class Density:
"""Density object.
Attributes:
=============== =====================================================
``gd`` Grid descriptor for coarse grids.
``finegd`` Grid descriptor for fine grids.
``interpolate`` Function for interpolating the electron density.
``mixer`` ``DensityMixer`` object.
=============== =====================================================
Soft and smooth pseudo functions on uniform 3D grids:
========== =========================================
``nt_sG`` Electron density on the coarse grid.
``nt_sg`` Electron density on the fine grid.
``nt_g`` Electron density on the fine grid.
``rhot_g`` Charge density on the fine grid.
``nct_G`` Core electron-density on the coarse grid.
========== =========================================
"""
def __init__(self, gd, finegd, nspins, charge, collinear=True):
"""Create the Density object."""
self.gd = gd
self.finegd = finegd
self.nspins = nspins
self.charge = float(charge)
self.collinear = collinear
self.ncomp = 2 - int(collinear)
self.charge_eps = 1e-7
self.D_asp = None
self.Q_aL = None
self.nct_G = None
self.nt_sG = None
self.rhot_g = None
self.nt_sg = None
self.nt_g = None
self.rank_a = None
self.mixer = BaseMixer()
self.timer = nulltimer
def initialize(self, setups, timer, magmom_av, hund):
self.timer = timer
self.setups = setups
self.hund = hund
self.magmom_av = magmom_av
def reset(self):
# TODO: reset other parameters?
self.nt_sG = None
def set_positions(self, spos_ac, rank_a=None):
self.nct.set_positions(spos_ac)
self.ghat.set_positions(spos_ac)
self.mixer.reset()
#self.nt_sG = None
self.nt_sg = None
self.nt_g = None
self.rhot_g = None
self.Q_aL = None
# If both old and new atomic ranks are present, start a blank dict if
# it previously didn't exist but it will needed for the new atoms.
if (self.rank_a is not None and rank_a is not None and
self.D_asp is None and (rank_a == self.gd.comm.rank).any()):
self.D_asp = {}
if (self.rank_a is not None and self.D_asp is not None and
rank_a is not None and not isinstance(self.gd.comm, SerialCommunicator)):
self.timer.start('Redistribute')
requests = []
flags = (self.rank_a != rank_a)
my_incoming_atom_indices = np.argwhere(np.bitwise_and(flags, \
rank_a == self.gd.comm.rank)).ravel()
my_outgoing_atom_indices = np.argwhere(np.bitwise_and(flags, \
self.rank_a == self.gd.comm.rank)).ravel()
for a in my_incoming_atom_indices:
# Get matrix from old domain:
ni = self.setups[a].ni
D_sp = np.empty((self.nspins * self.ncomp**2,
ni * (ni + 1) // 2))
requests.append(self.gd.comm.receive(D_sp, self.rank_a[a],
tag=a, block=False))
assert a not in self.D_asp
self.D_asp[a] = D_sp
for a in my_outgoing_atom_indices:
# Send matrix to new domain:
D_sp = self.D_asp.pop(a)
requests.append(self.gd.comm.send(D_sp, rank_a[a],
tag=a, block=False))
self.gd.comm.waitall(requests)
self.timer.stop('Redistribute')
self.rank_a = rank_a
def calculate_pseudo_density(self, wfs):
"""Calculate nt_sG from scratch.
nt_sG will be equal to nct_G plus the contribution from
wfs.add_to_density().
"""
wfs.calculate_density_contribution(self.nt_sG)
self.nt_sG[:self.nspins] += self.nct_G
def update(self, wfs):
self.timer.start('Density')
self.timer.start('Pseudo density')
self.calculate_pseudo_density(wfs)
self.timer.stop('Pseudo density')
self.timer.start('Atomic density matrices')
wfs.calculate_atomic_density_matrices(self.D_asp)
self.timer.stop('Atomic density matrices')
self.timer.start('Multipole moments')
comp_charge = self.calculate_multipole_moments()
self.timer.stop('Multipole moments')
if isinstance(wfs, LCAOWaveFunctions):
self.timer.start('Normalize')
self.normalize(comp_charge)
self.timer.stop('Normalize')
self.timer.start('Mix')
self.mix(comp_charge)
self.timer.stop('Mix')
self.timer.stop('Density')
def normalize(self, comp_charge=None):
"""Normalize pseudo density."""
