def crystalAtoms(self, start_layer=0, end_layer=1, coordinates='orth_surface', sub_layers=False, minimal=True, ): atoms = self.crystal().atoms() if sub_layers: in_equal_layers = self.inequivalentLayers() repeats = int(np.ceil(end_layer/float(in_equal_layers))) atoms = atoms.repeat((1, 1, repeats + 1)) atoms.positions -= self.crystal().unitCell()*(repeats) start_layer += in_equal_layers -1 end_layer += in_equal_layers -1 atoms = orderAtoms(atoms, (2, 1, 0)) sl = slice(start_layer, end_layer) groups = groupAtoms(atoms)[::-1][sl] if len(groups) == 0: atoms = Atoms([], cell=atoms.get_cell(), pbc=atoms.get_pbc()) else: atoms, indices = groups[0] for group, indices in groups[1:]: atoms += group else: cell = atoms.unitCell() atoms = atoms.repeat((1, 1, end_layer-start_layer)) atoms.positions += cell*start_layer return atoms
def test_negativeindex(): from ase.atoms import Atoms from ase.constraints import FixScaled a1 = Atoms( symbols='X2', positions=[ [0., 0., 0.], [2., 0., 0.], ], cell=[ [4., 0., 0.], [0., 4., 0.], [0., 0., 4.], ], ) fs1 = FixScaled(a1.get_cell(), -1, mask=(True, False, False)) fs2 = FixScaled(a1.get_cell(), 1, mask=(False, True, False)) a1.set_constraint([fs1, fs2]) # reassigning using atoms.__getitem__ a2 = a1[0:2] assert len(a1._constraints) == len(a2._constraints)
def unf(phonon, sc_mat, qpoints, knames=None, x=None, xpts=None): prim = phonon.get_primitive() prim = Atoms(symbols=prim.get_chemical_symbols(), cell=prim.get_cell(), positions=prim.get_positions()) #vesta_view(prim) sc_qpoints = np.array([np.dot(q, sc_mat) for q in qpoints]) phonon.set_qpoints_phonon(sc_qpoints, is_eigenvectors=True) freqs, eigvecs = phonon.get_qpoints_phonon() uf = phonon_unfolder(atoms=prim, supercell_matrix=sc_mat, eigenvectors=eigvecs, qpoints=sc_qpoints, phase=False) weights = uf.get_weights() #ax=plot_band_weight([list(x)]*freqs.shape[1],freqs.T*8065.6,weights[:,:].T*0.98+0.01,xticks=[knames,xpts],style='alpha') ax = plot_band_weight([list(x)] * freqs.shape[1], freqs.T * 33.356, weights[:, :].T * 0.99 + 0.001, xticks=[knames, xpts], style='alpha') return ax
from __future__ import print_function from ase.atoms import Atoms from ase.constraints import FixScaled a1 = Atoms(symbols = 'X2', positions = [[0.,0.,0.], [2.,0.,0.], ], cell = [[4.,0.,0.], [0.,4.,0.], [0.,0.,4.], ], ) fs1 = FixScaled(a1.get_cell(), -1, mask=(True, False, False)) fs2 = FixScaled(a1.get_cell(), 1, mask=(False, True, False)) a1.set_constraint([fs1,fs2]) # reassigning using atoms.__getitem__ a2 = a1[0:2] assert len(a1._constraints) == len(a2._constraints)
def HEA(f_file, verbose=3): """ Helium approach Calculate porosity using celllist f_file ... file name verbose ... verbosity """ #print('----------------') #print('HEA: calculation') #print('----------------') print('f_file : {}'.format(f_file)) t1 = time.time() # read the structure struct = read(f_file) p_framework = struct.get_positions() s_framework = struct.get_chemical_symbols() cell = struct.get_cell() Lx = numpy.linalg.norm(cell[:][0, :]) Ly = numpy.linalg.norm(cell[:][1, :]) Lz = numpy.linalg.norm(cell[:][2, :]) # Guess for M M0 = get_M(Lx=Lx, Ly=Ly, Lz=Lz, m='high') # Optimize d for M0 dopt = get_opt_r(Lx=Lx, Ly=Ly, Lz=Lz, Mx=M0[0], My=M0[1], Mz=M0[2]) # Optimize M for dopt M = get_M(Lx=Lx, Ly=Ly, Lz=Lz, m='high', d=dopt) Mx, My, Mz = M # Celllist = get_subcells(cell, M, p_framework) # Get COMs of Celllist p_insert = get_COMs_celllist(cell, M) # Species we are inserting s_insert = ['He'] * len(p_insert) # Remove atoms an the insertion grid overlapping with the framework ropt = dopt / 2. print('r_opt: {:10.5f} A'.format(ropt)) p_insert_red, s_insert_red = prune_points(p_framework=p_framework, p_insert=p_insert, s_framework=s_framework, s_insert=s_insert, r_i=ropt) # Calculate number of atoms N_insert = len(p_insert) N_insert_red = len(p_insert_red) P_points = N_insert_red / N_insert print('N_insert: {:10d}'.format(N_insert)) print('N_insert_reduced: {:10d}'.format(N_insert_red)) # Calculate