def initialize_clgd(self):
N_c = get_number_of_grid_points(self.cl.cell, self.cl.spacing)
self.cl.spacing = np.diag(self.cl.cell) / N_c
self.cl.gd = GridDescriptor(N_c, self.cl.cell, False, self.cl.dcomm,
self.cl.dparsize)
self.cl.gd_global = GridDescriptor(N_c, self.cl.cell, False,
serial_comm, None)
self.cl.extrapolated_qm_phi = self.cl.gd.empty()
def initialize_clgd(self):
N_c = get_number_of_grid_points(self.cl.cell, self.cl.spacing)
self.cl.spacing = np.diag(self.cl.cell) / N_c
self.cl.gd = GridDescriptor(N_c,
self.cl.cell,
False,
self.cl.dcomm,
self.cl.dparsize)
self.cl.gd_global = GridDescriptor(N_c,
self.cl.cell,
False,
serial_comm,
None)
self.cl.extrapolated_qm_phi = self.cl.gd.empty()
def __call__(self, name, atoms):
kpts = self.calculate_kpts(atoms)
kwargs = self.kwargs.copy() # modify a copy
if (not atoms.pbc.any() and len(atoms) == 1 and
atoms.get_initial_magnetic_moments().any() and
'hund' not in kwargs):
kwargs['hund'] = True
if atoms.pbc.any() and 'gpts' not in kwargs:
# Use fixed number of gpts:
h = kwargs.get('h')
if h is not None:
h /= Bohr
cell_cv = atoms.cell / Bohr
mode = kwargs.get('mode')
if mode == 'pw':
mode = PW()
gpts = get_number_of_grid_points(cell_cv, h, mode,
kwargs.get('realspace'))
kwargs['h'] = None
kwargs['gpts'] = gpts
if isinstance(mode, PW):
kwargs['mode'] = PW(mode.ecut * Hartree,
mode.fftwflags,
atoms.cell)
if self.show_text_output:
txt = '-'
else:
txt = name + '.txt'
if self.write_gpw_file is not None:
from gpaw.hooks import hooks
hooks['converged'] = (
lambda calc, name=name + '.gpw', mode=self.write_gpw_file:
calc.write(name, mode))
from gpaw import GPAW
return GPAW(txt=txt, kpts=kpts, **kwargs)
def __call__(self, name, atoms):
kpts = self.calculate_kpts(atoms)
kwargs = self.kwargs.copy() # modify a copy
if (not atoms.pbc.any() and len(atoms) == 1 and
atoms.get_initial_magnetic_moments().any() and
'hund' not in kwargs):
kwargs['hund'] = True
if atoms.pbc.any() and 'gpts' not in kwargs:
# Use fixed number of gpts:
h = kwargs.get('h')
if h is not None:
h /= Bohr
cell_cv = atoms.cell / Bohr
mode = kwargs.get('mode')
if mode == 'pw':
mode = PW()
gpts = get_number_of_grid_points(cell_cv, h, mode,
kwargs.get('realspace'))
kwargs['h'] = None
kwargs['gpts'] = gpts
if isinstance(mode, PW):
kwargs['mode'] = PW(mode.ecut * Hartree,
mode.fftwflags,
atoms.cell)
if self.show_text_output:
txt = '-'
else:
txt = name + '.txt'
from gpaw import GPAW
return GPAW(txt=txt, kpts=kpts, **kwargs)
def create_subsystems(self, atoms_in):
# Create new Atoms object
atoms_out = atoms_in.copy()
# New quantum grid
self.qm.cell = \
np.diag([(self.shift_indices_2[w] - self.shift_indices_1[w]) *
self.cl.spacing[w] for w in range(3)])
self.qm.spacing = self.cl.spacing / self.hratios
N_c = get_number_of_grid_points(self.qm.cell, self.qm.spacing)
atoms_out.set_cell(np.diag(self.qm.cell) * Bohr)
atoms_out.positions = atoms_in.get_positions() - self.qm.corner1 * Bohr
msg = self.messages.append
msg("Quantum box readjustment:")
msg(" Given cell: [%10.5f %10.5f %10.5f]" %
tuple(np.diag(atoms_in.get_cell())))
msg(" Given atomic coordinates:")
for s, c in zip(atoms_in.get_chemical_symbols(),
atoms_in.get_positions()):
msg(" %s %10.5f %10.5f %10.5f" %
(s, c[0], c[1], c[2]))
msg(" Readjusted cell: [%10.5f %10.5f %10.5f]" %
tuple(np.diag(atoms_out.get_cell())))
msg(" Readjusted atomic coordinates:")
for s, c in zip(atoms_out.get_chemical_symbols(),
atoms_out.get_positions()):
msg(" %s %10.5f %10.5f %10.5f" %
(s, c[0], c[1], c[2]))
msg(" Given corner points: " +
"(%10.5f %10.5f %10.5f) - (%10.5f %10.5f %10.5f)"
% (tuple(np.concatenate((self.given_corner_v1,
self.given_corner_v2)))))
msg(" Readjusted corner points: " +
"(%10.5f %10.5f %10.5f) - (%10.5f %10.5f %10.5f)"
% (tuple(np.concatenate((self.qm.corner1,
self.qm.corner2)) * Bohr)))
msg(" Indices in classical grid: " +
"(%10i %10i %10i) - (%10i %10i %10i)"
% (tuple(np.concatenate((self.shift_indices_1,
self.shift_indices_2)))))
msg(" Grid points in classical grid: " +
"(%10i %10i %10i)"
% (tuple(self.cl.gd.N_c)))
msg(" Grid points in quantum grid: " +
"(%10i %10i %10i)"
% (tuple(N_c)))
msg(" Spacings in quantum grid: " +
"(%10.5f %10.5f %10.5f)"
% (tuple(np.diag(self.qm.cell) * Bohr / N_c)))
msg(" Spacings in classical grid: " +
"(%10.5f %10.5f %10.5f)"
% (tuple(np.diag(self.cl.cell) *
Bohr / get_number_of_grid_points(self.cl.cell,
self.cl.spacing))))
# msg(" Ratios of cl/qm spacings: " +
# "(%10i %10i %10i)" % (tuple(self.hratios)))
# msg(" = (%10.2f %10.2f %10.2f)" %
# (tuple((np.diag(self.cl.cell) * Bohr / \
# get_number_of_grid_points(self.cl.cell,
# self.cl.spacing)) / \
# (np.diag(self.qm.cell) * Bohr / N_c))))
msg(" Needed number of refinements: %10i"
% self.num_refinements)
# First, create the quantum grid equivalent GridDescriptor
# self.cl.subgd. Then coarsen it until its h_cv equals
# that of self.cl.gd. Finally, map the points between
# clgd and coarsened subgrid.
subcell_cv = np.diag(self.qm.corner2 - self.qm.corner1)
N_c = get_number_of_grid_points(subcell_cv, self.cl.spacing)
N_c = self.shift_indices_2 - self.shift_indices_1
self.cl.subgds = []
self.cl.subgds.append(GridDescriptor(N_c, subcell_cv, False,
serial_comm, self.cl.dparsize))
# msg(" N_c/spacing of the subgrid: " +
# "%3i %3i %3i / %.4f %.4f %.4f" %
# (self.cl.subgds[0].N_c[0],
# self.cl.subgds[0].N_c[1],
# self.cl.subgds[0].N_c[2],
# self.cl.subgds[0].h_cv[0][0] * Bohr,
# self.cl.subgds[0].h_cv[1][1] * Bohr,
# self.cl.subgds[0].h_cv[2][2] * Bohr))
# msg(" shape from the subgrid: " +
# "%3i %3i %3i" % (tuple(self.cl.subgds[0].empty().shape)))
self.cl.coarseners = []
self.cl.refiners = []
for n in range(self.num_refinements):
self.cl.subgds.append(self.cl.subgds[n].refine())
self.cl.refiners.append(Transformer(self.cl.subgds[n],
self.cl.subgds[n + 1]))
# msg(" refiners[%i] can perform the transformation " +
# "(%3i %3i %3i) -> (%3i %3i %3i)" % (\
# n,
# self.cl.subgds[n].empty().shape[0],
# self.cl.subgds[n].empty().shape[1],
# self.cl.subgds[n].empty().shape[2],
# self.cl.subgds[n + 1].empty().shape[0],
# self.cl.subgds[n + 1].empty().shape[1],
# self.cl.subgds[n + 1].empty().shape[2]))
self.cl.coarseners.append(Transformer(self.cl.subgds[n + 1],
self.cl.subgds[n]))
self.cl.coarseners[:] = self.cl.coarseners[::-1]
# Now extend the grid in order to handle
# the zero boundary conditions that the refiner assumes
# The default interpolation order
self.extend_nn = \
Transformer(GridDescriptor([8, 8, 8], [1, 1, 1],
False, serial_comm, None),
GridDescriptor([8, 8, 8], [1, 1, 1],
False, serial_comm, None).coarsen()).nn
self.extended_num_indices = self.num_indices + [2, 2, 2]
# Center, left, and right points of the suggested quantum grid
extended_cp = 0.5 * (np.array(self.given_corner_v1 / Bohr) +
np.array(self.given_corner_v2 / Bohr))
extended_lp = extended_cp - 0.5 * (self.extended_num_indices *
self.cl.spacing)
# extended_rp = extended_cp + 0.5 * (self.extended_num_indices *
# self.cl.spacing)
# Indices in the classical grid restricting the quantum grid
self.extended_shift_indices_1 = \
np.round(extended_lp / self.cl.spacing).astype(int)
self.extended_shift_indices_2 = \
self.extended_shift_indices_1 + self.extended_num_indices
# msg(' extended_shift_indices_1: %i %i %i'
# % (self.extended_shift_indices_1[0],
# self.extended_shift_indices_1[1],
# self.extended_shift_indices_1[2]))
# msg(' extended_shift_indices_2: %i %i %i'
# % (self.extended_shift_indices_2[0],
# self.extended_shift_indices_2[1],
# self.extended_shift_indices_2[2]))
# msg(' cl.gd.N_c: %i %i %i'
# % (self.cl.gd.N_c[0], self.cl.gd.N_c[1], self.cl.gd.N_c[2]))
# Sanity checks
assert(all([self.extended_shift_indices_1[w] >= 0 and
self.extended_shift_indices_2[w] <= self.cl.gd.N_c[w]
for w in range(3)])), \
"Could not find appropriate quantum grid. " + \
"Move it further away from the boundary."