if comp_charge is None:
comp_charge = self.calculate_multipole_moments()
pseudo_charge = self.gd.integrate(self.nt_sG[:self.nspins]).sum()
if pseudo_charge + self.charge + comp_charge != 0:
if pseudo_charge != 0:
x = -(self.charge + comp_charge) / pseudo_charge
self.nt_sG *= x
else:
# Use homogeneous background:
volume = self.gd.get_size_of_global_array().prod() * self.gd.dv
self.nt_sG[:self.nspins] = -(self.charge +
comp_charge) / volume
def mix(self, comp_charge):
if not self.mixer.mix_rho:
self.mixer.mix(self)
comp_charge = None
self.interpolate_pseudo_density(comp_charge)
self.calculate_pseudo_charge()
if self.mixer.mix_rho:
self.mixer.mix(self)
def calculate_multipole_moments(self):
"""Calculate multipole moments of compensation charges.
Returns the total compensation charge in units of electron
charge, so the number will be negative because of the
dominating contribution from the nuclear charge."""
comp_charge = 0.0
self.Q_aL = {}
for a, D_sp in self.D_asp.items():
Q_L = self.Q_aL[a] = np.dot(D_sp[:self.nspins].sum(0),
self.setups[a].Delta_pL)
Q_L[0] += self.setups[a].Delta0
comp_charge += Q_L[0]
return self.gd.comm.sum(comp_charge) * sqrt(4 * pi)
def initialize_from_atomic_densities(self, basis_functions):
"""Initialize D_asp, nt_sG and Q_aL from atomic densities.
nt_sG is initialized from atomic orbitals, and will
be constructed with the specified magnetic moments and
obeying Hund's rules if ``hund`` is true."""
# XXX does this work with blacs? What should be distributed?
# Apparently this doesn't use blacs at all, so it's serial
# with respect to the blacs distribution. That means it works
# but is not particularly efficient (not that this is a time
# consuming step)
self.D_asp = {}
f_asi = {}
for a in basis_functions.atom_indices:
c = self.charge / len(self.setups) # distribute on all atoms
M_v = self.magmom_av[a]
M = (M_v**2).sum()**0.5
f_si = self.setups[a].calculate_initial_occupation_numbers(
M, self.hund, charge=c,
nspins=self.nspins * self.ncomp)
if self.collinear:
if M_v[2] < 0:
f_si = f_si[::-1].copy()
else:
f_i = f_si.sum(axis=0)
fm_i = f_si[0] - f_si[1]
f_si = np.zeros((4, len(f_i)))
f_si[0] = f_i
if M > 0:
f_si[1:4] = np.outer(M_v / M, fm_i)
if a in basis_functions.my_atom_indices:
self.D_asp[a] = self.setups[a].initialize_density_matrix(f_si)
f_asi[a] = f_si
self.nt_sG = self.gd.zeros(self.nspins * self.ncomp**2)
basis_functions.add_to_density(self.nt_sG, f_asi)
self.nt_sG[:self.nspins] += self.nct_G
self.calculate_normalized_charges_and_mix()
def initialize_from_wavefunctions(self, wfs):
"""Initialize D_asp, nt_sG and Q_aL from wave functions."""
self.nt_sG = self.gd.empty(self.nspins * self.ncomp**2)
self.calculate_pseudo_density(wfs)
self.D_asp = {}
my_atom_indices = np.argwhere(wfs.rank_a == self.gd.comm.rank).ravel()
for a in my_atom_indices:
ni = self.setups[a].ni
self.D_asp[a] = np.empty((self.nspins, ni * (ni + 1) // 2))
wfs.calculate_atomic_density_matrices(self.D_asp)
self.calculate_normalized_charges_and_mix()
def initialize_directly_from_arrays(self, nt_sG, D_asp):
"""Set D_asp and nt_sG directly."""
self.nt_sG = nt_sG
self.D_asp = D_asp
#self.calculate_normalized_charges_and_mix()
# No calculate multipole moments? Tests will fail because of
# improperly initialized mixer
def calculate_normalized_charges_and_mix(self):
comp_charge = self.calculate_multipole_moments()
self.normalize(comp_charge)
self.mix(comp_charge)
def set_mixer(self, mixer):
if mixer is not None:
if self.nspins == 1 and isinstance(mixer, MixerSum):
raise RuntimeError('Cannot use MixerSum with nspins==1')
self.mixer = mixer
else:
if self.gd.pbc_c.any():
beta = 0.05
history = 5
weight = 50.0
else:
beta = 0.25
history = 3
weight = 1.0
if self.nspins == 2:
self.mixer = MixerSum(beta, history, weight)
else:
self.mixer = Mixer(beta, history, weight)
self.mixer.initialize(self)
def estimate_magnetic_moments(self):
magmom_av = np.zeros_like(self.magmom_av)
if self.nspins == 2 or not self.collinear:
for a, D_sp in self.D_asp.items():
if self.collinear:
magmom_av[a, 2] = np.dot(D_sp[0] - D_sp[1],
self.setups[a].N0_p)
else:
magmom_av[a] = np.dot(D_sp[1:4], self.setups[a].N0_p)
self.gd.comm.sum(magmom_av)
return magmom_av
def get_correction(self, a, spin):
"""Integrated atomic density correction.
