# Copyright 2008, 2009 CAMd # (see accompanying license files for details). """Definition of the Atoms class. This module defines the central object in the ASE package: the Atoms object. """ import numbers import warnings from math import cos, sin import copy import numpy as np import ase.units as units from ase.atom import Atom from ase.data import atomic_numbers, chemical_symbols, atomic_masses from ase.utils import basestring from ase.geometry import (wrap_positions, find_mic, cellpar_to_cell, cell_to_cellpar) class Atoms(object): """Atoms object. The Atoms object can represent an isolated molecule, or a periodically repeated structure. It has a unit cell and there may be periodic boundary conditions along any of the three unit cell axes. Information about the atoms (atomic numbers and position) is stored in ndarrays. Optionally, there can be information about tags, momenta, masses, magnetic moments and charges. In order to calculate energies, forces and stresses, a calculator object has to attached to the atoms object. Parameters: symbols: str (formula) or list of str Can be a string formula, a list of symbols or a list of Atom objects. Examples: 'H2O', 'COPt12', ['H', 'H', 'O'], [Atom('Ne', (x, y, z)), ...]. positions: list of xyz-positions Atomic positions. Anything that can be converted to an ndarray of shape (n, 3) will do: [(x1,y1,z1), (x2,y2,z2), ...]. scaled_positions: list of scaled-positions Like positions, but given in units of the unit cell. Can not be set at the same time as positions. numbers: list of int Atomic numbers (use only one of symbols/numbers). tags: list of int Special purpose tags. momenta: list of xyz-momenta Momenta for all atoms. masses: list of float Atomic masses in atomic units. magmoms: list of float or list of xyz-values Magnetic moments. Can be either a single value for each atom for collinear calculations or three numbers for each atom for non-collinear calculations. charges: list of float Atomic charges. cell: 3x3 matrix or length 3 or 6 vector Unit cell vectors. Can also be given as just three numbers for orthorhombic cells, or 6 numbers, where first three are lengths of unit cell vectors, and the other three are angles between them (in degrees), in following order: [len(a), len(b), len(c), angle(b,c), angle(a,c), angle(a,b)]. First vector will lie in x-direction, second in xy-plane, and the third one in z-positive subspace. Default value: [1, 1, 1]. celldisp: Vector Unit cell displacement vector. To visualize a displaced cell around the center of mass of a Systems of atoms. Default value = (0,0,0) pbc: one or three bool Periodic boundary conditions flags. Examples: True, False, 0, 1, (1, 1, 0), (True, False, False). Default value: False. constraint: constraint object(s) Used for applying one or more constraints during structure optimization. calculator: calculator object Used to attach a calculator for calculating energies and atomic forces. info: dict of key-value pairs Dictionary of key-value pairs with additional information about the system. The following keys may be used by ase: - spacegroup: Spacegroup instance - unit_cell: 'conventional' | 'primitive' | int | 3 ints - adsorbate_info: Items in the info attribute survives copy and slicing and can be stored in and retrieved from trajectory files given that the key is a string, the value is JSON-compatible and, if the value is a user-defined object, its base class is importable. One should not make any assumptions about the existence of keys. Examples: These three are equivalent: >>> d = 1.104 # N2 bondlength >>> a = Atoms('N2', [(0, 0, 0), (0, 0, d)]) >>> a = Atoms(numbers=[7, 7], positions=[(0, 0, 0), (0, 0, d)]) >>> a = Atoms([Atom('N', (0, 0, 0)), Atom('N', (0, 0, d))]) FCC gold: >>> a = 4.05 # Gold lattice constant >>> b = a / 2 >>> fcc = Atoms('Au', ... cell=[(0, b, b), (b, 0, b), (b, b, 0)], ... pbc=True) Hydrogen wire: >>> d = 0.9 # H-H distance >>> L = 7.0 >>> h = Atoms('H', positions=[(0, L / 2, L / 2)], ... cell=(d, L, L), ... pbc=(1, 0, 0)) """ def __init__(self, symbols=None, positions=None, numbers=None, tags=None, momenta=None, masses=None, magmoms=None, charges=None, scaled_positions=None, cell=None, pbc=None, celldisp=None, constraint=None, calculator=None, info=None): atoms = None if hasattr(symbols, 'get_positions'): atoms = symbols symbols = None elif (isinstance(symbols, (list, tuple)) and len(symbols) > 0 and isinstance(symbols[0], Atom)): # Get data from a list or tuple of Atom objects: data = [[atom.get_raw(name) for atom in symbols] for name in ['position', 'number', 'tag', 'momentum', 'mass', 'magmom', 'charge']] atoms = self.__class__(None, *data) symbols = None if atoms is not None: # Get data from another Atoms object: if scaled_positions is not None: raise NotImplementedError if symbols is None and numbers is None: numbers = atoms.get_atomic_numbers() if positions is None: positions = atoms.get_positions() if tags is None and atoms.has('tags'): tags = atoms.get_tags() if momenta is None and atoms.has('momenta'): momenta = atoms.get_momenta() if magmoms is None and atoms.has('magmoms'): magmoms = atoms.get_initial_magnetic_moments() if masses is None and atoms.has('masses'): masses = atoms.get_masses() if charges is None and atoms.has('charges'): charges = atoms.get_initial_charges() if cell is None: cell = atoms.get_cell() if celldisp is None: celldisp = atoms.get_celldisp() if pbc is None: pbc = atoms.get_pbc() if constraint is None: constraint = [c.copy() for c in atoms.constraints] if calculator is None: calculator = atoms.get_calculator() if info is None: info = copy.deepcopy(atoms.info) self.arrays = {} if symbols is None: if numbers is None: if positions is not None: natoms = len(positions) elif scaled_positions is not None: natoms = len(scaled_positions) else: natoms = 0 numbers = np.zeros(natoms, int) self.new_array('numbers', numbers, int) else: if numbers is not None: raise ValueError( 'Use only one of "symbols" and "numbers".') else: self.new_array('numbers', symbols2numbers(symbols), int) if cell is None: cell = np.eye(3) self.set_cell(cell) if celldisp is None: celldisp = np.zeros(shape=(3, 1)) self.set_celldisp(celldisp) if positions is None: if scaled_positions is None: positions = np.zeros((len(self.arrays['numbers']), 3)) else: positions = np.dot(scaled_positions, self._cell) else: if scaled_positions is not None: raise RuntimeError('Both scaled and cartesian positions set!') self.new_array('positions', positions, float, (3,)) self.set_constraint(constraint) self.set_tags(default(tags, 0)) self.set_masses(default(masses, None)) self.set_initial_magnetic_moments(default(magmoms, 0.0)) self.set_initial_charges(default(charges, 0.0)) if pbc is None: pbc = False self.set_pbc(pbc) self.set_momenta(default(momenta, (0.0, 0.0, 0.0))) if info is None: self.info = {} else: self.info = dict(info) self.adsorbate_info = {} self.set_calculator(calculator) def set_calculator(self, calc=None): """Attach calculator object.""" self._calc = calc if hasattr(calc, 'set_atoms'): calc.set_atoms(self) def get_calculator(self): """Get currently attached calculator object.""" return self._calc def _del_calculator(self): self._calc = None calc = property(get_calculator, set_calculator, _del_calculator, doc='Calculator object.') def set_constraint(self, constraint=None): """Apply one or more constrains. The *constraint* argument must be one constraint object or a list of constraint objects.""" if constraint is None: self._constraints = [] else: if isinstance(constraint, (list, tuple)): self._constraints = constraint else: self._constraints = [constraint] def _get_constraints(self): return self._constraints def _del_constraints(self): self._constraints = [] constraints = property(_get_constraints, set_constraint, _del_constraints, 'Constraints of the atoms.') def set_cell(self, cell, scale_atoms=False, fix=None): """Set unit cell vectors. Parameters: cell: 3x3 matrix or length 3 or 6 vector Unit cell. A 3x3 matrix (the three unit cell vectors) or just three numbers for an orthorhombic cell. Another option is 6 numbers, which describes unit cell with lengths of unit cell vectors and with angles between them (in degrees), in following order: [len(a), len(b), len(c), angle(b,c), angle(a,c), angle(a,b)]. First vector will lie in x-direction, second in xy-plane, and the third one in z-positive subspace. scale_atoms: bool Fix atomic positions or move atoms with the unit cell? Default behavior is to *not* move the atoms (scale_atoms=False). Examples: Two equivalent ways to define an orthorhombic cell: >>> atoms = Atoms('He') >>> a, b, c = 7, 7.5, 8 >>> atoms.set_cell([a, b, c]) >>> atoms.set_cell([(a, 0, 0), (0, b, 0), (0, 0, c)]) FCC unit cell: >>> atoms.set_cell([(0, b, b), (b, 0, b), (b, b, 0)]) Hexagonal unit cell: >>> atoms.set_cell([a, a, c, 90, 90, 120]) Rhombohedral unit cell: >>> alpha = 77 >>> atoms.set_cell([a, a, a, alpha, alpha, alpha]) """ if fix is not None: raise TypeError('Please use scale_atoms=%s' % (not fix)) cell = np.array(cell, float) if cell.shape == (3,): cell = np.diag(cell) elif cell.shape == (6,): cell = cellpar_to_cell(cell) elif cell.shape != (3, 3): raise ValueError('Cell must be length 3 sequence, length 6 ' 'sequence or 3x3 matrix!') if scale_atoms: M = np.linalg.solve(self._cell, cell) self.arrays['positions'][:] = np.dot(self.arrays['positions'], M) self._cell = cell def set_celldisp(self, celldisp): """Set the unit cell displacement vectors.""" celldisp = np.array(celldisp, float) self._celldisp = celldisp def get_celldisp(self): """Get the unit cell displacement vectors.""" return self._celldisp.copy() def get_cell(self): """Get the three unit cell vectors as a 3x3 ndarray.""" return self._cell.copy() def get_cell_lengths_and_angles(self): """Get unit cell parameters. Sequence of 6 numbers. First three are unit cell vector lengths and second three are angles between them:: [len(a), len(b), len(c), angle(a,b), angle(a,c), angle(b,c)] in degrees. """ return cell_to_cellpar(self._cell) def get_reciprocal_cell(self): """Get the three reciprocal lattice vectors as a 3x3 ndarray. Note that the commonly used factor of 2 pi for Fourier transforms is not included here.""" rec_unit_cell = np.linalg.inv(self.get_cell()).transpose() return rec_unit_cell def set_pbc(self, pbc): """Set periodic boundary condition flags.""" if isinstance(pbc, int): pbc = (pbc,) * 3 self._pbc = np.array(pbc, bool) def get_pbc(self): """Get periodic boundary condition flags.""" return self._pbc.copy() def new_array(self, name, a, dtype=None, shape=None): """Add new array. If *shape* is not *None*, the shape of *a* will be checked.""" if dtype is not None: a = np.array(a, dtype) if len(a) == 0 and shape is not None: a.shape = (-1,) + shape else: a = a.copy() if name in self.arrays: raise RuntimeError for b in self.arrays.values(): if len(a) != len(b): raise ValueError('Array has wrong length: %d != %d.' % (len(a), len(b))) break if shape is not None and a.shape[1:] != shape: raise ValueError('Array has wrong shape %s != %s.' % (a.shape, (a.shape[0:1] + shape))) self.arrays[name] = a def get_array(self, name, copy=True): """Get an array. Returns a copy unless the optional argument copy is false. """ if copy: return self.arrays[name].copy() else: return self.arrays[name] def set_array(self, name, a, dtype=None, shape=None): """Update array. If *shape* is not *None*, the shape of *a* will be checked. If *a* is *None*, then the array is deleted.""" b = self.arrays.get(name) if b is None: if a is not None: self.new_array(name, a, dtype, shape) else: if a is None: del self.arrays[name] else: a = np.asarray(a) if a.shape != b.shape: raise ValueError('Array has wrong shape %s != %s.' % (a.shape, b.shape)) b[:] = a def has(self, name): """Check for