# Corner coordinates
self.qm.extended_corner1 = \
self.extended_shift_indices_1 * self.cl.spacing
self.qm.extended_corner2 = \
self.extended_shift_indices_2 * self.cl.spacing
N_c = self.extended_shift_indices_2 - self.extended_shift_indices_1
self.cl.extended_subgds = []
self.cl.extended_refiners = []
extended_subcell_cv = np.diag(self.qm.extended_corner2 -
self.qm.extended_corner1)
self.cl.extended_subgds.append(GridDescriptor(
N_c, extended_subcell_cv, False, serial_comm, None))
for n in range(self.num_refinements):
self.cl.extended_subgds.append(self.cl.extended_subgds[n].refine())
self.cl.extended_refiners.append(Transformer(
self.cl.extended_subgds[n], self.cl.extended_subgds[n + 1]))
# msg(" extended_refiners[%i] can perform the transformation " +
# "(%3i %3i %3i) -> (%3i %3i %3i)"
# % (n,
# self.cl.extended_subgds[n].empty().shape[0],
# self.cl.extended_subgds[n].empty().shape[1],
# self.cl.extended_subgds[n].empty().shape[2],
# self.cl.extended_subgds[n + 1].empty().shape[0],
# self.cl.extended_subgds[n + 1].empty().shape[1],
# self.cl.extended_subgds[n + 1].empty().shape[2]))
# msg(" N_c/spacing of the refined subgrid: " +
# "%3i %3i %3i / %.4f %.4f %.4f"
# % (self.cl.subgds[-1].N_c[0],
# self.cl.subgds[-1].N_c[1],
# self.cl.subgds[-1].N_c[2],
# self.cl.subgds[-1].h_cv[0][0] * Bohr,
# self.cl.subgds[-1].h_cv[1][1] * Bohr,
# self.cl.subgds[-1].h_cv[2][2] * Bohr))
# msg(" shape from the refined subgrid: %3i %3i %3i"
# % (tuple(self.cl.subgds[-1].empty().shape)))
self.extended_deltaIndex = 2**(self.num_refinements) * self.extend_nn
# msg(" self.extended_deltaIndex = %i" % self.extended_deltaIndex)
qgpts = self.cl.subgds[-1].coarsen().N_c
# Assure that one returns to the original shape
dmygd = self.cl.subgds[-1].coarsen()
for n in range(self.num_refinements - 1):
dmygd = dmygd.coarsen()
# msg(" N_c/spacing of the coarsened subgrid: " +
# "%3i %3i %3i / %.4f %.4f %.4f"
# % (dmygd.N_c[0], dmygd.N_c[1], dmygd.N_c[2],
# dmygd.h_cv[0][0] * Bohr,
# dmygd.h_cv[1][1] * Bohr,
# dmygd.h_cv[2][2] * Bohr))
self.has_subsystems = True
return atoms_out, self.qm.spacing[0] * Bohr, qgpts
def cut_cell(self,
atoms_in,
vacuum=5.0,
corners=None,
create_subsystems=True):
qmh = self.qm.spacing_def
if corners is not None:
v1 = np.array(corners[0]).ravel() / Bohr
v2 = np.array(corners[1]).ravel() / Bohr
else: # Use vacuum
pos_old = atoms_in.get_positions()[0]
dmy_atoms = atoms_in.copy()
dmy_atoms.center(vacuum=vacuum)
pos_new = dmy_atoms.get_positions()[0]
v1 = (pos_old - pos_new) / Bohr
v2 = v1 + np.diag(dmy_atoms.get_cell()) / Bohr
# Needed for restarting
self.given_corner_v1 = v1 * Bohr
self.given_corner_v2 = v2 * Bohr
self.given_cell = atoms_in.get_cell()
# Sanity check: quantum box must be inside the classical one
assert (all([v1[w] <= v2[w] and v1[w] >= 0 and
v2[w] <= np.diag(self.cl.cell)[w] for w in range(3)]))
# Ratios of the user-given spacings
self.hratios = self.cl.spacing_def / qmh
self.num_refinements = \
1 + int(round(np.log(self.hratios[0]) / np.log(2.0)))
assert ([int(round(np.log(self.hratios[w]) / np.log(2.0))) ==
self.num_refinements for w in range(3)])
# Create quantum grid
self.qm.cell = np.zeros((3, 3))
for w in range(3):
self.qm.cell[w, w] = v2[w] - v1[w]
N_c = get_number_of_grid_points(self.qm.cell, qmh)
self.qm.spacing = np.diag(self.qm.cell) / N_c
# Classical corner indices must be divisible with numb
if any(self.cl.spacing / self.qm.spacing >= 3):
numb = 1
elif any(self.cl.spacing / self.qm.spacing >= 2):
numb = 2
else:
numb = 4
# The index mismatch of the two simulation cells
# Round before taking floor/ceil to avoid floating point
# arithmetic errors
num_indices_1 = numb * np.floor(
np.round(np.array(v1) / self.cl.spacing / numb, 2)).astype(int)
num_indices_2 = numb * np.ceil(
np.round(np.array(v2) / self.cl.spacing / numb, 2)).astype(int)
self.num_indices = num_indices_2 - num_indices_1
# Center, left, and right points of the suggested quantum grid
cp = 0.5 * (np.array(v1) + np.array(v2))
lp = cp - 0.5 * self.num_indices * self.cl.spacing
# rp = cp + 0.5 * self.num_indices * self.cl.spacing
# Indices in the classical grid restricting the quantum grid
self.shift_indices_1 = np.round(lp / self.cl.spacing).astype(int)
self.shift_indices_2 = self.shift_indices_1 + self.num_indices
# Sanity checks
assert(all([self.shift_indices_1[w] >= 0 and
self.shift_indices_2[w] <= self.cl.gd.N_c[w]
for w in range(3)])), \
"Could not find appropriate quantum grid. " + \
"Move it further away from the boundary."
# Corner coordinates
self.qm.corner1 = self.shift_indices_1 * self.cl.spacing
self.qm.corner2 = self.shift_indices_2 * self.cl.spacing
# Now the information for creating the subsystems is ready
if create_subsystems:
return self.create_subsystems(atoms_in)
def initialize(self, atoms=None):
"""Inexpensive initialization."""
if atoms is None:
atoms = self.atoms
else:
# Save the state of the atoms:
self.atoms = atoms.copy()
par = self.input_parameters
world = par.communicator
if world is None:
world = mpi.world
elif hasattr(world, 'new_communicator'):
# Check for whether object has correct type already
#
# Using isinstance() is complicated because of all the
# combinations, serial/parallel/debug...
pass
else:
# world should be a list of ranks:
world = mpi.world.new_communicator(np.asarray(world))
self.wfs.world = world
self.set_text(par.txt, par.verbose)
natoms = len(atoms)
cell_cv = atoms.get_cell() / Bohr
pbc_c = atoms.get_pbc()
Z_a = atoms.get_atomic_numbers()
magmom_av = atoms.get_initial_magnetic_moments()
# Generate new xc functional only when it is reset by set
if self.hamiltonian is None or self.hamiltonian.xc is None:
if isinstance(par.xc, str):
xc = XC(par.xc)
else:
xc = par.xc
else:
xc = self.hamiltonian.xc
mode = par.mode
if xc.orbital_dependent and mode == 'lcao':
raise NotImplementedError('LCAO mode does not support '
'orbital-dependent XC functionals.')
if mode == 'pw':
mode = PW()
if mode == 'fd' and par.usefractrans:
raise NotImplementedError('FD mode does not support '
'fractional translations.')
if mode == 'lcao' and par.usefractrans:
raise Warning('Fractional translations have not been tested '
'with LCAO mode. Use with care!')
if par.realspace is None:
realspace = not isinstance(mode, PW)
else:
realspace = par.realspace
if isinstance(mode, PW):
assert not realspace
if par.gpts is not None:
N_c = np.array(par.gpts)
else:
h = par.h
if h is not None:
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace)
if par.filter is None and not isinstance(mode, PW):
gamma = 1.6
hmax = ((np.linalg.inv(cell_cv)**2).sum(0)**-0.5 / N_c).max()
def filter(rgd, rcut, f_r, l=0):
gcut = np.pi / hmax - 2 / rcut / gamma
f_r[:] = rgd.filter(f_r, rcut * gamma, gcut, l)
else:
filter = par.filter
setups = Setups(Z_a, par.setups, par.basis, par.lmax, xc,
filter, world)
if magmom_av.ndim == 1:
collinear = True
magmom_av, magmom_a = np.zeros((natoms, 3)), magmom_av
magmom_av[:, 2] = magmom_a
else:
collinear = False
magnetic = magmom_av.any()
spinpol = par.spinpol
if par.hund:
if natoms != 1:
raise ValueError('hund=True arg only valid for single atoms!')
spinpol = True
magmom_av[0] = (0, 0, setups[0].get_hunds_rule_moment(par.charge))
if spinpol is None:
spinpol = magnetic
elif magnetic and not spinpol:
raise ValueError('Non-zero initial magnetic moment for a ' +
'spin-paired calculation!')
if collinear:
nspins = 1 + int(spinpol)
ncomp = 1
else:
nspins = 1
ncomp = 2
# K-point descriptor
bzkpts_kc = kpts2ndarray(par.kpts, self.atoms)
kd = KPointDescriptor(bzkpts_kc, nspins, collinear, par.usefractrans)
width = par.width
if width is None:
if pbc_c.any():
width = 0.1 # eV
else:
width = 0.0
else:
assert par.occupations is None
if hasattr(self, 'time') or par.dtype == complex:
dtype = complex
else:
if kd.gamma:
dtype = float
else:
dtype = complex
## rbw: If usefractrans=True, kd.set_symmetry might overwrite N_c.
## This is necessary, because N_c must be dividable by 1/(fractional translation),
## f.e. fractional translations of a grid point must land on a grid point.
N_c = kd.set_symmetry(atoms, setups, magmom_av, par.usesymm, N_c, world)
nao = setups.nao
nvalence = setups.nvalence - par.charge
M_v = magmom_av.sum(0)
M = np.dot(M_v, M_v)**0.5
nbands = par.nbands
if nbands is None:
nbands = 0
for setup in setups:
nbands_from_atom = setup.get_default_nbands()
# Any obscure setup errors?
if nbands_from_atom < -(-setup.Nv // 2):
raise ValueError('Bad setup: This setup requests %d'
' bands but has %d electrons.'