volumes struct_subcell = Atoms(cell=cell) cell_subcell = struct_subcell.get_cell() cell_subcell[:][0, :] = cell_subcell[:][0, :] / Mx cell_subcell[:][1, :] = cell_subcell[:][1, :] / My cell_subcell[:][2, :] = cell_subcell[:][2, :] / Mz struct_subcell.set_cell(cell_subcell) V_subcell = struct_subcell.get_volume() * N_insert_red V_insert = 4 / 3. * numpy.pi * ropt**3. * N_insert_red V_framework = struct.get_volume() P_subcell = V_subcell / V_framework print('optimal M: {:10d} {:10d} {:10d}'.format(Mx, My, Mz)) # simple cubic packing f_sc = 0.52 f_calc = V_insert / V_subcell print('------------------') print('packing fraction f') print('------------------') print('f_sc: {:15.5f}'.format(f_sc)) print('f_calc: {:15.5f}'.format(f_calc)) P_volume = V_insert / (V_framework * f_calc) print('----------------------') print('HEA porosity P [%]') print('----------------------') print('P_subcell: {:15.5f}'.format(P_subcell * 100.0)) print('P_points: {:15.5f}'.format(P_points * 100.0)) print('P_volume: {:15.5f}'.format(P_volume * 100.0)) print('---------------') print(' Volume V [A^3]') print('---------------') print('V_framework: {:15.5f}'.format(V_framework)) print('V_subcell: {:15.5f}'.format(V_subcell)) print('V_He: {:15.5f}'.format(V_insert)) t2 = time.time() print('Total CPU time: {:15.5f} s'.format(t2 - t1)) print('\n') if verbose > 3: get_ase(f_file, p_insert_red, s_insert_red) return [ P_points * 100.0, P_subcell * 100.0, P_volume * 100.0, V_framework, V_subcell, V_insert, N_insert, N_insert_red, f_calc, ropt ]
class execution(object): def __init__(self, executionInput, xyz=None, stateFile='state.json'): ''' Setup a new execution Name: input file xyz: xyz coordinates (for future automated execution) stateFile: state file to use for tracking of executions TODO: - only ground state in non-periodic cell supported NOW, more to add! - inputs from a dict to better handle automated executions ''' if isinstance(executionInput, str): self.inp = self.json(executionInput) self.inputFileName = executionInput if isinstance(executionInput, dict): self.inp = executionInput self.inputFileName = 'dict' # TODO: generate meaningful name here (how?) self.setup() def setup(self): '''setup''' self.functional = self.inp.get('functional') or 'LDA' self.runtype = self.inp.get('runtype') or 'calc' # this actually can't be none! _xyz = self.inp.get('xyz') if isinstance(_xyz, str): self.inputXYZ = _xyz self.atoms = read(self.inputXYZ) else: # support reading frm a dict (TODO: other type of objects - ?) from ase.atoms import Atoms from ase.atom import Atom self.atoms = \ Atoms([Atom(a[0],(a[1],a[2],a[3])) for a in _xyz]) # TODO: support multiple cell types self.atoms.cell = (self.inp['cell']['x'], self.inp['cell']['y'], self.inp['cell']['z']) # Mixer setup - only "broyden" supported self.mixer = self.inp.get('mixer') if self.mixer is None: self.mixer = Mixer else: self.mixer = BroydenMixer # Custom ID: allow to trace parameter set using a custom id self.custom_id = self.inp.get('custom_id') # Periodic Boundary Conditions self.atoms.pbc = False # This should not be done always self.atoms.center() self.nbands = self.inp.get('nbands') self.gpts = None self.setup_gpts() self.max_iterations = self.inp.get('maxiter') or 333 # Name: to be set by __str__ first time it is run self.name = None # self.logExtension = 'txt' self.dataExtension = 'gpw' # Timing / statistics self.lastElapsed = None # def get_optimizer(self, name='QuasiNewton'): from importlib import import_module name = name.lower() _optimMap = \ { 'mdmin': 'MDMin', 'hesslbfgs': 'HessLBFGS', 'linelbfgs': 'LineLBFGS', 'fire': 'FIRE', 'lbfgs': 'LBFGS', 'lbfgsls': 'LBFGSLineSearch', 'bfgsls': 'BFGSLineSearch', 'bfgs': 'BFGS', 'quasinewton': 'QuasiNewton' } _class = _optimMap[name] _m = import_module('ase.optimize') return getattr(_m, _class) # def setup_gpts(self): self.gpts = h2gpts(self.inp.get('spacing') or 0.20, self.atoms.get_cell(), idiv=self.inp.get('idiv') or 8) def filename(self, force_id=None, extension='txt'): '''to correctly support this: stateFile support must be completed as it isn't just add "_0" to __str__() for now ''' base = self.