existence of array. name must be one of: 'tags', 'momenta', 'masses', 'magmoms', 'charges'.""" return name in self.arrays def set_atomic_numbers(self, numbers): """Set atomic numbers.""" self.set_array('numbers', numbers, int, ()) def get_atomic_numbers(self): """Get integer array of atomic numbers.""" return self.arrays['numbers'].copy() def get_chemical_symbols(self): """Get list of chemical symbol strings.""" return [chemical_symbols[Z] for Z in self.arrays['numbers']] def set_chemical_symbols(self, symbols): """Set chemical symbols.""" self.set_array('numbers', symbols2numbers(symbols), int, ()) def get_chemical_formula(self, mode='hill'): """Get the chemial formula as a string based on the chemical symbols. Parameters: mode: str There are three different modes available: 'all': The list of chemical symbols are contracted to at string, e.g. ['C', 'H', 'H', 'H', 'O', 'H'] becomes 'CHHHOH'. 'reduce': The same as 'all' where repeated elements are contracted to a single symbol and a number, e.g. 'CHHHOCHHH' is reduced to 'CH3OCH3'. 'hill': The list of chemical symbols are contracted to a string following the Hill notation (alphabetical order with C and H first), e.g. 'CHHHOCHHH' is reduced to 'C2H6O' and 'SOOHOHO' to 'H2O4S'. This is default. """ if len(self) == 0: return '' if mode == 'reduce': numbers = self.get_atomic_numbers() n = len(numbers) changes = np.concatenate(([0], np.arange(1, n)[numbers[1:] != numbers[:-1]])) symbols = [chemical_symbols[e] for e in numbers[changes]] counts = np.append(changes[1:], n) - changes elif mode == 'hill': numbers = self.get_atomic_numbers() elements = np.unique(numbers) symbols = np.array([chemical_symbols[e] for e in elements]) counts = np.array([(numbers == e).sum() for e in elements]) ind = symbols.argsort() symbols = symbols[ind] counts = counts[ind] if 'H' in symbols: i = np.arange(len(symbols))[symbols == 'H'] symbols = np.insert(np.delete(symbols, i), 0, symbols[i]) counts = np.insert(np.delete(counts, i), 0, counts[i]) if 'C' in symbols: i = np.arange(len(symbols))[symbols == 'C'] symbols = np.insert(np.delete(symbols, i), 0, symbols[i]) counts = np.insert(np.delete(counts, i), 0, counts[i]) elif mode == 'all': numbers = self.get_atomic_numbers() symbols = [chemical_symbols[n] for n in numbers] counts = [1] * len(numbers) else: raise ValueError("Use mode = 'all', 'reduce' or 'hill'.") formula = '' for s, c in zip(symbols, counts): formula += s if c > 1: formula += str(c) return formula def set_tags(self, tags): """Set tags for all atoms. If only one tag is supplied, it is applied to all atoms.""" if isinstance(tags, int): tags = [tags] * len(self) self.set_array('tags', tags, int, ()) def get_tags(self): """Get integer array of tags.""" if 'tags' in self.arrays: return self.arrays['tags'].copy() else: return np.zeros(len(self), int) def set_momenta(self, momenta, apply_constraint=True): """Set momenta.""" if (apply_constraint and len(self.constraints) > 0 and momenta is not None): momenta = np.array(momenta) # modify a copy for constraint in self.constraints: if hasattr(constraint, 'adjust_momenta'): constraint.adjust_momenta(self, momenta) self.set_array('momenta', momenta, float, (3,)) def set_velocities(self, velocities): """Set the momenta by specifying the velocities.""" self.set_momenta(self.get_masses()[:, np.newaxis] * velocities) def get_momenta(self): """Get array of momenta.""" if 'momenta' in self.arrays: return self.arrays['momenta'].copy() else: return np.zeros((len(self), 3)) def set_masses(self, masses='defaults'): """Set atomic masses. The array masses should contain a list of masses. In case the masses argument is not given or for those elements of the masses list that are None, standard values are set.""" if isinstance(masses, str) and masses == 'defaults': masses = atomic_masses[self.arrays['numbers']] elif isinstance(masses, (list, tuple)): newmasses = [] for m, Z in zip(masses, self.arrays['numbers']): if m is None: newmasses.append(atomic_masses[Z]) else: newmasses.append(m) masses = newmasses self.set_array('masses', masses, float, ()) def get_masses(self): """Get array of masses.""" if 'masses' in self.arrays: return self.arrays['masses'].copy() else: return atomic_masses[self.arrays['numbers']] def set_initial_magnetic_moments(self, magmoms=None): """Set the initial magnetic moments. Use either one or three numbers for every atom (collinear or non-collinear spins).""" if magmoms is None: self.set_array('magmoms', None) else: magmoms = np.asarray(magmoms) self.set_array('magmoms', magmoms, float, magmoms.shape[1:]) def get_initial_magnetic_moments(self): """Get array of initial magnetic moments.""" if 'magmoms' in self.arrays: return self.arrays['magmoms'].copy() else: return np.zeros(len(self)) def get_magnetic_moments(self): """Get calculated local magnetic moments.""" if self._calc is None: raise RuntimeError('Atoms object has no calculator.') return self._calc.get_magnetic_moments(self) def get_magnetic_moment(self): """Get calculated total magnetic moment.""" if self._calc is None: raise RuntimeError('Atoms object has no calculator.') return self._calc.get_magnetic_moment(self) def set_initial_charges(self, charges=None): """Set the initial charges.""" if charges is None: self.set_array('charges', None) else: self.set_array('charges', charges, float, ()) def get_initial_charges(self): """Get array of initial charges.""" if 'charges' in self.arrays: return self.arrays['charges'].copy() else: return np.zeros(len(self)) def get_charges(self): """Get calculated charges.""" if self._calc is None: raise RuntimeError('Atoms object has no calculator.') try: return self._calc.get_charges(self) except AttributeError: raise NotImplementedError def set_positions(self, newpositions): """Set positions, honoring any constraints.""" if self.constraints: newpositions = np.array(newpositions, float) for constraint in