Get the integrated correction to the pseuso density relative to
the all-electron density.
"""
setup = self.setups[a]
return sqrt(4 * pi) * (
np.dot(self.D_asp[a][spin], setup.Delta_pL[:, 0])
+ setup.Delta0 / self.nspins)
def get_all_electron_density(self, atoms, gridrefinement=2):
"""Return real all-electron density array."""
# Refinement of coarse grid, for representation of the AE-density
if gridrefinement == 1:
gd = self.gd
n_sg = self.nt_sG.copy()
elif gridrefinement == 2:
gd = self.finegd
if self.nt_sg is None:
self.interpolate_pseudo_density()
n_sg = self.nt_sg.copy()
elif gridrefinement == 4:
# Extra fine grid
gd = self.finegd.refine()
# Interpolation function for the density:
interpolator = Transformer(self.finegd, gd, 3)
# Transfer the pseudo-density to the fine grid:
n_sg = gd.empty(self.nspins)
if self.nt_sg is None:
self.interpolate_pseudo_density()
for s in range(self.nspins):
interpolator.apply(self.nt_sg[s], n_sg[s])
else:
raise NotImplementedError
# Add corrections to pseudo-density to get the AE-density
splines = {}
phi_aj = []
phit_aj = []
nc_a = []
nct_a = []
for a, id in enumerate(self.setups.id_a):
if id in splines:
phi_j, phit_j, nc, nct = splines[id]
else:
# Load splines:
phi_j, phit_j, nc, nct = self.setups[a].get_partial_waves()[:4]
splines[id] = (phi_j, phit_j, nc, nct)
phi_aj.append(phi_j)
phit_aj.append(phit_j)
nc_a.append([nc])
nct_a.append([nct])
# Create localized functions from splines
phi = BasisFunctions(gd, phi_aj)
phit = BasisFunctions(gd, phit_aj)
nc = LFC(gd, nc_a)
nct = LFC(gd, nct_a)
spos_ac = atoms.get_scaled_positions() % 1.0
phi.set_positions(spos_ac)
phit.set_positions(spos_ac)
nc.set_positions(spos_ac)
nct.set_positions(spos_ac)
I_sa = np.zeros((self.nspins, len(atoms)))
a_W = np.empty(len(phi.M_W), np.intc)
W = 0
for a in phi.atom_indices:
nw = len(phi.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
x_W = phi.create_displacement_arrays()[0]
rho_MM = np.zeros((phi.Mmax, phi.Mmax))
for s, I_a in enumerate(I_sa):
M1 = 0
for a, setup in enumerate(self.setups):
ni = setup.ni
D_sp = self.D_asp.get(a)
if D_sp is None:
D_sp = np.empty((self.nspins, ni * (ni + 1) // 2))
else:
I_a[a] = ((setup.Nct - setup.Nc) / self.nspins -
sqrt(4 * pi) *
np.dot(D_sp[s], setup.Delta_pL[:, 0]))
if gd.comm.size > 1:
gd.comm.broadcast(D_sp, self.rank_a[a])
M2 = M1 + ni
rho_MM[M1:M2, M1:M2] = unpack2(D_sp[s])
M1 = M2
phi.lfc.ae_valence_density_correction(rho_MM, n_sg[s], a_W, I_a,
x_W)
phit.lfc.ae_valence_density_correction(-rho_MM, n_sg[s], a_W, I_a,
x_W)
a_W = np.empty(len(nc.M_W), np.intc)
W = 0
for a in nc.atom_indices:
nw = len(nc.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
scale = 1.0 / self.nspins
for s, I_a in enumerate(I_sa):
nc.lfc.ae_core_density_correction(scale, n_sg[s], a_W, I_a)
nct.lfc.ae_core_density_correction(-scale, n_sg[s], a_W, I_a)
gd.comm.sum(I_a)
N_c = gd.N_c
g_ac = np.around(N_c * spos_ac).astype(int) % N_c - gd.beg_c
for I, g_c in zip(I_a, g_ac):
if (g_c >= 0).all() and (g_c < gd.n_c).all():
n_sg[s][tuple(g_c)] -= I / gd.dv
return n_sg, gd
def estimate_memory(self, mem):
nspins = self.nspins
nbytes = self.gd.bytecount()
nfinebytes = self.finegd.bytecount()
arrays = mem.subnode('Arrays')
for name, size in [('nt_sG', nbytes * nspins),
('nt_sg', nfinebytes * nspins),
('nt_g', nfinebytes),
('rhot_g', nfinebytes),
('nct_G', nbytes)]:
arrays.subnode(name, size)
lfs = mem.subnode('Localized functions')
for name, obj in [('nct', self.nct),
('ghat', self.ghat)]:
obj.estimate_memory(lfs.subnode(name))
self.mixer.estimate_memory(mem.subnode('Mixer'), self.gd)