% (nbands_from_atom, setup.Nv))
nbands += nbands_from_atom
nbands = min(nao, nbands)
elif nbands > nao and mode == 'lcao':
raise ValueError('Too many bands for LCAO calculation: '
'%d bands and only %d atomic orbitals!' %
(nbands, nao))
if nvalence < 0:
raise ValueError(
'Charge %f is not possible - not enough valence electrons' %
par.charge)
if nbands <= 0:
nbands = int(nvalence + M + 0.5) // 2 + (-nbands)
if nvalence > 2 * nbands:
raise ValueError('Too few bands! Electrons: %f, bands: %d'
% (nvalence, nbands))
nbands *= ncomp
if par.width is not None:
self.text('**NOTE**: please start using '
'occupations=FermiDirac(width).')
if par.fixmom:
self.text('**NOTE**: please start using '
'occupations=FermiDirac(width, fixmagmom=True).')
if self.occupations is None:
if par.occupations is None:
# Create object for occupation numbers:
self.occupations = occupations.FermiDirac(width, par.fixmom)
else:
self.occupations = par.occupations
self.occupations.magmom = M_v[2]
cc = par.convergence
if mode == 'lcao':
niter_fixdensity = 0
else:
niter_fixdensity = None
if self.scf is None:
self.scf = SCFLoop(
cc['eigenstates'] / Hartree**2 * nvalence,
cc['energy'] / Hartree * max(nvalence, 1),
cc['density'] * nvalence,
par.maxiter, par.fixdensity,
niter_fixdensity)
parsize_kpt = par.parallel['kpt']
parsize_domain = par.parallel['domain']
parsize_bands = par.parallel['band']
if not realspace:
pbc_c = np.ones(3, bool)
if not self.wfs:
if parsize_domain == 'domain only': # XXX this was silly!
parsize_domain = world.size
parallelization = mpi.Parallelization(world,
nspins * kd.nibzkpts)
ndomains = None
if parsize_domain is not None:
ndomains = np.prod(parsize_domain)
if isinstance(mode, PW):
if ndomains > 1:
raise ValueError('Planewave mode does not support '
'domain decomposition.')
ndomains = 1
parallelization.set(kpt=parsize_kpt,
domain=ndomains,
band=parsize_bands)
domain_comm, kpt_comm, band_comm = \
parallelization.build_communicators()
#domain_comm, kpt_comm, band_comm = mpi.distribute_cpus(
# parsize_domain, parsize_bands,
# nspins, kd.nibzkpts, world, par.idiotproof, mode)
kd.set_communicator(kpt_comm)
parstride_bands = par.parallel['stridebands']
# Unfortunately we need to remember that we adjusted the
# number of bands so we can print a warning if it differs
# from the number specified by the user. (The number can
# be inferred from the input parameters, but it's tricky
# because we allow negative numbers)
self.nbands_parallelization_adjustment = -nbands % band_comm.size
nbands += self.nbands_parallelization_adjustment
# I would like to give the following error message, but apparently
# there are cases, e.g. gpaw/test/gw_ppa.py, which involve
# nbands > nao and are supposed to work that way.
#if nbands > nao:
# raise ValueError('Number of bands %d adjusted for band '
# 'parallelization %d exceeds number of atomic '
# 'orbitals %d. This problem can be fixed '
# 'by reducing the number of bands a bit.'
# % (nbands, band_comm.size, nao))
bd = BandDescriptor(nbands, band_comm, parstride_bands)
if (self.density is not None and
self.density.gd.comm.size != domain_comm.size):
# Domain decomposition has changed, so we need to
# reinitialize density and hamiltonian:
if par.fixdensity:
raise RuntimeError('Density reinitialization conflict ' +
'with "fixdensity" - specify domain decomposition.')
self.density = None
self.hamiltonian = None
# Construct grid descriptor for coarse grids for wave functions:
gd = self.grid_descriptor_class(N_c, cell_cv, pbc_c,
domain_comm, parsize_domain)
# do k-point analysis here? XXX
args = (gd, nvalence, setups, bd, dtype, world, kd, self.timer)
if par.parallel['sl_auto']:
# Choose scalapack parallelization automatically
for key, val in par.parallel.items():
if (key.startswith('sl_') and key != 'sl_auto'
and val is not None):
raise ValueError("Cannot use 'sl_auto' together "
"with '%s'" % key)
max_scalapack_cpus = bd.comm.size * gd.comm.size
nprow = max_scalapack_cpus
npcol = 1
# Get a sort of reasonable number of columns/rows
while npcol < nprow and nprow % 2 == 0:
npcol *= 2
nprow //= 2
assert npcol * nprow == max_scalapack_cpus
# ScaLAPACK creates trouble if there aren't at least a few
# whole blocks; choose block size so there will always be
# several blocks. This will crash for small test systems,
# but so will ScaLAPACK in any case
blocksize = min(-(-nbands // 4), 64)
sl_default = (nprow, npcol, blocksize)
else:
sl_default = par.parallel['sl_default']
if mode == 'lcao':
# Layouts used for general diagonalizer
sl_lcao = par.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
lcaoksl = get_KohnSham_layouts(sl_lcao, 'lcao',
gd, bd, dtype,
nao=nao, timer=self.timer)
if collinear:
self.wfs = LCAOWaveFunctions(lcaoksl, *args)
else:
from gpaw.xc.noncollinear import \
NonCollinearLCAOWaveFunctions
self.wfs = NonCollinearLCAOWaveFunctions(lcaoksl, *args)
elif mode == 'fd' or isinstance(mode, PW):
# buffer_size keyword only relevant for fdpw
buffer_size = par.parallel['buffer_size']
# Layouts used for diagonalizer
sl_diagonalize = par.parallel['sl_diagonalize']
if sl_diagonalize is None:
sl_diagonalize = sl_default
diagksl = get_KohnSham_layouts(sl_diagonalize, 'fd',
gd, bd, dtype,
buffer_size=buffer_size,
timer=self.timer)
# Layouts used for orthonormalizer
sl_inverse_cholesky = par.parallel['sl_inverse_cholesky']
if sl_inverse_cholesky is None:
sl_inverse_cholesky = sl_default
if sl_inverse_cholesky != sl_diagonalize:
message = 'sl_inverse_cholesky != sl_diagonalize ' \
'is not implemented.'
raise NotImplementedError(message)
orthoksl = get_KohnSham_layouts(sl_inverse_cholesky, 'fd',
gd, bd, dtype,
buffer_size=buffer_size,
timer=self.timer)
# Use (at most) all available LCAO for initialization
lcaonbands = min(nbands, nao)
try:
lcaobd = BandDescriptor(lcaonbands, band_comm,
parstride_bands)
except RuntimeError:
initksl = None
else:
# Layouts used for general diagonalizer
# (LCAO initialization)
sl_lcao = par.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
initksl = get_KohnSham_layouts(sl_lcao, 'lcao',
gd, lcaobd, dtype,
nao=nao,
timer=self.timer)
if hasattr(self, 'time'):
assert mode == 'fd'
from gpaw.tddft import TimeDependentWaveFunctions
self.wfs = TimeDependentWaveFunctions(par.stencils[0],
diagksl, orthoksl, initksl, gd, nvalence, setups,
bd, world, kd, self.timer)
elif mode == 'fd':
self.wfs = FDWaveFunctions(par.stencils[0], diagksl,
orthoksl, initksl, *args)
else:
# Planewave basis:
self.wfs = mode(diagksl, orthoksl, initksl, *args)
else:
self.wfs = mode(self, *args)
else:
self.wfs.set_setups(setups)
if not self.wfs.eigensolver:
# Number of bands to converge:
nbands_converge = cc['bands']
if nbands_converge == 'all':
nbands_converge = nbands
elif nbands_converge != 'occupied':
assert isinstance(nbands_converge, int)
if nbands_converge < 0:
nbands_converge += nbands
eigensolver = get_eigensolver(par.eigensolver, mode,
par.convergence)
eigensolver.nbands_converge = nbands_converge
# XXX Eigensolver class doesn't define an nbands_converge property
if isinstance(xc, SIC):
eigensolver.blocksize = 1
self.wfs.set_eigensolver(eigensolver)
if self.density is None:
gd = self.wfs.gd
if par.stencils[1] != 9:
# Construct grid descriptor for fine grids for densities
# and potentials:
finegd = gd.refine()
else:
# Special case (use only coarse grid):
finegd = gd
if realspace:
self.density = RealSpaceDensity(
gd, finegd, nspins, par.charge + setups.core_charge,
collinear, par.stencils[1])
else:
self.density = ReciprocalSpaceDensity(
gd, finegd, nspins, par.charge + setups.core_charge,
collinear)
self.density.initialize(setups, self.timer, magmom_av, par.hund)
self.density.set_mixer(par.mixer)
if self.hamiltonian is None:
gd, finegd = self.density.gd, self.density.finegd
if realspace:
self.hamiltonian = RealSpaceHamiltonian(
gd, finegd, nspins, setups, self.timer, xc, par.external,
collinear, par.poissonsolver, par.stencils[1], world)
else:
self.hamiltonian = ReciprocalSpaceHamiltonian(
gd, finegd,
self.density.pd2, self.density.pd3,
nspins, setups, self.timer, xc, par.external,
collinear, world)
xc.initialize(self.density, self.hamiltonian, self.wfs,
self.occupations)
self.text()
self.print_memory_estimate(self.txt, maxdepth=memory_estimate_depth)
self.txt.flush()
self.timer.print_info(self)
if dry_run:
self.dry_run()
self.initialized = True
def get_eigenmodes(self,filename = None, chi0 = None, calc = None, dm = None,
xc = 'RPA', sum = None, vcut = None, checkphase = False,
return_full = False):
"""
Calculate the plasmonic eigenmodes as eigenvectors of the dielectric matrix.
Parameters:
filename: pckl file
output from response calculation.
chi0: gpw file
chi0_wGG from response calculation.
calc: gpaw calculator instance
ground state calculator used in response calculation.
Wavefunctions only needed if chi0 is calculated from scratch
dm: gpw file
dielectric matrix from response calculation
xc: str 'RPA'or 'ALDA' XC- Kernel
sum: str
'2D': sum in the x and y directions
'1D': To be implemented
vcut: str '0D','1D' or '2D'
Cut the Coulomb potential
checkphase: Bool
if True, the eigenfunctions id rotated in the complex
plane, to be made as real as posible
return_full: Bool
if True, the eigenvectors in reciprocal space is also
returned.