self.constraints: constraint.adjust_positions(self, newpositions) self.set_array('positions', newpositions, shape=(3,)) def get_positions(self, wrap=False): """Get array of positions. If wrap==True, wraps atoms back into unit cell. """ if wrap: scaled = self.get_scaled_positions() return np.dot(scaled, self._cell) else: return self.arrays['positions'].copy() def get_potential_energy(self, force_consistent=False, apply_constraint=True): """Calculate potential energy. Ask the attached calculator to calculate the potential energy and apply constraints. Use *apply_constraint=False* to get the raw forces. When supported by the calculator, either the energy extrapolated to zero Kelvin or the energy consistent with the forces (the free energy) can be returned. """ if self._calc is None: raise RuntimeError('Atoms object has no calculator.') if force_consistent: energy = self._calc.get_potential_energy( self, force_consistent=force_consistent) else: energy = self._calc.get_potential_energy(self) if apply_constraint: for constraint in self.constraints: if hasattr(constraint, 'adjust_potential_energy'): energy += constraint.adjust_potential_energy(self) return energy def get_potential_energies(self): """Calculate the potential energies of all the atoms. Only available with calculators supporting per-atom energies (e.g. classical potentials). """ if self._calc is None: raise RuntimeError('Atoms object has no calculator.') return self._calc.get_potential_energies(self) def get_kinetic_energy(self): """Get the kinetic energy.""" momenta = self.arrays.get('momenta') if momenta is None: return 0.0 return 0.5 * np.vdot(momenta, self.get_velocities()) def get_velocities(self): """Get array of velocities.""" momenta = self.arrays.get('momenta') if momenta is None: return None m = self.arrays.get('masses') if m is None: m = atomic_masses[self.arrays['numbers']] return momenta / m.reshape(-1, 1) def get_total_energy(self): """Get the total energy - potential plus kinetic energy.""" return self.get_potential_energy() + self.get_kinetic_energy() def get_forces(self, apply_constraint=True, md=False): """Calculate atomic forces. Ask the attached calculator to calculate the forces and apply constraints. Use *apply_constraint=False* to get the raw forces. For molecular dynamics (md=True) we don't apply the constraint to the forces but to the momenta.""" if self._calc is None: raise RuntimeError('Atoms object has no calculator.') forces = self._calc.get_forces(self) if apply_constraint: # We need a special md flag here because for MD we want # to skip real constraints but include special "constraints" # Like Hookean. for constraint in self.constraints: if not md or hasattr(constraint, 'adjust_potential_energy'): constraint.adjust_forces(self, forces) return forces def get_stress(self, voigt=True): """Calculate stress tensor. Returns an array of the six independent components of the symmetric stress tensor, in the traditional Voigt order (xx, yy, zz, yz, xz, xy) or as a 3x3 matrix. Default is Voigt order. """ if self._calc is None: raise RuntimeError('Atoms object has no calculator.') stress = self._calc.get_stress(self) shape = stress.shape if shape == (3, 3): warnings.warn('Converting 3x3 stress tensor from %s ' % self._calc.__class__.__name__ + 'calculator to the required Voigt form.') stress = np.array([stress[0, 0], stress[1, 1], stress[2, 2], stress[1, 2], stress[0, 2], stress[0, 1]]) else: assert shape == (6,) if voigt: return stress else: xx, yy, zz, yz, xz, xy = stress return np.array([(xx, xy, xz), (xy, yy, yz), (xz, yz, zz)]) def get_stresses(self): """Calculate the stress-tensor of all the atoms. Only available with calculators supporting per-atom energies and stresses (e.g. classical potentials). Even for such calculators there is a certain arbitrariness in defining per-atom stresses. """ if self._calc is None: raise RuntimeError('Atoms object has no calculator.') return self._calc.get_stresses(self) def get_dipole_moment(self): """Calculate the electric dipole moment for the atoms object. Only available for calculators which has a get_dipole_moment() method.""" if self._calc is None: raise RuntimeError('Atoms object has no calculator.') return self._calc.get_dipole_moment(self) def copy(self): """Return a copy.""" atoms = self.__class__(cell=self._cell, pbc=self._pbc, info=self.info) atoms.arrays = {} for name, a in self.arrays.items(): atoms.arrays[name] = a.copy() atoms.constraints = copy.deepcopy(self.constraints) atoms.adsorbate_info = copy.deepcopy(self.adsorbate_info) return atoms def __len__(self): return len(self.arrays['positions']) def get_number_of_atoms(self): """Returns the global number of atoms in a distributed-atoms parallel simulation. DO NOT USE UNLESS YOU KNOW WHAT YOU ARE DOING! Equivalent to len(atoms) in the standard ASE Atoms class. You should normally use len(atoms) instead. This function's only purpose is to make compatibility between ASE and Asap easier to maintain by having a few places in ASE use this function instead. It is typically only when counting the global number of degrees of freedom or in similar situations. """ return len(self) def __repr__(self): tokens = [] N = len(self) if N <= 60: symbols = self.get_chemical_formula('reduce') else: symbols = self.get_chemical_formula('hill') tokens.append("symbols='{0}'".format(symbols)) tokens.append('pbc={0}'.format(self._pbc.tolist())) if (self._cell - np.diag(self._cell.diagonal())).any(): cell = self._cell.tolist() else: cell = self._cell.diagonal().tolist() tokens.append('cell={0}'.format(cell)) for name in sorted(self.arrays): if name == 'numbers': continue tokens.append('{0}=...'.format(name)) if self.constraints: if len(self.constraints) == 1: constraint = self.constraints[0] else: constraint = self.constraints tokens.append('constraint={0}'.format(repr(constraint))) if self._calc is not None: tokens.append('calculator={0}(...)' .format(self._calc.