# TODO
# The implementation of interpolator memory use is not very
# accurate; 20 MiB vs 13 MiB estimated in one example, probably
# worse for parallel calculations.
def get_spin_contamination(self, atoms, majority_spin=0):
"""Calculate the spin contamination.
Spin contamination is defined as the integral over the
spin density difference, where it is negative (i.e. the
minority spin density is larger than the majority spin density.
"""
if majority_spin == 0:
smaj = 0
smin = 1
else:
smaj = 1
smin = 0
nt_sg, gd = self.get_all_electron_density(atoms)
dt_sg = nt_sg[smin] - nt_sg[smaj]
dt_sg = np.where(dt_sg > 0, dt_sg, 0.0)
return gd.integrate(dt_sg)
def read(self, reader, parallel, kd, bd):
if reader['version'] > 0.3:
density_error = reader['DensityError']
if density_error is not None:
self.mixer.set_charge_sloshing(density_error)
if not reader.has_array('PseudoElectronDensity'):
return
hdf5 = hasattr(reader, 'hdf5')
nt_sG = self.gd.empty(self.nspins)
if hdf5:
# Read pseudoelectron density on the coarse grid
# and broadcast on kpt_comm and band_comm:
indices = [slice(0, self.nspins)] + self.gd.get_slice()
do_read = (kd.comm.rank == 0) and (bd.comm.rank == 0)
reader.get('PseudoElectronDensity', out=nt_sG, parallel=parallel,
read=do_read, *indices) # XXX read=?
kd.comm.broadcast(nt_sG, 0)
bd.comm.broadcast(nt_sG, 0)
else:
for s in range(self.nspins):
self.gd.distribute(reader.get('PseudoElectronDensity', s),
nt_sG[s])
# Read atomic density matrices
D_asp = {}
natoms = len(self.setups)
self.rank_a = np.zeros(natoms, int)
all_D_sp = reader.get('AtomicDensityMatrices', broadcast=True)
if self.gd.comm.rank == 0:
D_asp = read_atomic_matrices(all_D_sp, self.setups)
self.initialize_directly_from_arrays(nt_sG, D_asp)
class Density:
"""Density object.
Attributes:
=============== =====================================================
``gd`` Grid descriptor for coarse grids.
``finegd`` Grid descriptor for fine grids.
``interpolate`` Function for interpolating the electron density.
``mixer`` ``DensityMixer`` object.
=============== =====================================================
Soft and smooth pseudo functions on uniform 3D grids:
========== =========================================
``nt_sG`` Electron density on the coarse grid.
``nt_sg`` Electron density on the fine grid.
``nt_g`` Electron density on the fine grid.
``rhot_g`` Charge density on the fine grid.
``nct_G`` Core electron-density on the coarse grid.
========== =========================================
"""
def __init__(self, gd, finegd, nspins, charge):
"""Create the Density object."""