"""
self.read(filename)
self.pbc = [1,1,1]
#self.calc.atoms.pbc = [1,1,1]
npw = self.npw
self.w_w = np.linspace(0, self.dw * (self.Nw - 1)*Hartree, self.Nw)
self.vcut = vcut
dm_wGG = self.get_dielectric_matrix(xc=xc,
symmetric=False,
chi0_wGG=chi0,
calc=calc,
vcut=vcut)
q = self.q_c
# get grid on which the eigenmodes are calculated
#gd = self.calc.wfs.gd
#r = gd.get_grid_point_coordinates()
#rrr = r*Bohr
from gpaw.utilities.gpts import get_number_of_grid_points
from gpaw.grid_descriptor import GridDescriptor
grid_size = [1,1,1]
h=0.2
cell_cv = self.acell_cv*np.diag(grid_size)
mode = 'fd'
realspace = True
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace)
gd = GridDescriptor(N_c, cell_cv, self.pbc)
#gd = self.calc.wfs.gd
r = gd.get_grid_point_coordinates()
rrr = r*Bohr
eig_0 = np.array([], dtype = complex)
eig_left = np.array([], dtype = complex)
eig_right = np.array([], dtype = complex)
vec_modes = np.zeros([1, self.npw], dtype = complex)
vec_modes_dual = np.zeros([1, self.npw], dtype = complex)
vec_modes_density = np.zeros([1, self.npw], dtype = complex)
vec_modes_norm = np.zeros([1, self.npw], dtype = complex)
eig_all = np.zeros([1, self.npw], dtype = complex)
eig_dummy = np.zeros([1, self.npw], dtype = complex)
v_dummy = np.zeros([1, self.npw], dtype = complex)
vec_dummy = np.zeros([1, self.npw], dtype = complex)
vec_dummy2 = np.zeros([1, self.npw], dtype = complex)
w_0 = np.array([])
w_left = np.array([])
w_right = np.array([])
if sum == '2D':
v_ind = np.zeros([1, r.shape[-1]], dtype = complex)
n_ind = np.zeros([1, r.shape[-1]], dtype = complex)
elif sum == '1D':
self.printtxt('1D sum not implemented')
return
else:
v_ind = np.zeros([1, r.shape[1], r.shape[2], r.shape[3]], dtype = complex)
n_ind = np.zeros([1, r.shape[1], r.shape[2], r.shape[3]], dtype = complex)
eps_GG_plus = dm_wGG[0]
eig_plus, vec_plus = np.linalg.eig(eps_GG_plus) # find eigenvalues and eigenvectors
vec_plus_dual = np.linalg.inv(vec_plus)
# loop over frequencies, where the eigenvalues for the 2D matrix in G,G' are found.
for i in np.array(range(self.Nw-1))+1:
eps_GG = eps_GG_plus
eig, vec = eig_plus,vec_plus
vec_dual = vec_plus_dual
eps_GG_plus = dm_wGG[i] # epsilon_GG'(omega + d-omega)
eig_plus, vec_plus = np.linalg.eig(eps_GG_plus)
vec_plus_dual = np.linalg.inv(vec_plus)
eig_dummy[0,:] = eig
eig_all = np.append(eig_all, eig_dummy, axis=0) # append all eigenvalues to array
# loop to check find the eigenvalues that crosses zero from negative to positive values:
for k in range(self.npw):
for m in range(self.npw):
if eig[k]< 0 and 0 < eig_plus[m]:
# check it's the same mode - Overlap between eigenvectors should be large:
if abs(np.inner(vec[:,k], vec_plus_dual[m,:])) > 0.95:
self.printtxt('crossing found at w = %1.1f eV'%self.w_w[i-1])
eig_left = np.append(eig_left, eig[k])
eig_right = np.append(eig_right, eig_plus[m])
vec_dummy[0, :] = vec[:,k]
vec_modes = np.append(vec_modes, vec_dummy, axis = 0)
vec_dummy[0, :] = vec_dual[k, :].T
vec_modes_dual = np.append(vec_modes_dual, vec_dummy, axis = 0)
w1 = self.w_w[i-1]
w2 = self.w_w[i]
a = np.real((eig_plus[m]-eig[k]) / (w2-w1))
w0 = np.real(-eig[k]) / a + w1
eig0 = a*(w0-w1)+eig[k]
w_0 = np.append(w_0,w0)
w_left = np.append(w_left, w1)
eig_0 = np.append(eig_0,eig0)
n_dummy = np.zeros([1, r.shape[1], r.shape[2],
r.shape[3]], dtype = complex)
v_dummy = np.zeros([1, r.shape[1], r.shape[2],
r.shape[3]], dtype = complex)
vec_n = np.zeros([self.npw])
for iG in range(self.npw): # Fourier transform
qG = np.dot((q + self.Gvec_Gc[iG]), self.bcell_cv)
coef_G = np.dot(qG, qG) / (4 * pi)
qGr_R = np.inner(qG, r.T).T
v_dummy += vec[iG, k] * np.exp(1j * qGr_R)
n_dummy += vec[iG, k] * np.exp(1j * qGr_R) * coef_G
if checkphase: # rotate eigenvectors in complex plane
integral = np.zeros([81])
phases = np.linspace(0,2,81)
for ip in range(81):
v_int = v_dummy * np.exp(1j * pi * phases[ip])
integral[ip] = abs(np.imag(v_int)).sum()
phase = phases[np.argsort(integral)][0]
v_dummy *= np.exp(1j * pi * phase)
n_dummy *= np.exp(1j * pi * phase)
if sum == '2D':
i_xyz = 3
v_dummy_z = np.zeros([1,v_dummy.shape[i_xyz]],
dtype = complex)
n_dummy_z = np.zeros([1,v_dummy.shape[i_xyz]],
dtype = complex)
v_dummy_z[0,:] = np.sum(np.sum(v_dummy, axis = 1),
axis = 1)[0,:]
n_dummy_z[0,:] = np.sum(np.sum(n_dummy, axis = 1),
axis = 1)[0,:]
v_ind = np.append(v_ind, v_dummy_z, axis=0)
n_ind = np.append(n_ind, n_dummy_z, axis=0)
elif sum == '1D':
self.printtxt('1D sum not implemented')
else :
v_ind = np.append(v_ind, v_dummy, axis=0)
n_ind = np.append(n_ind, n_dummy, axis=0)
"""
returns: grid points, frequency grid, all eigenvalues, mode energies, left point energies,
mode eigenvalues, eigenvalues of left and right-side points,
(mode eigenvectors, mode dual eigenvectors,)
induced potential in real space, induced density in real space
"""
if return_full:
return rrr, self.w_w, eig_all[1:], w_0, eig_0, w_left, eig_left, \
eig_right, vec_modes[1:], vec_modes_dual[1:], v_ind[1:], n_ind[1:]
else:
return rrr, self.w_w, eig_all[1:], w_0, eig_0, w_left, eig_left, \
eig_right, v_ind[1:], n_ind[1:]
def create_wave_functions(self, mode, realspace, nspins, nbands, nao,
nvalence, setups, magmom_a, cell_cv, pbc_c):
par = self.parameters
bzkpts_kc = kpts2ndarray(par.kpts, self.atoms)
kd = KPointDescriptor(bzkpts_kc, nspins)
self.timer.start('Set symmetry')
kd.set_symmetry(self.atoms, self.symmetry, comm=self.world)
self.timer.stop('Set symmetry')
self.log(kd)
parallelization = mpi.Parallelization(self.world, nspins * kd.nibzkpts)
parsize_kpt = self.parallel['kpt']
parsize_domain = self.parallel['domain']
parsize_bands = self.parallel['band']
ndomains = None
if parsize_domain is not None:
ndomains = np.prod(parsize_domain)
if mode.name == 'pw':
if ndomains is not None and ndomains > 1:
raise ValueError('Planewave mode does not support '
'domain decomposition.')
ndomains = 1
parallelization.set(kpt=parsize_kpt,
domain=ndomains,
band=parsize_bands)
comms = parallelization.build_communicators()
domain_comm = comms['d']
kpt_comm = comms['k']
band_comm = comms['b']
kptband_comm = comms['D']
domainband_comm = comms['K']
self.comms = comms
if par.gpts is not None:
if par.h is not None:
raise ValueError("""You can't use both "gpts" and "h"!""")
N_c = np.array(par.gpts)
else:
h = par.h
if h is not None:
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace,
kd.symmetry)
self.symmetry.check_grid(N_c)
kd.set_communicator(kpt_comm)
parstride_bands = self.parallel['stridebands']
# Unfortunately we need to remember that we adjusted the
# number of bands so we can print a warning if it differs
# from the number specified by the user. (The number can
# be inferred from the input parameters, but it's tricky
# because we allow negative numbers)
self.nbands_parallelization_adjustment = -nbands % band_comm.size
nbands += self.nbands_parallelization_adjustment
bd = BandDescriptor(nbands, band_comm, parstride_bands)
# Construct grid descriptor for coarse grids for wave functions:
gd = self.create_grid_descriptor(N_c, cell_cv, pbc_c, domain_comm,
parsize_domain)
if hasattr(self, 'time') or mode.force_complex_dtype:
dtype = complex
else:
if kd.gamma:
dtype = float
else:
dtype = complex
wfs_kwargs = dict(gd=gd,
nvalence=nvalence,
setups=setups,
bd=bd,
dtype=dtype,
world=self.world,
kd=kd,
kptband_comm=kptband_comm,
timer=self.timer)
if self.parallel['sl_auto']:
# Choose scalapack parallelization automatically
for key, val in self.parallel.items():
if (key.startswith('sl_') and key != 'sl_auto'
and val is not None):
raise ValueError("Cannot use 'sl_auto' together "
"with '%s'" % key)
max_scalapack_cpus = bd.comm.size * gd.comm.size
nprow = max_scalapack_cpus
npcol = 1
# Get a sort of reasonable number of columns/rows
while npcol < nprow and nprow % 2 == 0:
npcol *= 2
nprow //= 2
assert npcol * nprow == max_scalapack_cpus
# ScaLAPACK creates trouble if there aren't at least a few
# whole blocks; choose block size so there will always be
# several blocks. This will crash for small test systems,
# but so will ScaLAPACK in any case
blocksize = min(-(-nbands // 4), 64)
sl_default = (nprow, npcol, blocksize)
else:
sl_default = self.parallel['sl_default']
if mode.name == 'lcao':
# Layouts used for general diagonalizer
sl_lcao = self.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
lcaoksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
bd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer)
self.wfs = mode(lcaoksl, **wfs_kwargs)
elif mode.name == 'fd' or mode.name == 'pw':
# buffer_size keyword only relevant for fdpw
buffer_size = self.parallel['buffer_size']
# Layouts used for diagonalizer
sl_diagonalize = self.parallel['sl_diagonalize']
if sl_diagonalize is None:
sl_diagonalize = sl_default
diagksl = get_KohnSham_layouts(
sl_diagonalize,
'fd', # XXX
# choice of key 'fd' not so nice
gd,
bd,
domainband_comm,
dtype,
buffer_size=buffer_size,
timer=self.timer)
# Layouts used for orthonormalizer
sl_inverse_cholesky = self.parallel['sl_inverse_cholesky']
if sl_inverse_cholesky is None:
sl_inverse_cholesky = sl_default
if sl_inverse_cholesky != sl_diagonalize:
message = 'sl_inverse_cholesky != sl_diagonalize ' \
'is not implemented.'