__str__() if self.custom_id is not None: base = base + '_' + self.custom_id if force_id is None: _fn = base + '_0.' + extension else: _fn = '{}_{}.{}'.format(base, force_id, extension) return _fn # @property def xyz(self): return self.inp['xyz'] # @property def spacegroup(self): '''international ID of determined spacegroup''' sg = spglib.get_symmetry_dataset(spglib.refine_cell( self.atoms))['international'] return sg.replace('/', ':') # def __str__(self): '''get execution name - to be used for log filename Components: - Number atoms - Volume - Spacegroup number - Functional name ''' if self.name is None: self.name = '{}_{}_{}_{}'.format( len(self.atoms), int(self.atoms.get_volume() * 100), self.spacegroup, self.functional) _gpaw = os.environ['GPAW'] _pwd = os.environ['PWD'] self.procname = '{} ({})'.format(self.name, _pwd.replace(_gpaw + '/', '')) setproctitle(self.procname) return self.name ## def print(self, *args, **kwargs): '''print wrapper - initial step before logger''' r = print(*args, *kwargs) return r # def pretty(self): '''pretty print original parameters''' _p = pformat(self.inp) self.print(_p) # def json(self, filename): '''load parameters from json''' with open(filename, 'rb') as f: s = f.read() j = json.loads(s) return j # def run(self, force_id=None, runtype=None): ''' perform execution - only calc or optim supported now ''' _lastStart = time.time() if runtype is None and self.runtype is 'calc': _f = self.calc else: _f = self.optim # calculate try: result = _f(force_id) finally: _lastEnd = time.time() self.lastElapsed = _lastEnd - _lastStart return result # def calc(self, force_id=None, functional=None): '''prepare and launch execution force_id: use this id for filename generation ''' # Allow to override the funtional _fnl = functional or self.functional # generate filenames _fn = self.filename(force_id) _dn = self.filename(force_id, extension=self.dataExtension) self.print('Calculation with {} log and Input: {}.'.format( _fn, self.inputFileName)) if self.nbands is None: _calc = GPAW(xc=_fnl, txt=self.filename(force_id), mixer=self.mixer(), maxiter=self.max_iterations, gpts=self.gpts) else: _calc = GPAW(xc=_fnl, txt=self.filename(force_id), nbands=self.nbands, mixer=self.mixer(), maxiter=self.max_iterations, gpts=self.gpts) self.atoms.set_calculator(_calc) self.atoms.get_potential_energy() # write out wavefunctions _calc.write(_dn, 'all') return _calc ##### def optim(self, force_id=None, functional=None): '''prepare and launch execution force_id: use this id for filename generation ''' _optim_name = self.inp.get('optimizer') or 'QuasiNewton' optimizer = self.get_optimizer(_optim_name) # Allow to override the funtional _fnl = functional or self.functional # generate filenames _fn = self.filename(force_id) _dn = self.filename(force_id, extension=self.dataExtension) self.print('Optimization with {} log and Input: {}.'.format( _fn, self.inputFileName)) if self.nbands is None: _calc = GPAW(xc=_fnl, txt=self.filename(force_id), mixer=self.mixer(), maxiter=self.max_iterations, gpts=self.gpts) else: _calc = GPAW(xc=_fnl, txt=self.filename(force_id), nbands=self.nbands, mixer=self.mixer(), maxiter=self.max_iterations, gpts=self.gpts) self.atoms.set_calculator(_calc) self.opt = optimizer(self.atoms, trajectory=self.__str__() + '_emt.traj') self.opt.run(fmax=0.05) # write out wavefunctions _calc.write(_dn, 'all') return _calc