self.gd = gd
self.finegd = finegd
self.nspins = nspins
self.charge = float(charge)
self.charge_eps = 1e-7
self.D_asp = None
self.Q_aL = None
self.nct_G = None
self.nt_sG = None
self.rhot_g = None
self.nt_sg = None
self.nt_g = None
self.rank_a = None
self.mixer = BaseMixer()
self.timer = nulltimer
self.allocated = False
def initialize(self, setups, stencil, timer, magmom_a, hund):
self.timer = timer
self.setups = setups
self.hund = hund
self.magmom_a = magmom_a
# Interpolation function for the density:
self.interpolator = Transformer(self.gd, self.finegd, stencil,
allocate=False)
spline_aj = []
for setup in setups:
if setup.nct is None:
spline_aj.append([])
else:
spline_aj.append([setup.nct])
self.nct = LFC(self.gd, spline_aj,
integral=[setup.Nct for setup in setups],
forces=True, cut=True)
self.ghat = LFC(self.finegd, [setup.ghat_l for setup in setups],
integral=sqrt(4 * pi), forces=True)
if self.allocated:
self.allocated = False
self.allocate()
def allocate(self):
assert not self.allocated
self.interpolator.allocate()
self.allocated = True
def reset(self):
# TODO: reset other parameters?
self.nt_sG = None
def set_positions(self, spos_ac, rank_a=None):
if not self.allocated:
self.allocate()
self.nct.set_positions(spos_ac)
self.ghat.set_positions(spos_ac)
self.mixer.reset()
self.nct_G = self.gd.zeros()
self.nct.add(self.nct_G, 1.0 / self.nspins)
#self.nt_sG = None
self.nt_sg = None
self.nt_g = None
self.rhot_g = None
self.Q_aL = None
# If both old and new atomic ranks are present, start a blank dict if
# it previously didn't exist but it will needed for the new atoms.
if (self.rank_a is not None and rank_a is not None and
self.D_asp is None and (rank_a == self.gd.comm.rank).any()):
self.D_asp = {}
if self.rank_a is not None and self.D_asp is not None:
self.timer.start('Redistribute')
requests = []
flags = (self.rank_a != rank_a)
my_incoming_atom_indices = np.argwhere(np.bitwise_and(flags, \
rank_a == self.gd.comm.rank)).ravel()
my_outgoing_atom_indices = np.argwhere(np.bitwise_and(flags, \
self.rank_a == self.gd.comm.rank)).ravel()
for a in my_incoming_atom_indices:
# Get matrix from old domain:
ni = self.setups[a].ni
D_sp = np.empty((self.nspins, ni * (ni + 1) // 2))
requests.append(self.gd.comm.receive(D_sp, self.rank_a[a],
tag=a, block=False))
assert a not in self.D_asp
self.D_asp[a] = D_sp
for a in my_outgoing_atom_indices:
# Send matrix to new domain:
D_sp = self.D_asp.pop(a)
requests.append(self.gd.comm.send(D_sp, rank_a[a],
tag=a, block=False))
self.gd.comm.waitall(requests)
self.timer.stop('Redistribute')
self.rank_a = rank_a
def calculate_pseudo_density(self, wfs):
"""Calculate nt_sG from scratch.
nt_sG will be equal to nct_G plus the contribution from
wfs.add_to_density().
"""
wfs.calculate_density_contribution(self.nt_sG)
self.nt_sG += self.nct_G
def update(self, wfs):
self.timer.start('Density')
self.timer.start('Pseudo density')
self.calculate_pseudo_density(wfs)
self.timer.stop('Pseudo density')
self.timer.start('Atomic density matrices')
wfs.calculate_atomic_density_matrices(self.D_asp)
self.timer.stop('Atomic density matrices')
self.timer.start('Multipole moments')
comp_charge = self.calculate_multipole_moments()
self.timer.stop('Multipole moments')
if isinstance(wfs, LCAOWaveFunctions):
self.timer.start('Normalize')
self.normalize(comp_charge)
self.timer.stop('Normalize')
self.timer.start('Mix')
self.mix(comp_charge)
self.timer.stop('Mix')
self.timer.stop('Density')
def normalize(self, comp_charge=None):
"""Normalize pseudo density."""
if comp_charge is None:
comp_charge = self.calculate_multipole_moments()
pseudo_charge = self.gd.integrate(self.nt_sG).sum()
if pseudo_charge + self.charge + comp_charge != 0:
if pseudo_charge != 0:
x = -(self.charge + comp_charge) / pseudo_charge
self.nt_sG *= x
else:
# Use homogeneous background:
self.nt_sG[:] = (self.charge + comp_charge) * self.gd.dv
def calculate_pseudo_charge(self, comp_charge):
self.nt_g = self.nt_sg.sum(axis=0)
self.rhot_g = self.nt_g.copy()
self.ghat.add(self.rhot_g, self.Q_aL)
if debug:
charge = self.finegd.integrate(self.rhot_g) + self.charge
if abs(charge) > self.charge_eps:
raise RuntimeError('Charge not conserved: excess=%.9f' %
charge)
def mix(self, comp_charge):
if not self.mixer.mix_rho:
self.mixer.mix(self)
comp_charge = None
self.interpolate(comp_charge)
self.calculate_pseudo_charge(comp_charge)
if self.mixer.mix_rho:
self.mixer.mix(self)
def interpolate(self, comp_charge=None):
"""Interpolate pseudo density to fine grid."""