__class__.__name__)) return '{0}({1})'.format(self.__class__.__name__, ', '.join(tokens)) def __add__(self, other): atoms = self.copy() atoms += other return atoms def extend(self, other): """Extend atoms object by appending atoms from *other*.""" if isinstance(other, Atom): other = self.__class__([other]) n1 = len(self) n2 = len(other) for name, a1 in self.arrays.items(): a = np.zeros((n1 + n2,) + a1.shape[1:], a1.dtype) a[:n1] = a1 if name == 'masses': a2 = other.get_masses() else: a2 = other.arrays.get(name) if a2 is not None: a[n1:] = a2 self.arrays[name] = a for name, a2 in other.arrays.items(): if name in self.arrays: continue a = np.empty((n1 + n2,) + a2.shape[1:], a2.dtype) a[n1:] = a2 if name == 'masses': a[:n1] = self.get_masses()[:n1] else: a[:n1] = 0 self.set_array(name, a) return self __iadd__ = extend def append(self, atom): """Append atom to end.""" self.extend(self.__class__([atom])) def __getitem__(self, i): """Return a subset of the atoms. i -- scalar integer, list of integers, or slice object describing which atoms to return. If i is a scalar, return an Atom object. If i is a list or a slice, return an Atoms object with the same cell, pbc, and other associated info as the original Atoms object. The indices of the constraints will be shuffled so that they match the indexing in the subset returned. """ if isinstance(i, numbers.Integral): natoms = len(self) if i < -natoms or i >= natoms: raise IndexError('Index out of range.') return Atom(atoms=self, index=i) import copy from ase.constraints import FixConstraint, FixBondLengths atoms = self.__class__(cell=self._cell, pbc=self._pbc, info=self.info) # TODO: Do we need to shuffle indices in adsorbate_info too? atoms.adsorbate_info = self.adsorbate_info atoms.arrays = {} for name, a in self.arrays.items(): atoms.arrays[name] = a[i].copy() # Constraints need to be deepcopied, since we need to shuffle # the indices atoms.constraints = copy.deepcopy(self.constraints) condel = [] for con in atoms.constraints: if isinstance(con, (FixConstraint, FixBondLengths)): try: con.index_shuffle(self, i) except IndexError: condel.append(con) for con in condel: atoms.constraints.remove(con) return atoms def __delitem__(self, i): from ase.constraints import FixAtoms for c in self._constraints: if not isinstance(c, FixAtoms): raise RuntimeError('Remove constraint using set_constraint() ' 'before deleting atoms.') if len(self._constraints) > 0: n = len(self) i = np.arange(n)[i] if isinstance(i, int): i = [i] constraints = [] for c in self._constraints: c = c.delete_atoms(i, n) if c is not None: constraints.append(c) self.constraints = constraints mask = np.ones(len(self), bool) mask[i] = False for name, a in self.arrays.items(): self.arrays[name] = a[mask] def pop(self, i=-1): """Remove and return atom at index *i* (default last).""" atom = self[i] atom.cut_reference_to_atoms() del self[i] return atom def __imul__(self, m): """In-place repeat of atoms.""" if isinstance(m, int): m = (m, m, m) M = np.product(m) n = len(self) for name, a in self.arrays.items(): self.arrays[name] = np.tile(a, (M,) + (1,) * (len(a.shape) - 1)) positions = self.arrays['positions'] i0 = 0 for m0 in range(m[0]): for m1 in range(m[1]): for m2 in range(m[2]): i1 = i0 + n positions[i0:i1] += np.dot((m0, m1, m2), self._cell) i0 = i1 if self.constraints is not None: self.constraints = [c.repeat(m, n) for c in self.constraints] self._cell = np.array([m[c] * self._cell[c] for c in range(3)]) return self def repeat(self, rep): """Create new repeated atoms object. The *rep* argument should be a sequence of three positive integers like *(2,3,1)* or a single integer (*r*) equivalent to *(r,r,r)*.""" atoms = self.copy() atoms *= rep return atoms __mul__ = repeat def translate(self, displacement): """Translate atomic positions. The displacement argument can be a float an xyz vector or an nx3 array (where n is the number of atoms).""" self.arrays['positions'] += np.array(displacement) def center(self, vacuum=None, axis=(0, 1, 2), about=None): """Center atoms in unit cell. Centers the atoms in the unit cell, so there is the same amount of vacuum on all sides. vacuum: float (default: None) If specified adjust the amount of vacuum when centering. If vacuum=10.0 there will thus be 10 Angstrom of vacuum on each side. axis: int or sequence of ints Axis or axes to act on. Default: Act on all axes. about: float or array (default: None) If specified, center the atoms about . I.e., about=(0., 0., 0.) (or just "about=0.", interpreted identically), to center about the origin. """ # Find the orientations of the faces of the unit cell c = self.get_cell() dirs = np.zeros_like(c) for i in range(3): dirs[i] = np.cross(c[i - 1], c[i - 2]) dirs[i] /= np.sqrt(np.dot(dirs[i], dirs[i])) # normalize if np.dot(dirs[i], c[i]) < 0.0: dirs[i] *= -1 # Now, decide how much each basis vector should be made longer if isinstance(axis, int): axes = (axis,) else: axes = axis p = self.arrays['positions'] longer = np.zeros(3) shift = np.zeros(3) for i in axes: p0 = np.dot(p, dirs[i]).min() p1 = np.dot(p, dirs[i]).max() height = np.dot(c[i], dirs[i]) if vacuum is not None: lng = (p1 - p0 + 2 * vacuum) - height else: lng = 0.0 # Do not change unit cell size! top = lng + height - p1 shf = 0.5 * (top - p0) cosphi = np.dot(c[i], dirs[i]) / np.sqrt(np.dot(c[i], c[i])) longer[i] = lng / cosphi shift[i] = shf / cosphi # Now, do it! translation = np.zeros(3) for i in axes: nowlen = np.sqrt(np.dot(c[i], c[i])) self._cell[i] *= 1 + longer[i] / nowlen translation += shift[i] * c[i] / nowlen self.arrays['positions'] += translation # Optionally, translate to center about a point in space. if about is not None: for vector in self.cell: self.positions -= vector / 2.0 self.positions += about def get_center_of_mass(self, scaled=False): """Get the center of mass. If scaled=True the