def __init__(self, geometry=None, **kwargs) -> None: if geometry == None: atoms = Atoms(**kwargs) elif type(geometry) == ase.atoms.Atoms: atoms = geometry.copy() elif Path(geometry).is_file(): if str(Path(geometry).parts[-1]) == "geometry.in.next_step": atoms = ase.io.read(geometry, format="aims") else: try: atoms = ase.io.read(geometry) except Exception as excpt: logger.error(str(excpt)) raise Exception( "ASE was not able to recognize the file format, e.g., a non-standard cif-format." ) elif Path(geometry).is_dir(): raise Exception( "You specified a directory as input. The geometry must be a file." ) else: atoms = None assert type(atoms) == ase.atoms.Atoms, "Atoms not read correctly." # Get data from another Atoms object: numbers = atoms.get_atomic_numbers() positions = atoms.get_positions() cell = atoms.get_cell() celldisp = atoms.get_celldisp() pbc = atoms.get_pbc() constraint = [c.copy() for c in atoms.constraints] masses = atoms.get_masses() magmoms = None charges = None momenta = None if atoms.has("initial_magmoms"): magmoms = atoms.get_initial_magnetic_moments() if atoms.has("initial_charges"): charges = atoms.get_initial_charges() if atoms.has("momenta"): momenta = atoms.get_momenta() self.arrays = {} super().__init__( numbers=numbers, positions=positions, cell=cell, celldisp=celldisp, pbc=pbc, constraint=constraint, masses=masses, magmoms=magmoms, charges=charges, momenta=momenta, ) self._is_1d = None self._is_2d = None self._is_3d = None self._periodic_axes = None self._check_lattice_vectors() try: self.sg = ase.spacegroup.get_spacegroup(self, symprec=1e-2) except: self.sg = ase.spacegroup.Spacegroup(1) self.lattice = self.cell.get_bravais_lattice().crystal_family
def read_pw_out(fileobj, index=-1, results_required=True): """Reads Quantum ESPRESSO output files. The atomistic configurations as well as results (energy, force, stress, magnetic moments) of the calculation are read for all configurations within the output file. Will probably raise errors for broken or incomplete files. Parameters ---------- fileobj : file|str A file like object or filename index : slice The index of configurations to extract. results_required : bool If True, atomistic configurations that do not have any associated results will not be included. This prevents double printed configurations and incomplete calculations from being returned as the final configuration with no results data. Yields ------ structure : Atoms The next structure from the index slice. The Atoms has a SinglePointCalculator attached with any results parsed from the file. """ if isinstance(fileobj, str): fileobj = open(fileobj, 'rU') # work with a copy in memory for faster random access pwo_lines = fileobj.readlines() # TODO: index -1 special case? # Index all the interesting points indexes = { _PW_START: [], _PW_END: [], _PW_CELL: [], _PW_POS: [], _PW_MAGMOM: [], _PW_FORCE: [], _PW_TOTEN: [], _PW_STRESS: [], _PW_FERMI: [], _PW_HIGHEST_OCCUPIED: [], _PW_HIGHEST_OCCUPIED_LOWEST_FREE: [], _PW_KPTS: [], _PW_BANDS: [], _PW_BANDSTRUCTURE: [], _PW_ELECTROSTATIC_EMBEDDING: [], _PW_NITER: [], _PW_DONE: [], _PW_WALLTIME: [] } for idx, line in enumerate(pwo_lines): for identifier in indexes: if identifier in line: indexes[identifier].append(idx) # Configurations are either at the start, or defined in ATOMIC_POSITIONS # in a subsequent step. Can deal with concatenated output files. all_config_indexes = sorted(indexes[_PW_START] + indexes[_PW_POS]) # Slice only requested indexes # setting results_required argument stops configuration-only # structures from being returned. This ensures the [-1] structure # is one that has results. Two cases: # - SCF of last configuration is not converged, job terminated # abnormally. # - 'relax' and 'vc-relax' re-prints the final configuration but # only 'vc-relax' recalculates. if results_required: results_indexes = sorted(indexes[_PW_TOTEN] + indexes[_PW_FORCE] + indexes[_PW_STRESS] + indexes[_PW_MAGMOM] + indexes[_PW_BANDS] + indexes[_PW_ELECTROSTATIC_EMBEDDING] + indexes[_PW_BANDSTRUCTURE]) # Prune to only configurations with results data before the next # configuration results_config_indexes = [] for config_index, config_index_next in zip( all_config_indexes, all_config_indexes[1:] + [len(pwo_lines)]): if any([ config_index < results_index < config_index_next for results_index in results_indexes ]): results_config_indexes.append(config_index) # slice from the subset