raise NotImplementedError(message)
orthoksl = get_KohnSham_layouts(sl_inverse_cholesky,
'fd',
gd,
bd,
domainband_comm,
dtype,
buffer_size=buffer_size,
timer=self.timer)
# Use (at most) all available LCAO for initialization
lcaonbands = min(nbands, nao // band_comm.size * band_comm.size)
try:
lcaobd = BandDescriptor(lcaonbands, band_comm, parstride_bands)
except RuntimeError:
initksl = None
else:
# Layouts used for general diagonalizer
# (LCAO initialization)
sl_lcao = self.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
initksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
lcaobd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer)
self.wfs = mode(diagksl, orthoksl, initksl, **wfs_kwargs)
else:
self.wfs = mode(self, **wfs_kwargs)
self.log(self.wfs, '\n')
def create_wave_functions(self, mode, realspace, nspins, collinear, nbands,
nao, nvalence, setups, cell_cv, pbc_c):
par = self.parameters
kd = self.create_kpoint_descriptor(nspins)
parallelization = mpi.Parallelization(self.world, nspins * kd.nibzkpts)
parsize_kpt = self.parallel['kpt']
parsize_domain = self.parallel['domain']
parsize_bands = self.parallel['band']
ndomains = None
if parsize_domain is not None:
ndomains = np.prod(parsize_domain)
parallelization.set(kpt=parsize_kpt,
domain=ndomains,
band=parsize_bands)
comms = parallelization.build_communicators()
domain_comm = comms['d']
kpt_comm = comms['k']
band_comm = comms['b']
kptband_comm = comms['D']
domainband_comm = comms['K']
self.comms = comms
if par.gpts is not None:
if par.h is not None:
raise ValueError("""You can't use both "gpts" and "h"!""")
N_c = np.array(par.gpts)
else:
h = par.h
if h is not None:
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace,
kd.symmetry)
self.symmetry.check_grid(N_c)
kd.set_communicator(kpt_comm)
parstride_bands = self.parallel['stridebands']
bd = BandDescriptor(nbands, band_comm, parstride_bands)
# Construct grid descriptor for coarse grids for wave functions:
gd = self.create_grid_descriptor(N_c, cell_cv, pbc_c, domain_comm,
parsize_domain)
if hasattr(self, 'time') or mode.force_complex_dtype or not collinear:
dtype = complex
else:
if kd.gamma:
dtype = float
else:
dtype = complex
wfs_kwargs = dict(gd=gd,
nvalence=nvalence,
setups=setups,
bd=bd,
dtype=dtype,
world=self.world,
kd=kd,
kptband_comm=kptband_comm,
timer=self.timer)
if self.parallel['sl_auto']:
# Choose scalapack parallelization automatically
for key, val in self.parallel.items():
if (key.startswith('sl_') and key != 'sl_auto'
and val is not None):
raise ValueError("Cannot use 'sl_auto' together "
"with '%s'" % key)
max_scalapack_cpus = bd.comm.size * gd.comm.size
sl_default = suggest_blocking(nbands, max_scalapack_cpus)
else:
sl_default = self.parallel['sl_default']
if mode.name == 'lcao':
assert collinear
# Layouts used for general diagonalizer
sl_lcao = self.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
elpasolver = None
if self.parallel['use_elpa']:
elpasolver = self.parallel['elpasolver']
lcaoksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
bd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer,
elpasolver=elpasolver)
self.wfs = mode(lcaoksl, **wfs_kwargs)
elif mode.name == 'fd' or mode.name == 'pw':
# Use (at most) all available LCAO for initialization
lcaonbands = min(nbands, nao)
try:
lcaobd = BandDescriptor(lcaonbands, band_comm, parstride_bands)
except RuntimeError:
initksl = None
else:
# Layouts used for general diagonalizer
# (LCAO initialization)
sl_lcao = self.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
initksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
lcaobd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer)
reuse_wfs_method = par.experimental.get('reuse_wfs_method', 'paw')
sl = (domainband_comm, ) + (self.parallel['sl_diagonalize']
or sl_default or (1, 1, None))
self.wfs = mode(sl,
initksl,
reuse_wfs_method=reuse_wfs_method,
collinear=collinear,
**wfs_kwargs)
else:
self.wfs = mode(self, collinear=collinear, **wfs_kwargs)
self.log(self.wfs, '\n')
def initialize(self, atoms=None):
"""Inexpensive initialization."""
if atoms is None:
atoms = self.atoms
else:
# Save the state of the atoms:
self.atoms = atoms.copy()
par = self.input_parameters
world = par.communicator
if world is None:
world = mpi.world
elif hasattr(world, 'new_communicator'):
# Check for whether object has correct type already
#
# Using isinstance() is complicated because of all the
# combinations, serial/parallel/debug...
pass
else:
# world should be a list of ranks:
world = mpi.world.new_communicator(np.asarray(world))
self.wfs.world = world
if 'txt' in self._changed_keywords:
self.set_txt(par.txt)
self.verbose = par.verbose
natoms = len(atoms)
cell_cv = atoms.get_cell() / Bohr
pbc_c = atoms.get_pbc()
Z_a = atoms.get_atomic_numbers()
magmom_av = atoms.get_initial_magnetic_moments()
self.check_atoms()
# Generate new xc functional only when it is reset by set
# XXX sounds like this should use the _changed_keywords dictionary.
if self.hamiltonian is None or self.hamiltonian.xc is None:
if isinstance(par.xc, str):
xc = XC(par.xc)
else:
xc = par.xc
else:
xc = self.hamiltonian.xc
mode = par.mode
if mode == 'fd':
mode = FD()
elif mode == 'pw':
mode = pw.PW()
elif mode == 'lcao':
mode = LCAO()
else:
assert hasattr(mode, 'name'), str(mode)
if xc.orbital_dependent and mode.name == 'lcao':
raise NotImplementedError('LCAO mode does not support '
'orbital-dependent XC functionals.')
if par.realspace is None:
realspace = (mode.name != 'pw')
else:
realspace = par.realspace
if mode.name == 'pw':
assert not realspace
if par.filter is None and mode.name != 'pw':
gamma = 1.6
if par.gpts is not None:
h = ((np.linalg.inv(cell_cv)**2).sum(0)**-0.5 / par.gpts).max()
else:
h = (par.h or 0.2) / Bohr
def filter(rgd, rcut, f_r, l=0):
gcut = np.pi / h - 2 / rcut / gamma
f_r[:] = rgd.filter(f_r, rcut * gamma, gcut, l)
else:
filter = par.filter
setups = Setups(Z_a, par.setups, par.basis, par.lmax, xc, filter,
world)
if magmom_av.ndim == 1:
collinear = True
magmom_av, magmom_a = np.zeros((natoms, 3)), magmom_av
magmom_av[:, 2] = magmom_a
else:
collinear = False
magnetic = magmom_av.any()
spinpol = par.spinpol
if par.hund:
if natoms != 1:
raise ValueError('hund=True arg only valid for single atoms!')
spinpol = True
magmom_av[0] = (0, 0, setups[0].get_hunds_rule_moment(par.charge))
if spinpol is None:
spinpol = magnetic
elif magnetic and not spinpol:
raise ValueError('Non-zero initial magnetic moment for a ' +
'spin-paired calculation!')
if collinear:
nspins = 1 + int(spinpol)
ncomp = 1
else:
nspins = 1
ncomp = 2
if par.usesymm != 'default':
warnings.warn('Use "symmetry" keyword instead of ' +
'"usesymm" keyword')
par.symmetry = usesymm2symmetry(par.usesymm)
symm = par.symmetry
if symm == 'off':
symm = {'point_group': False, 'time_reversal': False}
bzkpts_kc = kpts2ndarray(par.kpts, self.atoms)
kd = KPointDescriptor(bzkpts_kc, nspins, collinear)
m_av = magmom_av.round(decimals=3) # round off
id_a = zip(setups.id_a, *m_av.T)
symmetry = Symmetry(id_a, cell_cv, atoms.pbc, **symm)
kd.set_symmetry(atoms, symmetry, comm=world)
setups.set_symmetry(symmetry)
if par.gpts is not None:
N_c = np.array(par.gpts)
else:
h = par.h
if h is not None:
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace,
kd.symmetry)
symmetry.check_grid(N_c)
width = par.width
if width is None:
if pbc_c.any():
width = 0.1 # eV
else:
width = 0.0
else:
assert par.occupations is None
if hasattr(self, 'time') or par.dtype == complex:
dtype = complex
else:
if kd.gamma:
dtype = float
else:
dtype = complex
nao = setups.nao
nvalence = setups.nvalence - par.charge
M_v = magmom_av.sum(0)
M = np.dot(M_v, M_v)**0.5
nbands = par.nbands
orbital_free = any(setup.orbital_free for setup in setups)
if orbital_free:
nbands = 1
if isinstance(nbands, basestring):
if nbands[-1] == '%':
basebands = int(nvalence + M + 0.5) // 2
nbands = int((float(nbands[:-1]) / 100) * basebands)
else:
raise ValueError('Integer Expected: Only use a string '
'if giving a percentage of occupied bands')
if nbands is None:
nbands = 0
for setup in setups:
nbands_from_atom = setup.get_default_nbands()
# Any obscure setup errors?
if nbands_from_atom < -(-setup.Nv // 2):
raise ValueError('Bad setup: This setup requests %d'
' bands but has %d electrons.' %
(nbands_from_atom, setup.Nv))
nbands += nbands_from_atom
nbands = min(nao, nbands)
elif nbands > nao and mode.name == 'lcao':
raise ValueError('Too many bands for LCAO calculation: '
'%d bands and only %d atomic orbitals!' %
(nbands, nao))
if nvalence < 0:
raise ValueError(
'Charge %f is not possible - not enough valence electrons' %
par.charge)
if nbands <= 0:
nbands = int(nvalence + M + 0.5) // 2 + (-nbands)
if nvalence > 2 * nbands and not orbital_free:
raise ValueError('Too few bands! Electrons: %f, bands: %d' %
(nvalence, nbands))
nbands *= ncomp
if par.width is not None:
self.text('**NOTE**: please start using '
'occupations=FermiDirac(width).')