center of mass in scaled coordinates is returned.""" m = self.get_masses() com = np.dot(m, self.arrays['positions']) / m.sum() if scaled: return np.linalg.solve(self._cell.T, com) else: return com def get_moments_of_inertia(self, vectors=False): """Get the moments of inertia along the principal axes. The three principal moments of inertia are computed from the eigenvalues of the symmetric inertial tensor. Periodic boundary conditions are ignored. Units of the moments of inertia are amu*angstrom**2. """ com = self.get_center_of_mass() positions = self.get_positions() positions -= com # translate center of mass to origin masses = self.get_masses() # Initialize elements of the inertial tensor I11 = I22 = I33 = I12 = I13 = I23 = 0.0 for i in range(len(self)): x, y, z = positions[i] m = masses[i] I11 += m * (y ** 2 + z ** 2) I22 += m * (x ** 2 + z ** 2) I33 += m * (x ** 2 + y ** 2) I12 += -m * x * y I13 += -m * x * z I23 += -m * y * z I = np.array([[I11, I12, I13], [I12, I22, I23], [I13, I23, I33]]) evals, evecs = np.linalg.eigh(I) if vectors: return evals, evecs.transpose() else: return evals def get_angular_momentum(self): """Get total angular momentum with respect to the center of mass.""" com = self.get_center_of_mass() positions = self.get_positions() positions -= com # translate center of mass to origin return np.cross(positions, self.get_momenta()).sum(0) def rotate(self, v, a=None, center=(0, 0, 0), rotate_cell=False): """Rotate atoms based on a vector and an angle, or two vectors. Parameters: v: Vector to rotate the atoms around. Vectors can be given as strings: 'x', '-x', 'y', ... . a = None: Angle that the atoms is rotated around the vecor 'v'. If an angle is not specified, the length of 'v' is used as the angle (default). The angle can also be a vector and then 'v' is rotated into 'a'. center = (0, 0, 0): The center is kept fixed under the rotation. Use 'COM' to fix the center of mass, 'COP' to fix the center of positions or 'COU' to fix the center of cell. rotate_cell = False: If true the cell is also rotated. Examples: Rotate 90 degrees around the z-axis, so that the x-axis is rotated into the y-axis: >>> from math import pi >>> a = pi / 2 >>> atoms = Atoms() >>> atoms.rotate('z', a) >>> atoms.rotate((0, 0, 1), a) >>> atoms.rotate('-z', -a) >>> atoms.rotate((0, 0, a)) >>> atoms.rotate('x', 'y') """ norm = np.linalg.norm v = string2vector(v) if a is None: a = norm(v) if isinstance(a, (float, int)): v /= norm(v) c = cos(a) s = sin(a) else: v2 = string2vector(a) v /= norm(v) v2 /= norm(v2) c = np.dot(v, v2) v = np.cross(v, v2) s = norm(v) # In case *v* and *a* are parallel, np.cross(v, v2) vanish # and can't be used as a rotation axis. However, in this # case any rotation axis perpendicular to v2 will do. eps = 1e-7 if s < eps: v = np.cross((0, 0, 1), v2) if norm(v) < eps: v = np.cross((1, 0, 0), v2) assert norm(v) >= eps elif s > 0: v /= s if isinstance(center, str): if center.lower() == 'com': center = self.get_center_of_mass() elif center.lower() == 'cop': center = self.get_positions().mean(axis=0) elif center.lower() == 'cou': center = self.get_cell().sum(axis=0) / 2 else: raise ValueError('Cannot interpret center') else: center = np.array(center) p = self.arrays['positions'] - center self.arrays['positions'][:] = (c * p - np.cross(p, s * v) + np.outer(np.dot(p, v), (1.0 - c) * v) + center) if rotate_cell: rotcell = self.get_cell() rotcell[:] = (c * rotcell - np.cross(rotcell, s * v) + np.outer(np.dot(rotcell, v), (1.0 - c) * v)) self.set_cell(rotcell) def rotate_euler(self, center=(0, 0, 0), phi=0.0, theta=0.0, psi=0.0): """Rotate atoms via Euler angles. See e.g http://mathworld.wolfram.com/EulerAngles.html for explanation. Parameters: center : The point to rotate about. A sequence of length 3 with the coordinates, or 'COM' to select the center of mass, 'COP' to select center of positions or 'COU' to select center of cell. phi : The 1st rotation angle around the z axis. theta : Rotation around the x axis. psi : 2nd rotation around the z axis. """ if isinstance(center, str): if center.lower() == 'com': center = self.get_center_of_mass() elif center.lower() == 'cop': center = self.get_positions().mean(axis=0) elif center.lower() == 'cou': center = self.get_cell().sum(axis=0) / 2 else: raise ValueError('Cannot interpret center') else: center = np.array(center) # First move the molecule to the origin In contrast to MATLAB, # numpy broadcasts the smaller array to the larger row-wise, # so there is no need to play with the Kronecker product. rcoords = self.positions - center # First Euler rotation about z in matrix form D = np.array(((cos(phi), sin(phi), 0.), (-sin(phi), cos(phi), 0.), (0., 0., 1.))) # Second Euler rotation about x: C = np.array(((1., 0., 0.), (0., cos(theta), sin(theta)), (0., -sin(theta), cos(theta)))) # Third Euler rotation, 2nd rotation about z: B = np.array(((cos(psi), sin(psi), 0.), (-sin(psi), cos(psi), 0.), (0., 0., 1.))) # Total Euler rotation A = np.dot(B, np.dot(C, D)) # Do the rotation rcoords = np.dot(A, np.transpose(rcoords)) # Move back to the rotation point self.positions = np.transpose(rcoords) + center def get_dihedral(self, list): """Calculate dihedral angle. Calculate dihedral angle between the vectors list[0]->list[1] and list[2]->list[3], where list contains the atomic indexes in question. """ # vector 0->1, 1->2, 2->3 and their normalized cross products: a = self.positions[list[1]] - self.positions[list[0]] b = self.positions[list[2]] - self.positions[list[1]] c = self.positions[list[3]] - self.positions[list[2]] bxa = np.cross(b, a) bxa /= np.linalg.norm(bxa) cxb = np.cross(c, b) cxb /= np.linalg.norm(cxb) angle = np.vdot(bxa, cxb) # check for numerical trouble due to finite precision: if angle < -1: angle = -1 if angle > 1: angle = 1 angle = np.arccos(angle) if np.vdot(bxa, c) > 0: angle = 2 * np.pi - angle return angle def _masked_rotate(self, center, axis, diff, mask): # do rotation of subgroup by copying it to temporary atoms object # and then rotating that # # recursive object definition might not be the most elegant thing, # more generally useful might be a rotation function with a mask? group = self.