mar_exp":
print("structure from marilyn", item["name"], item["icsd"], item["mp"])
exp = local_db_gaps.exp.find({"MP_id": item["mp"]})[0]
structure = Structure.from_dict(exp["icsd_data"]["structure"])
structure = refine_structure(structure)
structure.to(fmt="cif", filename=item["name"])
try:
kpts = local_db_gaps.GGA_BS.find({"transformations.history.0.id": item["icsd"]})[0]["calculations"][
-1
]["band_structure"]["kpoints"]
except (IndexError, KeyError):
kpts = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
structure.kpts = kpts
print("kpoints:", structure.kpts[0], structure.kpts[1])
structure.item = item["name"]
else:
if item.startswith("POSCAR_"):
structure = pmg.read_structure(item)
comment = Poscar.from_file(item).comment
# print comment
if comment.startswith("gap"):
structure.vbm_l = comment.split(" ")[1]
structure.vbm = (comment.split(" ")[2], comment.split(" ")[3], comment.split(" ")[4])
structure.cbm_l = comment.split(" ")[5]
structure.cbm = (comment.split(" ")[6], comment.split(" ")[7], comment.split(" ")[8])
else:
# print "no bandstructure information available, adding GG as 'gap'"
structure = add_gg_gap(structure)
elif "xyz" in item:
structure = pmg.read_structure(item)
raise NotImplementedError
elif item.startswith("mp-"):
with MPRester(mp_key) as mp_database:
print("structure from mp database", item)
structure = mp_database.get_structure_by_material_id(item, final=True)
try:
bandstructure = mp_database.get_bandstructure_by_material_id(item)
structure.vbm_l = bandstructure.kpoints[bandstructure.get_vbm()["kpoint_index"][0]].label
structure.cbm_l = bandstructure.kpoints[bandstructure.get_cbm()["kpoint_index"][0]].label
structure.cbm = tuple(
bandstructure.kpoints[bandstructure.get_cbm()["kpoint_index"][0]].frac_coords
)
structure.vbm = tuple(
bandstructure.kpoints[bandstructure.get_vbm()["kpoint_index"][0]].frac_coords
)
except (MPRestError, IndexError, KeyError) as err:
print(err.message)
structure = add_gg_gap(structure)
else:
continue
structure.kpts = [list(structure.cbm), list(structure.vbm)]
structure.item = item
print(item, s_name(structure))
if mode == "i":
try:
self.excecute_flow(structure)
except Exception as exc:
print("input generation failed")
print(exc)
elif mode == "w":
try:
self.print_results(structure)
except:
print("writing output failed")
elif mode == "s":
try:
self.insert_in_database(structure)
except:
print("database insertion failed")
elif mode == "o":
# if os.path.isdir(s_name(structure)) or os.path.isdir(s_name(structure)+'.conv'):
try:
self.process_data(structure)
except:
print("output parsing failed")
if "ceci" in self.data["mode"] and mode == "i":
os.chmod("job_collection", stat.S_IRWXU)
nseg = len(k) - 1
r = [0.5 * (red[i] + red[i + 1]) for i in range(nseg)]
g = [0.5 * (green[i] + green[i + 1]) for i in range(nseg)]
b = [0.5 * (blue[i] + blue[i + 1]) for i in range(nseg)]
a = np.ones(nseg, np.float) * alpha
lc = LineCollection(seg, colors=zip(r, g, b, a), linewidth=2)
ax.add_collection(lc)
if __name__ == "__main__":
# read data
# ---------
# kpoints labels
path = HighSymmKpath(mg.read_structure("./POSCAR")).kpath["path"]
labels = list()
for p in path:
if len(labels) != 0:
labels[-1] = r"$%s,%s$" % (labels[-1].strip("$"), p[0])
else:
labels.append(r"$%s$" % p[0])
for kp in p[1:]:
labels.append(r"$%s$" % kp)
# density of state
dosrun = Vasprun("../../DOS/Cu/vasprun.xml")
spd_dos = dosrun.complete_dos.get_spd_dos()
# bands
run = Vasprun("vasprun.xml", parse_projected_eigen=True)
import numpy as np
import math
import pymatgen
from pymatgen.symmetry.finder import SymmetryFinder
s = pymatgen.read_structure('POSCAR')
# composition
print "Formula:\t"+str(s.composition.formula)
# spacegroup
print "Spacegroup:\t"+str(SymmetryFinder(s).get_spacegroup())
# lattice constant lengths
print "---------"
print "a\tb\tc"
print "\t".join(str(round(each,2)) for each in s.lattice.abc)
# and angles
print "alpha\tbeta\tgamma"
print "\t".join(str(round(each,2)) for each in s.lattice.angles)
print "---------"
# total number of sites
print "Num. of Sites: "+str(s.num_sites)
print "---------"
# print symmetrically distinct atoms and their wyckoff positions
print "Wyckoff Postions"
equiv=SymmetryFinder(s).get_symmetry_dataset()["equivalent_atoms"]
wy=SymmetryFinder(s).get_symmetry_dataset()["wyckoffs"]
# GROUP ATOMS BY EQUIVALENT POSITIONS
#
import pymatgen as mg
from pymatgen.core.structure import Structure
filename = raw_input("file name:")
l = raw_input("x scale:")
m = raw_input("y scale:")
n = raw_input("z scale:")
struct = mg.read_structure(filename)
Structure.make_supercell(struct, [l, m, n])
mg.write_structure(struct, "poscar_temp")
f = open("poscar_temp")
w = open("mast.inp", "w")
def getinfo(line):
line = line.strip("\n")
data = line.split(" ")
while 1:
try:
data.remove("")
except:
break
return data
line = []
elenum = []
line.append(f.readline())