image_indexes = results_config_indexes[index] else: image_indexes = all_config_indexes[index] # Extract initialisation information each time PWSCF starts # to add to subsequent configurations. Use None so slices know # when to fill in the blanks. pwscf_start_info = dict((idx, None) for idx in indexes[_PW_START]) if isinstance(image_indexes, int): image_indexes = [image_indexes] for image_index in image_indexes: # Find the nearest calculation start to parse info. Needed in, # for example, relaxation where cell is only printed at the # start. if image_index in indexes[_PW_START]: prev_start_index = image_index else: # The greatest start index before this structure prev_start_index = [ idx for idx in indexes[_PW_START] if idx < image_index ][-1] # add structure to reference if not there if pwscf_start_info[prev_start_index] is None: pwscf_start_info[prev_start_index] = parse_pwo_start( pwo_lines, prev_start_index) # Get the bounds for information for this structure. Any associated # values will be between the image_index and the following one, # EXCEPT for cell, which will be 4 lines before if it exists. for next_index in all_config_indexes: if next_index > image_index: break else: # right to the end of the file next_index = len(pwo_lines) # Get the structure # Use this for any missing data prev_structure = pwscf_start_info[prev_start_index]['atoms'] if image_index in indexes[_PW_START]: structure = prev_structure.copy() # parsed from start info else: if _PW_CELL in pwo_lines[image_index - 5]: # CELL_PARAMETERS would be just before positions if present cell, cell_alat = get_cell_parameters(pwo_lines[image_index - 5:image_index]) else: cell = prev_structure.cell cell_alat = pwscf_start_info[prev_start_index]['alat'] # give at least enough lines to parse the positions # should be same format as input card n_atoms = len(prev_structure) positions_card = get_atomic_positions( pwo_lines[image_index:image_index + n_atoms + 1], n_atoms=n_atoms, cell=cell, alat=cell_alat) # convert to Atoms object symbols = [ label_to_symbol(position[0]) for position in positions_card ] tags = [label_to_tag(position[0]) for position in positions_card] positions = [position[1] for position in positions_card] constraint_idx = [position[2] for position in positions_card] constraint = get_constraint(constraint_idx) structure = Atoms(symbols=symbols, positions=positions, cell=cell, constraint=constraint, pbc=True, tags=tags) # Extract calculation results # Energy energy = None for energy_index in indexes[_PW_TOTEN]: if image_index < energy_index < next_index: energy = float( pwo_lines[energy_index].split()[-2]) * units['Ry'] # Electrostatic enbedding energy elec_embedding_energy = None for eee_index in indexes[_PW_ELECTROSTATIC_EMBEDDING]: if image_index < eee_index < next_index: elec_embedding_energy = float( pwo_lines[eee_index].split()[-2]) * units['Ry'] # Number of iterations n_iterations = None for niter_index in indexes[_PW_NITER]: if image_index < niter_index < next_index: n_iterations = int( pwo_lines[niter_index].split('#')[1].split()[0]) # Forces forces = None for force_index in indexes[_PW_FORCE]: if image_index < force_index < next_index: # Before QE 5.3 'negative rho' added 2 lines before forces # Use exact lines to stop before 'non-local' forces # in high verbosity if not pwo_lines[force_index + 2].strip(): force_index += 4 else: force_index += 2 # assume contiguous forces = [[float(x) for x in force_line.split()[-3:]] for force_line in pwo_lines[force_index:force_index + len(structure)]] forces = np.array(forces) * units['Ry'] / units['Bohr'] # Stress stress = None for stress_index in indexes[_PW_STRESS]: if image_index < stress_index < next_index: sxx, sxy, sxz = pwo_lines[stress_index + 1].split()[:3] _, syy, syz = pwo_lines[stress_index + 2].split()[:3] _, _, szz = pwo_lines[stress_index + 3].split()[:3] stress = np.array([sxx, syy, szz, syz, sxz, sxy], dtype=float) # sign convention is opposite of ase stress *= -1 * units['Ry'] / (units['Bohr']**3) # Magmoms magmoms = None for magmoms_index in indexes[_PW_MAGMOM]: if image_index < magmoms_index < next_index: magmoms = [ float(mag_line.split('=')[-1]) for mag_line in pwo_lines[magmoms_index + 1:magmoms_index + 1 + len(structure)] ] # Fermi level / highest occupied level and lowest unoccupied level efermi = None lumo_ene = None for fermi_index in indexes[_PW_FERMI]: if image_index < fermi_index < next_index: efermi = float(pwo_lines[fermi_index].split()[-2]) if efermi is None: for ho_index in indexes[_PW_HIGHEST_OCCUPIED]: if image_index < ho_index < next_index: efermi = float(pwo_lines[ho_index].split()[-1]) if efermi is None: for holf_index in indexes[_PW_HIGHEST_OCCUPIED_LOWEST_FREE]: if image_index < holf_index < next_index: efermi = float(pwo_lines[holf_index].split()[-2]) lumo_ene = float(pwo_lines[holf_index].split()[-1]) # K-points ibzkpts = None weights = None kpoints_warning = "Number of k-points >= 100: " + \ "set verbosity='high' to print them." for kpts_index in indexes[_PW_KPTS]: nkpts = int(pwo_lines[kpts_index].split()[4]) kpts_index += 2 if pwo_lines[kpts_index].strip() == kpoints_warning: continue # QE prints the k-points in units of 2*pi/alat # with alat defined as the length of the first # cell vector cell = structure.get_cell() alat = np.linalg.norm(cell[0]) ibzkpts = [] weights = [] for i in range(nkpts): L = pwo_lines[kpts_index + i].split() weights.append(float(L[-1])) coord = np.array([L[-6], L[-5], L[-4].strip('),')], dtype=float) coord *= 2 * np.pi / alat coord = kpoint_convert(cell, ckpts_kv=coord) ibzkpts.append(coord) ibzkpts = np.array(ibzkpts) weights = np.array(weights) # Bands kpts = None kpoints_warning = "Number of k-points >= 100: " + \ "set verbosity='high' to print the bands." for bands_index in indexes[_PW_BANDS] + indexes[_PW_BANDSTRUCTURE]: if image_index < bands_index < next_index: bands_index += 2 if pwo_lines[bands_index].strip() == kpoints_warning: continue assert ibzkpts is not None spin, bands, eigenvalues = 0, [], [[], []] while True: L = pwo_lines[bands_index].replace('-', ' -').split() if len(L) == 0: if len(bands) > 0: eigenvalues[spin].append(bands) bands = [] elif L == ['occupation', 'numbers']: # Skip the lines with the occupation numbers bands_index += len(eigenvalues[spin][0]) // 8 + 1 elif L[0] == 'k' and L[1].startswith('='): pass elif 'SPIN' in L: if 'DOWN' in L: spin += 1 else: try: bands.extend(map(float, L)) except ValueError: break bands_index += 1 if spin == 1: assert len(eigenvalues[0]) == len(eigenvalues[1]) # assert len(eigenvalues[0]) == len(ibzkpts), \ # (np.shape(eigenvalues), len(ibzkpts)) kpts = [] for s in range(spin + 1): for w, k, e in zip(weights, ibzkpts, eigenvalues[s]): kpt = SinglePointKPoint(w, s, k, eps_n=e) kpts.append(kpt) # Convergence job_done = False for done_index in indexes[_PW_DONE]: if image_index < done_index < next_index: job_done = True # Walltime walltime = None for wt_index in indexes[_PW_WALLTIME]: if image_index < wt_index < next_index: walltime = time_to_float(pwo_lines[wt_index].split()[-2]) # Put everything together calc = SinglePointDFTCalculator(structure, energy=energy, forces=forces, stress=stress, magmoms=magmoms, efermi=efermi, ibzkpts=ibzkpts) calc.results['homo_energy'] = efermi calc.results['lumo_energy'] = lumo_ene calc.results['electrostatic embedding'] = elec_embedding_energy calc.results['iterations'] = n_iterations calc.results['job done'] = job_done calc.results['walltime'] = walltime calc.kpts = kpts structure.calc = calc yield structure
from ase.atoms import Atoms from ase.constraints import FixScaled a1 = Atoms( symbols='X2', positions=[ [0., 0., 0.], [2., 0., 0.], ], cell=[ [4., 0., 0.], [0., 4., 0.], [0., 0., 4.], ], ) fs1 = FixScaled(a1.get_cell(), -1, mask=(True, False, False)) fs2 = FixScaled(a1.get_cell(), 1, mask=(False, True, False)) a1.set_constraint([fs1, fs2]) # reassigning using atoms.__getitem__ a2 = a1[0:2] assert len(a1._constraints) == len(a2._constraints)