__class__() for i in range(len(self)): if mask[i]: group += self[i] group.translate(-center) group.rotate(axis, diff) group.translate(center) # set positions in original atoms object j = 0 for i in range(len(self)): if mask[i]: self.positions[i] = group[j].position j += 1 def set_dihedral(self, list, angle, mask=None, indices=None): """Set the dihedral angle between vectors list[0]->list[1] and list[2]->list[3] by changing the atom indexed by list[3] if mask is not None, all the atoms described in mask (read: the entire subgroup) are moved. Alternatively to the mask, the indices of the atoms to be rotated can be supplied. example: the following defines a very crude ethane-like molecule and twists one half of it by 30 degrees. >>> from math import pi >>> atoms = Atoms('HHCCHH', [[-1, 1, 0], [-1, -1, 0], [0, 0, 0], ... [1, 0, 0], [2, 1, 0], [2, -1, 0]]) >>> atoms.set_dihedral([1, 2, 3, 4], 7 * pi / 6, ... mask=[0, 0, 0, 1, 1, 1]) """ # if not provided, set mask to the last atom in the # dihedral description if mask is None and indices is None: mask = np.zeros(len(self)) mask[list[3]] = 1 elif indices: mask = [index in indices for index in range(len(self))] # compute necessary in dihedral change, from current value current = self.get_dihedral(list) diff = angle - current axis = self.positions[list[2]] - self.positions[list[1]] center = self.positions[list[2]] self._masked_rotate(center, axis, diff, mask) def rotate_dihedral(self, list, angle, mask=None): """Rotate dihedral angle. Complementing the two routines above: rotate a group by a predefined dihedral angle, starting from its current configuration """ start = self.get_dihedral(list) self.set_dihedral(list, angle + start, mask) def get_angle(self, list): """Get angle formed by three atoms. calculate angle between the vectors list[1]->list[0] and list[1]->list[2], where list contains the atomic indexes in question.""" # normalized vector 1->0, 1->2: v10 = self.positions[list[0]] - self.positions[list[1]] v12 = self.positions[list[2]] - self.positions[list[1]] v10 /= np.linalg.norm(v10) v12 /= np.linalg.norm(v12) angle = np.vdot(v10, v12) angle = np.arccos(angle) return angle def set_angle(self, list, angle, mask=None): """Set angle formed by three atoms. Sets the angle between vectors list[1]->list[0] and list[1]->list[2]. Same usage as in set_dihedral.""" # If not provided, set mask to the last atom in the angle description if mask is None: mask = np.zeros(len(self)) mask[list[2]] = 1 # Compute necessary in angle change, from current value current = self.get_angle(list) diff = angle - current # Do rotation of subgroup by copying it to temporary atoms object and # then rotating that v10 = self.positions[list[0]] - self.positions[list[1]] v12 = self.positions[list[2]] - self.positions[list[1]] v10 /= np.linalg.norm(v10) v12 /= np.linalg.norm(v12) axis = np.cross(v10, v12) center = self.positions[list[1]] self._masked_rotate(center, axis, diff, mask) def rattle(self, stdev=0.001, seed=42): """Randomly displace atoms. This method adds random displacements to the atomic positions, taking a possible constraint into account. The random numbers are drawn from a normal distribution of standard deviation stdev. For a parallel calculation, it is important to use the same seed on all processors! """ rs = np.random.RandomState(seed) positions = self.arrays['positions'] self.set_positions(positions + rs.normal(scale=stdev, size=positions.shape)) def get_distance(self, a0, a1, mic=False, vector=False): """Return distance between two atoms. Use mic=True to use the Minimum Image Convention. vector=True gives the distance vector (from a0 to a1). """ R = self.arrays['positions'] D = np.array([R[a1] - R[a0]]) if mic: D, D_len = find_mic(D, self._cell, self._pbc) else: D_len = np.array([np.sqrt((D**2).sum())]) if vector: return D[0] return D_len[0] def get_distances(self, a, indices, mic=False, vector=False): """Return distances of atom No.i with a list of atoms. Use mic=True to use the Minimum Image Convention. vector=True gives the distance vector (from a to self[indices]). """ R = self.arrays['positions'] D = R[indices] - R[a] if mic: D, D_len = find_mic(D, self._cell, self._pbc) else: D_len = np.sqrt((D**2).sum(1)) if vector: return D return D_len def get_all_distances(self, mic=False): """Return distances of all of the atoms with all of the atoms. Use mic=True to use the Minimum Image Convention. """ L = len(self) R = self.arrays['positions'] D = [] for i in range(L - 1): D.append(R[i + 1:] - R[i]) D = np.concatenate(D) if mic: D, D_len = find_mic(D, self._cell, self._pbc) else: D_len = np.sqrt((D**2).sum(1)) results = np.zeros((L, L), dtype=float) start = 0 for i in range(L - 1): results[i, i + 1:] = D_len[start:start + L - i - 1] start += L - i - 1 return results + results.T def set_distance(self, a0, a1, distance, fix=0.5, mic=False): """Set the distance between two atoms. Set the distance between atoms *a0* and *a1* to *distance*. By default, the center of the two atoms will be fixed. Use *fix=0* to fix the first atom, *fix=1* to fix the second atom and *fix=0.5* (default) to fix the center of the bond.""" R = self.arrays['positions'] D = np.array([R[a1] - R[a0]]) if mic: D, D_len = find_mic(D, self._cell, self._pbc) else: D_len = np.array([np.sqrt((D**2).sum())]) x = 1.0 - distance / D_len[0] R[a0] += (x * fix) * D[0] R[a1] -= (x * (1.0 - fix)) * D[0] def get_scaled_positions(self, wrap=True): """Get positions relative to unit cell. If wrap is True, atoms outside the unit cell will be wrapped into the cell in those directions with periodic boundary conditions so