if par.fixmom:
self.text('**NOTE**: please start using '
'occupations=FermiDirac(width, fixmagmom=True).')
if self.occupations is None:
if par.occupations is None:
# Create object for occupation numbers:
if orbital_free:
width = 0.0 # even for PBC
self.occupations = occupations.TFOccupations(
width, par.fixmom)
else:
self.occupations = occupations.FermiDirac(
width, par.fixmom)
else:
self.occupations = par.occupations
# If occupation numbers are changed, and we have wave functions,
# recalculate the occupation numbers
if self.wfs is not None and not isinstance(self.wfs,
EmptyWaveFunctions):
self.occupations.calculate(self.wfs)
self.occupations.magmom = M_v[2]
cc = par.convergence
if mode.name == 'lcao':
niter_fixdensity = 0
else:
niter_fixdensity = None
if self.scf is None:
force_crit = cc['forces']
if force_crit is not None:
force_crit /= Hartree / Bohr
self.scf = SCFLoop(cc['eigenstates'] / Hartree**2 * nvalence,
cc['energy'] / Hartree * max(nvalence, 1),
cc['density'] * nvalence, par.maxiter,
par.fixdensity, niter_fixdensity, force_crit)
parsize_kpt = par.parallel['kpt']
parsize_domain = par.parallel['domain']
parsize_bands = par.parallel['band']
if not realspace:
pbc_c = np.ones(3, bool)
if not self.wfs:
if parsize_domain == 'domain only': # XXX this was silly!
parsize_domain = world.size
parallelization = mpi.Parallelization(world, nspins * kd.nibzkpts)
ndomains = None
if parsize_domain is not None:
ndomains = np.prod(parsize_domain)
if mode.name == 'pw':
if ndomains > 1:
raise ValueError('Planewave mode does not support '
'domain decomposition.')
ndomains = 1
parallelization.set(kpt=parsize_kpt,
domain=ndomains,
band=parsize_bands)
comms = parallelization.build_communicators()
domain_comm = comms['d']
kpt_comm = comms['k']
band_comm = comms['b']
kptband_comm = comms['D']
domainband_comm = comms['K']
self.comms = comms
kd.set_communicator(kpt_comm)
parstride_bands = par.parallel['stridebands']
# Unfortunately we need to remember that we adjusted the
# number of bands so we can print a warning if it differs
# from the number specified by the user. (The number can
# be inferred from the input parameters, but it's tricky
# because we allow negative numbers)
self.nbands_parallelization_adjustment = -nbands % band_comm.size
nbands += self.nbands_parallelization_adjustment
# I would like to give the following error message, but apparently
# there are cases, e.g. gpaw/test/gw_ppa.py, which involve
# nbands > nao and are supposed to work that way.
#if nbands > nao:
# raise ValueError('Number of bands %d adjusted for band '
# 'parallelization %d exceeds number of atomic '
# 'orbitals %d. This problem can be fixed '
# 'by reducing the number of bands a bit.'
# % (nbands, band_comm.size, nao))
bd = BandDescriptor(nbands, band_comm, parstride_bands)
if (self.density is not None
and self.density.gd.comm.size != domain_comm.size):
# Domain decomposition has changed, so we need to
# reinitialize density and hamiltonian:
if par.fixdensity:
raise RuntimeError(
'Density reinitialization conflict ' +
'with "fixdensity" - specify domain decomposition.')
self.density = None
self.hamiltonian = None
# Construct grid descriptor for coarse grids for wave functions:
gd = self.grid_descriptor_class(N_c, cell_cv, pbc_c, domain_comm,
parsize_domain)
# do k-point analysis here? XXX
args = (gd, nvalence, setups, bd, dtype, world, kd, kptband_comm,
self.timer)
if par.parallel['sl_auto']:
# Choose scalapack parallelization automatically
for key, val in par.parallel.items():
if (key.startswith('sl_') and key != 'sl_auto'
and val is not None):
raise ValueError("Cannot use 'sl_auto' together "
"with '%s'" % key)
max_scalapack_cpus = bd.comm.size * gd.comm.size
nprow = max_scalapack_cpus
npcol = 1
# Get a sort of reasonable number of columns/rows
while npcol < nprow and nprow % 2 == 0:
npcol *= 2
nprow //= 2
assert npcol * nprow == max_scalapack_cpus
# ScaLAPACK creates trouble if there aren't at least a few
# whole blocks; choose block size so there will always be
# several blocks. This will crash for small test systems,
# but so will ScaLAPACK in any case
blocksize = min(-(-nbands // 4), 64)
sl_default = (nprow, npcol, blocksize)
else:
sl_default = par.parallel['sl_default']
if mode.name == 'lcao':
# Layouts used for general diagonalizer
sl_lcao = par.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
lcaoksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
bd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer)
self.wfs = mode(collinear, lcaoksl, *args)
elif mode.name == 'fd' or mode.name == 'pw':
# buffer_size keyword only relevant for fdpw
buffer_size = par.parallel['buffer_size']
# Layouts used for diagonalizer
sl_diagonalize = par.parallel['sl_diagonalize']
if sl_diagonalize is None:
sl_diagonalize = sl_default
diagksl = get_KohnSham_layouts(
sl_diagonalize,
'fd', # XXX
# choice of key 'fd' not so nice
gd,
bd,
domainband_comm,
dtype,
buffer_size=buffer_size,
timer=self.timer)
# Layouts used for orthonormalizer
sl_inverse_cholesky = par.parallel['sl_inverse_cholesky']
if sl_inverse_cholesky is None:
sl_inverse_cholesky = sl_default
if sl_inverse_cholesky != sl_diagonalize:
message = 'sl_inverse_cholesky != sl_diagonalize ' \
'is not implemented.'
raise NotImplementedError(message)
orthoksl = get_KohnSham_layouts(sl_inverse_cholesky,
'fd',
gd,
bd,
domainband_comm,
dtype,
buffer_size=buffer_size,
timer=self.timer)
# Use (at most) all available LCAO for initialization
lcaonbands = min(nbands, nao)
try:
lcaobd = BandDescriptor(lcaonbands, band_comm,
parstride_bands)
except RuntimeError:
initksl = None
else:
# Layouts used for general diagonalizer
# (LCAO initialization)
sl_lcao = par.parallel['sl_lcao']
if sl_lcao is None:
sl_lcao = sl_default
initksl = get_KohnSham_layouts(sl_lcao,
'lcao',
gd,
lcaobd,
domainband_comm,
dtype,
nao=nao,
timer=self.timer)
if hasattr(self, 'time'):
assert mode.name == 'fd'
from gpaw.tddft import TimeDependentWaveFunctions
self.wfs = TimeDependentWaveFunctions(
par.stencils[0], diagksl, orthoksl, initksl, gd,
nvalence, setups, bd, world, kd, kptband_comm,
self.timer)
elif mode.name == 'fd':
self.wfs = mode(par.stencils[0], diagksl, orthoksl,
initksl, *args)
else:
assert mode.name == 'pw'
self.wfs = mode(diagksl, orthoksl, initksl, *args)
else:
self.wfs = mode(self, *args)
else:
self.wfs.set_setups(setups)
if not self.wfs.eigensolver:
# Number of bands to converge:
nbands_converge = cc['bands']
if nbands_converge == 'all':
nbands_converge = nbands
elif nbands_converge != 'occupied':
assert isinstance(nbands_converge, int)
if nbands_converge < 0:
nbands_converge += nbands
eigensolver = get_eigensolver(par.eigensolver, mode,
par.convergence)
eigensolver.nbands_converge = nbands_converge
# XXX Eigensolver class doesn't define an nbands_converge property
if isinstance(xc, SIC):
eigensolver.blocksize = 1
self.wfs.set_eigensolver(eigensolver)
if self.density is None:
gd = self.wfs.gd
if par.stencils[1] != 9:
# Construct grid descriptor for fine grids for densities
# and potentials:
finegd = gd.refine()
else:
# Special case (use only coarse grid):
finegd = gd
if realspace:
self.density = RealSpaceDensity(
gd, finegd, nspins, par.charge + setups.core_charge,
collinear, par.stencils[1])
else:
self.density = pw.ReciprocalSpaceDensity(
gd, finegd, nspins, par.charge + setups.core_charge,
collinear)
self.density.initialize(setups, self.timer, magmom_av, par.hund)
self.density.set_mixer(par.mixer)
if self.hamiltonian is None:
gd, finegd = self.density.gd, self.density.finegd
if realspace:
self.hamiltonian = RealSpaceHamiltonian(
gd, finegd, nspins, setups, self.timer, xc, world,
self.wfs.kptband_comm, par.external, collinear,
par.poissonsolver, par.stencils[1])
else:
self.hamiltonian = pw.ReciprocalSpaceHamiltonian(
gd, finegd, self.density.pd2, self.density.pd3, nspins,
setups, self.timer, xc, world, self.wfs.kptband_comm,
par.external, collinear)
xc.initialize(self.density, self.hamiltonian, self.wfs,
self.occupations)
self.text()
self.print_memory_estimate(self.txt, maxdepth=memory_estimate_depth)
self.txt.flush()
self.timer.print_info(self)
if dry_run:
self.dry_run()
if realspace and \
self.hamiltonian.poisson.get_description() == 'FDTD+TDDFT':
self.hamiltonian.poisson.set_density(self.density)
self.hamiltonian.poisson.print_messages(self.text)
self.txt.flush()
self.initialized = True
self._changed_keywords.clear()
def get_eigenmodes(self,
filename=None,
chi0=None,
calc=None,
dm=None,
xc='RPA',
sum=None,
vcut=None,
checkphase=False,
return_full=False):
"""
Calculate the plasmonic eigenmodes as eigenvectors of the dielectric matrix.