that the scaled coordinates are between zero and one.""" fractional = np.linalg.solve(self.cell.T, self.positions.T).T if wrap: for i, periodic in enumerate(self.pbc): if periodic: # Yes, we need to do it twice. # See the scaled_positions.py test. fractional[:, i] %= 1.0 fractional[:, i] %= 1.0 return fractional def set_scaled_positions(self, scaled): """Set positions relative to unit cell.""" self.arrays['positions'][:] = np.dot(scaled, self._cell) def wrap(self, center=(0.5, 0.5, 0.5), pbc=None, eps=1e-7): """Wrap positions to unit cell. Parameters: center: three float The positons in fractional coordinates that the new positions will be nearest possible to. pbc: one or 3 bool For each axis in the unit cell decides whether the positions will be moved along this axis. By default, the boundary conditions of the Atoms object will be used. eps: float Small number to prevent slightly negative coordinates from being wrapped. See also the :func:`ase.geometry.wrap_positions` function. Example: >>> a = Atoms('H', ... [[-0.1, 1.01, -0.5]], ... cell=[[1, 0, 0], [0, 1, 0], [0, 0, 4]], ... pbc=[1, 1, 0]) >>> a.wrap() >>> a.positions array([[ 0.9 , 0.01, -0.5 ]]) """ if pbc is None: pbc = self.pbc self.positions[:] = wrap_positions(self.positions, self.cell, pbc, center, eps) def get_temperature(self): """Get the temperature in Kelvin.""" dof = len(self) * 3 for constraint in self._constraints: dof -= constraint.removed_dof ekin = self.get_kinetic_energy() return 2 * ekin / (dof * units.kB) def __eq__(self, other): """Check for identity of two atoms objects. Identity means: same positions, atomic numbers, unit cell and periodic boundary conditions.""" try: a = self.arrays b = other.arrays return (len(self) == len(other) and (a['positions'] == b['positions']).all() and (a['numbers'] == b['numbers']).all() and (self._cell == other.cell).all() and (self._pbc == other.pbc).all()) except AttributeError: return NotImplemented def __ne__(self, other): """Check if two atoms objects are not equal. Any differences in positions, atomic numbers, unit cell or periodic boundary condtions make atoms objects not equal. """ eq = self.__eq__(other) if eq is NotImplemented: return eq else: return not eq __hash__ = None def get_volume(self): """Get volume of unit cell.""" return abs(np.linalg.det(self._cell)) def _get_positions(self): """Return reference to positions-array for in-place manipulations.""" return self.arrays['positions'] def _set_positions(self, pos): """Set positions directly, bypassing constraints.""" self.arrays['positions'][:] = pos positions = property(_get_positions, _set_positions, doc='Attribute for direct ' + 'manipulation of the positions.') def _get_atomic_numbers(self): """Return reference to atomic numbers for in-place manipulations.""" return self.arrays['numbers'] numbers = property(_get_atomic_numbers, set_atomic_numbers, doc='Attribute for direct ' + 'manipulation of the atomic numbers.') def _get_cell(self): """Return reference to unit cell for in-place manipulations.""" return self._cell cell = property(_get_cell, set_cell, doc='Attribute for direct ' + 'manipulation of the unit cell.') def _get_pbc(self): """Return reference to pbc-flags for in-place manipulations.""" return self._pbc pbc = property(_get_pbc, set_pbc, doc='Attribute for direct manipulation ' + 'of the periodic boundary condition flags.') def write(self, filename, format=None, **kwargs): """Write atoms object to a file. see ase.io.write for formats. kwargs are passed to ase.io.write. """ from ase.io import write write(filename, self, format, **kwargs) def edit(self): """Modify atoms interactively through ase-gui viewer. Conflicts leading to undesirable behaviour might arise when matplotlib has been pre-imported with certain incompatible backends and while trying to use the plot feature inside the interactive ag. To circumvent, please set matplotlib.use('gtk') before calling this method. """ from ase.gui.images import Images from ase.gui.gui import GUI images = Images([self]) gui = GUI(images) gui.run() # use atoms returned from gui: # (1) delete all currently available atoms self.set_constraint() for z in range(len(self)): self.pop() edited_atoms = gui.images.get_atoms(0) # (2) extract atoms from edit session self.extend(edited_atoms) self.set_constraint(edited_atoms._get_constraints()) self.set_cell(edited_atoms.get_cell()) self.set_initial_magnetic_moments( edited_atoms.get_initial_magnetic_moments()) self.set_tags(edited_atoms.get_tags()) return def string2symbols(s): """Convert string to list of chemical symbols.""" n = len(s) if n == 0: return [] c = s[0] if c.isdigit(): i = 1 while i < n and s[i].isdigit(): i += 1 return int(s[:i]) * string2symbols(s[i:]) if c == '(': p = 0 for i, c in enumerate(s): if c == '(': p += 1 elif c == ')': p -= 1 if p == 0: break j = i + 1 while j < n and s[j].isdigit(): j += 1 if j > i + 1: m = int(s[i + 1:j]) else: m = 1 return m * string2symbols(s[1:i]) + string2symbols(s[j:]) if c.isupper(): i = 1 if 1 < n and s[1].islower(): i += 1 j = i while j < n and s[j].isdigit(): j += 1 if j > i: m = int(s[i:j]) else: m = 1 return m * [s[:i]] + string2symbols(s[j:]) else: raise ValueError def symbols2numbers(symbols): if isinstance(symbols, str): symbols = string2symbols(symbols) numbers = [] for s in symbols: if isinstance(s, basestring): numbers.append(atomic_numbers[s]) else: numbers.append(s) return numbers def string2vector(v): if isinstance(v, str): if v[0] == '-': return -string2vector(v[1:]) w = np.zeros(3) w['xyz'.index(v)] = 1.0 return w return np.array(v, float) def default(data, dflt): """Helper function for setting default values.""" if data is None: return None elif isinstance(data, (list, tuple)): newdata = [] allnone = True for x in data: if x is None: newdata.append(dflt) else: newdata.append(x) allnone = False if allnone: return None return newdata else: return data