Parameters:
filename: pckl file
output from response calculation.
chi0: gpw file
chi0_wGG from response calculation.
calc: gpaw calculator instance
ground state calculator used in response calculation.
Wavefunctions only needed if chi0 is calculated from scratch
dm: gpw file
dielectric matrix from response calculation
xc: str 'RPA'or 'ALDA' XC- Kernel
sum: str
'2D': sum in the x and y directions
'1D': To be implemented
vcut: str '0D','1D' or '2D'
Cut the Coulomb potential
checkphase: Bool
if True, the eigenfunctions id rotated in the complex
plane, to be made as real as posible
return_full: Bool
if True, the eigenvectors in reciprocal space is also
returned.
"""
self.read(filename)
self.pbc = [1, 1, 1]
#self.calc.atoms.pbc = [1,1,1]
npw = self.npw
self.w_w = np.linspace(0, self.dw * (self.Nw - 1) * Hartree, self.Nw)
self.vcut = vcut
dm_wGG = self.get_dielectric_matrix(xc=xc,
symmetric=False,
chi0_wGG=chi0,
calc=calc,
vcut=vcut)
q = self.q_c
# get grid on which the eigenmodes are calculated
#gd = self.calc.wfs.gd
#r = gd.get_grid_point_coordinates()
#rrr = r*Bohr
from gpaw.utilities.gpts import get_number_of_grid_points
from gpaw.grid_descriptor import GridDescriptor
grid_size = [1, 1, 1]
h = 0.2
cell_cv = self.acell_cv * np.diag(grid_size)
mode = 'fd'
realspace = True
h /= Bohr
N_c = get_number_of_grid_points(cell_cv, h, mode, realspace)
gd = GridDescriptor(N_c, cell_cv, self.pbc)
#gd = self.calc.wfs.gd
r = gd.get_grid_point_coordinates()
rrr = r * Bohr
eig_0 = np.array([], dtype=complex)
eig_left = np.array([], dtype=complex)
eig_right = np.array([], dtype=complex)
vec_modes = np.zeros([1, self.npw], dtype=complex)
vec_modes_dual = np.zeros([1, self.npw], dtype=complex)
vec_modes_density = np.zeros([1, self.npw], dtype=complex)
vec_modes_norm = np.zeros([1, self.npw], dtype=complex)
eig_all = np.zeros([1, self.npw], dtype=complex)
eig_dummy = np.zeros([1, self.npw], dtype=complex)
v_dummy = np.zeros([1, self.npw], dtype=complex)
vec_dummy = np.zeros([1, self.npw], dtype=complex)
vec_dummy2 = np.zeros([1, self.npw], dtype=complex)
w_0 = np.array([])
w_left = np.array([])
w_right = np.array([])
if sum == '2D':
v_ind = np.zeros([1, r.shape[-1]], dtype=complex)
n_ind = np.zeros([1, r.shape[-1]], dtype=complex)
elif sum == '1D':
self.printtxt('1D sum not implemented')
return
else:
v_ind = np.zeros([1, r.shape[1], r.shape[2], r.shape[3]],
dtype=complex)
n_ind = np.zeros([1, r.shape[1], r.shape[2], r.shape[3]],
dtype=complex)
eps_GG_plus = dm_wGG[0]
eig_plus, vec_plus = np.linalg.eig(
eps_GG_plus) # find eigenvalues and eigenvectors
vec_plus_dual = np.linalg.inv(vec_plus)
# loop over frequencies, where the eigenvalues for the 2D matrix in G,G' are found.
for i in np.array(range(self.Nw - 1)) + 1:
eps_GG = eps_GG_plus
eig, vec = eig_plus, vec_plus
vec_dual = vec_plus_dual
eps_GG_plus = dm_wGG[i] # epsilon_GG'(omega + d-omega)
eig_plus, vec_plus = np.linalg.eig(eps_GG_plus)
vec_plus_dual = np.linalg.inv(vec_plus)
eig_dummy[0, :] = eig
eig_all = np.append(eig_all, eig_dummy,
axis=0) # append all eigenvalues to array
# loop to check find the eigenvalues that crosses zero from negative to positive values:
for k in range(self.npw):
for m in range(self.npw):
if eig[k] < 0 and 0 < eig_plus[m]:
# check it's the same mode - Overlap between eigenvectors should be large:
if abs(np.inner(vec[:, k],
vec_plus_dual[m, :])) > 0.95:
self.printtxt('crossing found at w = %1.1f eV' %
self.w_w[i - 1])
eig_left = np.append(eig_left, eig[k])
eig_right = np.append(eig_right, eig_plus[m])
vec_dummy[0, :] = vec[:, k]
vec_modes = np.append(vec_modes, vec_dummy, axis=0)
vec_dummy[0, :] = vec_dual[k, :].T
vec_modes_dual = np.append(vec_modes_dual,
vec_dummy,
axis=0)
w1 = self.w_w[i - 1]
w2 = self.w_w[i]
a = np.real((eig_plus[m] - eig[k]) / (w2 - w1))
w0 = np.real(-eig[k]) / a + w1
eig0 = a * (w0 - w1) + eig[k]
w_0 = np.append(w_0, w0)
w_left = np.append(w_left, w1)
eig_0 = np.append(eig_0, eig0)
n_dummy = np.zeros(
[1, r.shape[1], r.shape[2], r.shape[3]],
dtype=complex)
v_dummy = np.zeros(
[1, r.shape[1], r.shape[2], r.shape[3]],
dtype=complex)
vec_n = np.zeros([self.npw])
for iG in range(self.npw): # Fourier transform
qG = np.dot((q + self.Gvec_Gc[iG]),
self.bcell_cv)
coef_G = np.dot(qG, qG) / (4 * pi)
qGr_R = np.inner(qG, r.T).T
v_dummy += vec[iG, k] * np.exp(1j * qGr_R)
n_dummy += vec[iG, k] * np.exp(
1j * qGr_R) * coef_G
if checkphase: # rotate eigenvectors in complex plane
integral = np.zeros([81])
phases = np.linspace(0, 2, 81)
for ip in range(81):
v_int = v_dummy * np.exp(
1j * pi * phases[ip])
integral[ip] = abs(np.imag(v_int)).sum()
phase = phases[np.argsort(integral)][0]
v_dummy *= np.exp(1j * pi * phase)
n_dummy *= np.exp(1j * pi * phase)
if sum == '2D':
i_xyz = 3
v_dummy_z = np.zeros([1, v_dummy.shape[i_xyz]],
dtype=complex)
n_dummy_z = np.zeros([1, v_dummy.shape[i_xyz]],
dtype=complex)
v_dummy_z[0, :] = np.sum(np.sum(v_dummy,
axis=1),
axis=1)[0, :]
n_dummy_z[0, :] = np.sum(np.sum(n_dummy,
axis=1),
axis=1)[0, :]
v_ind = np.append(v_ind, v_dummy_z, axis=0)
n_ind = np.append(n_ind, n_dummy_z, axis=0)
elif sum == '1D':
self.printtxt('1D sum not implemented')
else:
v_ind = np.append(v_ind, v_dummy, axis=0)
n_ind = np.append(n_ind, n_dummy, axis=0)
"""
returns: grid points, frequency grid, all eigenvalues, mode energies, left point energies,
mode eigenvalues, eigenvalues of left and right-side points,
(mode eigenvectors, mode dual eigenvectors,)
induced potential in real space, induced density in real space
"""
if return_full:
return rrr, self.w_w, eig_all[1:], w_0, eig_0, w_left, eig_left, \
eig_right, vec_modes[1:], vec_modes_dual[1:], v_ind[1:], n_ind[1:]
else:
return rrr, self.w_w, eig_all[1:], w_0, eig_0, w_left, eig_left, \
eig_right, v_ind[1:], n_ind[1:]
def create_subsystems(self, atoms_in):
# Create new Atoms object
atoms_out = atoms_in.copy()
# New quantum grid
self.qm.cell = np.diag([(self.shift_indices_2[w] - self.shift_indices_1[w])*self.cl.spacing[w] for w in range(3)])
self.qm.spacing = self.cl.spacing / self.hratios
N_c = get_number_of_grid_points(self.qm.cell, self.qm.spacing)
atoms_out.set_cell(np.diag(self.qm.cell) * Bohr)
atoms_out.positions = atoms_in.get_positions() - self.qm.corner1 * Bohr
self.messages.append("Quantum box readjustment:")
self.messages.append(" Given cell: [%10.5f %10.5f %10.5f]" % tuple(np.diag(atoms_in.get_cell())))
self.messages.append(" Given atomic coordinates:")
for s, c in zip(atoms_in.get_chemical_symbols(), atoms_in.get_positions()):
self.messages.append(" %s %10.5f %10.5f %10.5f" % (s, c[0], c[1], c[2]))
self.messages.append(" Readjusted cell: [%10.5f %10.5f %10.5f]" % tuple(np.diag(atoms_out.get_cell())))
self.messages.append(" Readjusted atomic coordinates:")
for s, c in zip(atoms_out.get_chemical_symbols(), atoms_out.get_positions()):
self.messages.append(" %s %10.5f %10.5f %10.5f" % (s, c[0], c[1], c[2]))
self.messages.append(" Given corner points: (%10.5f %10.5f %10.5f) - (%10.5f %10.5f %10.5f)" %
(tuple(np.concatenate((self.given_corner_v1, self.given_corner_v2)))))
self.messages.append(" Readjusted corner points: (%10.5f %10.5f %10.5f) - (%10.5f %10.5f %10.5f)" %
(tuple(np.concatenate((self.qm.corner1,
self.qm.corner2)) * Bohr)))
self.messages.append(" Indices in classical grid: (%10i %10i %10i) - (%10i %10i %10i)" %
(tuple(np.concatenate((self.shift_indices_1,
self.shift_indices_2)))))
self.messages.append(" Grid points in classical grid: (%10i %10i %10i)" % (tuple(self.cl.gd.N_c)))
self.messages.append(" Grid points in quantum grid: (%10i %10i %10i)" % (tuple(N_c)))
self.messages.append(" Spacings in quantum grid: (%10.5f %10.5f %10.5f)" %
(tuple(np.diag(self.qm.cell) * Bohr / N_c)))
self.messages.append(" Spacings in classical grid: (%10.5f %10.5f %10.5f)" %
(tuple(np.diag(self.cl.cell) * Bohr / \
get_number_of_grid_points(self.cl.cell, self.cl.spacing))))
#self.messages.append(" Ratios of cl/qm spacings: (%10i %10i %10i)" % (tuple(self.hratios)))
#self.messages.append(" = (%10.2f %10.2f %10.2f)" %
# (tuple((np.diag(self.cl.cell) * Bohr / \
# get_number_of_grid_points(self.cl.cell,
# self.cl.spacing)) / \
# (np.diag(self.qm.cell) * Bohr / N_c))))
self.messages.append(" Needed number of refinements: %10i" % self.num_refinements)
# First, create the quantum grid equivalent GridDescriptor self.cl.subgd.
# Then coarsen it until its h_cv equals that of self.cl.gd.
# Finally, map the points between clgd and coarsened subgrid.
subcell_cv = np.diag(self.qm.corner2 - self.qm.corner1)
N_c = get_number_of_grid_points(subcell_cv, self.cl.spacing)
N_c = self.shift_indices_2 - self.shift_indices_1
self.cl.subgds = []
self.cl.subgds.append(GridDescriptor(N_c, subcell_cv, False, serial_comm, self.cl.dparsize))
#self.messages.append(" N_c/spacing of the subgrid: %3i %3i %3i / %.4f %.4f %.4f" %
# (self.cl.subgds[0].N_c[0],
# self.cl.subgds[0].N_c[1],
# self.cl.subgds[0].N_c[2],
# self.cl.subgds[0].h_cv[0][0] * Bohr,
# self.cl.subgds[0].h_cv[1][1] * Bohr,
# self.cl.subgds[0].h_cv[2][2] * Bohr))
#self.messages.append(" shape from the subgrid: %3i %3i %3i" % (tuple(self.cl.subgds[0].empty().shape)))
self.cl.coarseners = []
self.cl.refiners = []
for n in range(self.num_refinements):
self.cl.subgds.append(self.cl.subgds[n].refine())
self.cl.refiners.append(Transformer(self.cl.subgds[n], self.cl.subgds[n + 1]))
#self.messages.append(" refiners[%i] can perform the transformation (%3i %3i %3i) -> (%3i %3i %3i)" % (\
# n,
# self.cl.subgds[n].empty().shape[0],
# self.cl.subgds[n].empty().shape[1],
# self.cl.subgds[n].empty().shape[2],
# self.cl.subgds[n + 1].empty().shape[0],
# self.cl.subgds[n + 1].empty().shape[1],
# self.cl.subgds[n + 1].empty().shape[2]))
self.cl.coarseners.append(Transformer(self.cl.subgds[n + 1], self.cl.subgds[n]))
self.cl.coarseners[:] = self.cl.coarseners[::-1]
# Now extend the grid in order to handle the zero boundary conditions that the refiner assumes
# The default interpolation order
self.extend_nn = Transformer(GridDescriptor([8, 8, 8], [1, 1, 1], False, serial_comm, None),
GridDescriptor([8, 8, 8], [1, 1, 1], False, serial_comm, None).coarsen()).nn
self.extended_num_indices = self.num_indices + [2, 2, 2]
# Center, left, and right points of the suggested quantum grid
extended_cp = 0.5 * (np.array(self.given_corner_v1/Bohr) + np.array(self.given_corner_v2/Bohr))
extended_lp = extended_cp - 0.5 * (self.extended_num_indices) * self.cl.spacing
extended_rp = extended_cp + 0.5 * (self.extended_num_indices) * self.cl.spacing
# Indices in the classical grid restricting the quantum grid
self.extended_shift_indices_1 = np.round(extended_lp / self.cl.spacing)
self.extended_shift_indices_2 = self.extended_shift_indices_1 + self.extended_num_indices
#self.messages.append(' extended_shift_indices_1: %i %i %i' % (self.extended_shift_indices_1[0],self.extended_shift_indices_1[1], self.extended_shift_indices_1[2]))
#self.messages.append(' extended_shift_indices_2: %i %i %i' % (self.extended_shift_indices_2[0],self.extended_shift_indices_2[1], self.extended_shift_indices_2[2]))
#self.messages.append(' cl.gd.N_c: %i %i %i' % (self.cl.gd.N_c[0], self.cl.gd.N_c[1], self.cl.gd.N_c[2]))
# Sanity checks
assert(all([self.extended_shift_indices_1[w] >= 0 and
self.extended_shift_indices_2[w] <= self.cl.gd.N_c[w] for w in range(3)])), \
"Could not find appropriate quantum grid. Move it further away from the boundary."
# Corner coordinates
self.qm.extended_corner1 = self.extended_shift_indices_1 * self.cl.spacing
self.qm.extended_corner2 = self.extended_shift_indices_2 * self.cl.spacing
N_c = self.extended_shift_indices_2 - self.extended_shift_indices_1
self.cl.extended_subgds = []
self.cl.extended_refiners = []
extended_subcell_cv = np.diag(self.qm.extended_corner2 - self.qm.extended_corner1)
self.cl.extended_subgds.append(GridDescriptor(N_c,
extended_subcell_cv,
False,
serial_comm,
None))
for n in range(self.num_refinements):
self.cl.extended_subgds.append(self.cl.extended_subgds[n].refine())
self.cl.extended_refiners.append(Transformer(self.cl.extended_subgds[n], self.cl.extended_subgds[n + 1]))
#self.messages.append(" extended_refiners[%i] can perform the transformation (%3i %3i %3i) -> (%3i %3i %3i)" %
# (n,
# self.cl.extended_subgds[n].empty().shape[0],
# self.cl.extended_subgds[n].empty().shape[1],
# self.cl.extended_subgds[n].empty().shape[2],
# self.cl.extended_subgds[n + 1].empty().shape[0],
# self.cl.extended_subgds[n + 1].empty().shape[1],
# self.cl.extended_subgds[n + 1].empty().shape[2]))
#self.messages.append(" N_c/spacing of the refined subgrid: %3i %3i %3i / %.4f %.4f %.4f" %
# (self.cl.subgds[-1].N_c[0],
# self.cl.subgds[-1].N_c[1],
# self.cl.subgds[-1].N_c[2],
# self.cl.subgds[-1].h_cv[0][0] * Bohr,
# self.cl.subgds[-1].h_cv[1][1] * Bohr,
# self.cl.subgds[-1].h_cv[2][2] * Bohr))
#self.messages.append(" shape from the refined subgrid: %3i %3i %3i" %
# (tuple(self.cl.subgds[-1].empty().shape)))
self.extended_deltaIndex = 2 ** (self.num_refinements) * self.extend_nn
#self.messages.append(" self.extended_deltaIndex = %i" % self.extended_deltaIndex)
qgpts = self.cl.subgds[-1].coarsen().N_c
# Assure that one returns to the original shape
dmygd = self.cl.subgds[-1].coarsen()
for n in range(self.num_refinements - 1):
dmygd = dmygd.coarsen()
#self.messages.append(" N_c/spacing of the coarsened subgrid: %3i %3i %3i / %.4f %.4f %.4f" %
# (dmygd.N_c[0], dmygd.N_c[1], dmygd.N_c[2],
# dmygd.h_cv[0][0] * Bohr, dmygd.h_cv[1][1] * Bohr, dmygd.h_cv[2][2] * Bohr))
return atoms_out, self.qm.spacing[0] * Bohr, qgpts
def cut_cell(self, atoms_in, vacuum=5.0, corners=None, create_subsystems=True):
qmh = self.qm.spacing_def
if corners is not None:
v1 = np.array(corners[0]).ravel() / Bohr
v2 = np.array(corners[1]).ravel() / Bohr
else: # Use vacuum
pos_old = atoms_in.get_positions()[0];
dmy_atoms = atoms_in.copy()
dmy_atoms.center(vacuum=vacuum)
pos_new = dmy_atoms.get_positions()[0];
v1 = (pos_old - pos_new)/Bohr
v2 = v1 + np.diag(dmy_atoms.get_cell())/Bohr
# Needed for restarting
self.given_corner_v1 = v1 * Bohr
self.given_corner_v2 = v2 * Bohr
self.given_cell = atoms_in.get_cell()
# Sanity check: quantum box must be inside the classical one
assert(all([v1[w] <= v2[w] and
v1[w] >= 0 and
v2[w] <= np.diag(self.cl.cell)[w] for w in range(3)]))
# Ratios of the user-given spacings
self.hratios = self.cl.spacing_def / qmh
self.num_refinements = 1 + int(round(np.log(self.hratios[0]) / np.log(2.0)))
assert([int(round(np.log(self.hratios[w]) / np.log(2.0))) == self.num_refinements for w in range(3)])
# Create quantum grid
self.qm.cell = np.zeros((3, 3))
for w in range(3):
self.qm.cell[w, w] = v2[w] - v1[w]
N_c = get_number_of_grid_points(self.qm.cell, qmh)
self.qm.spacing = np.diag(self.qm.cell) / N_c
# Classical corner indices must be divisible with numb
if any(self.cl.spacing / self.qm.spacing >= 3):
numb = 1
elif any(self.cl.spacing / self.qm.spacing >= 2):
numb = 2
else:
numb = 4
# The index mismatch of the two simulation cells
self.num_indices = numb * np.ceil((np.array(v2) -
np.array(v1)) /
self.cl.spacing / numb)
self.num_indices_1 = numb * np.floor(np.array(v1) / self.cl.spacing / numb)
self.num_indices_2 = numb * np.ceil(np.array(v2) / self.cl.spacing / numb)
self.num_indices = self.num_indices_2 - self.num_indices_1
# Center, left, and right points of the suggested quantum grid
cp = 0.5 * (np.array(v1) + np.array(v2))
lp = cp - 0.5 * self.num_indices * self.cl.spacing
rp = cp + 0.5 * self.num_indices * self.cl.spacing
# Indices in the classical grid restricting the quantum grid
self.shift_indices_1 = np.round(lp / self.cl.spacing)
self.shift_indices_2 = self.shift_indices_1 + self.num_indices
# Sanity checks
assert(all([self.shift_indices_1[w] >= 0 and
self.shift_indices_2[w] <= self.cl.gd.N_c[w] for w in range(3)])), \
"Could not find appropriate quantum grid. Move it further away from the boundary."
# Corner coordinates
self.qm.corner1 = self.shift_indices_1 * self.cl.spacing
self.qm.corner2 = self.shift_indices_2 * self.cl.spacing
# Now the information for creating the subsystems is ready
if create_subsystems:
return self.create_subsystems(atoms_in)