if comp_charge is None:
comp_charge = self.calculate_multipole_moments()
if self.nt_sg is None:
self.nt_sg = self.finegd.empty(self.nspins)
for s in range(self.nspins):
self.interpolator.apply(self.nt_sG[s], self.nt_sg[s])
# With periodic boundary conditions, the interpolation will
# conserve the number of electrons.
if not self.gd.pbc_c.all():
# With zero-boundary conditions in one or more directions,
# this is not the case.
pseudo_charge = -(self.charge + comp_charge)
if abs(pseudo_charge) > 1.0e-14:
x = pseudo_charge / self.finegd.integrate(self.nt_sg).sum()
self.nt_sg *= x
def calculate_multipole_moments(self):
"""Calculate multipole moments of compensation charges.
Returns the total compensation charge in units of electron
charge, so the number will be negative because of the
dominating contribution from the nuclear charge."""
comp_charge = 0.0
self.Q_aL = {}
for a, D_sp in self.D_asp.items():
Q_L = self.Q_aL[a] = np.dot(D_sp.sum(0), self.setups[a].Delta_pL)
Q_L[0] += self.setups[a].Delta0
comp_charge += Q_L[0]
return self.gd.comm.sum(comp_charge) * sqrt(4 * pi)
def initialize_from_atomic_densities(self, basis_functions):
"""Initialize D_asp, nt_sG and Q_aL from atomic densities.
nt_sG is initialized from atomic orbitals, and will
be constructed with the specified magnetic moments and
obeying Hund's rules if ``hund`` is true."""
# XXX does this work with blacs? What should be distributed?
# Apparently this doesn't use blacs at all, so it's serial
# with respect to the blacs distribution. That means it works
# but is not particularly efficient (not that this is a time
# consuming step)
f_sM = np.empty((self.nspins, basis_functions.Mmax))
self.D_asp = {}
f_asi = {}
for a in basis_functions.atom_indices:
c = self.charge / len(self.setups) # distribute on all atoms
f_si = self.setups[a].calculate_initial_occupation_numbers(
self.magmom_a[a], self.hund, charge=c, nspins=self.nspins)
if a in basis_functions.my_atom_indices:
self.D_asp[a] = self.setups[a].initialize_density_matrix(f_si)
f_asi[a] = f_si
self.nt_sG = self.gd.zeros(self.nspins)
basis_functions.add_to_density(self.nt_sG, f_asi)
self.nt_sG += self.nct_G
self.calculate_normalized_charges_and_mix()
def initialize_from_wavefunctions(self, wfs):
"""Initialize D_asp, nt_sG and Q_aL from wave functions."""
self.nt_sG = self.gd.empty(self.nspins)
self.calculate_pseudo_density(wfs)
self.D_asp = {}
my_atom_indices = np.argwhere(wfs.rank_a == self.gd.comm.rank).ravel()
for a in my_atom_indices:
ni = self.setups[a].ni
self.D_asp[a] = np.empty((self.nspins, ni * (ni + 1) // 2))
wfs.calculate_atomic_density_matrices(self.D_asp)
self.calculate_normalized_charges_and_mix()
def initialize_directly_from_arrays(self, nt_sG, D_asp):
"""Set D_asp and nt_sG directly."""
self.nt_sG = nt_sG
self.D_asp = D_asp
#self.calculate_normalized_charges_and_mix()
# No calculate multipole moments? Tests will fail because of
# improperly initialized mixer
def calculate_normalized_charges_and_mix(self):
comp_charge = self.calculate_multipole_moments()
self.normalize(comp_charge)
self.mix(comp_charge)
def set_mixer(self, mixer):
if mixer is not None:
if self.nspins == 1 and isinstance(mixer, MixerSum):
raise RuntimeError('Cannot use MixerSum with nspins==1')
self.mixer = mixer
else:
if self.gd.pbc_c.any():
beta = 0.1
weight = 50.0
else:
beta = 0.25
weight = 1.0
if self.nspins == 2:
self.mixer = MixerSum(beta=beta, weight=weight)
else:
self.mixer = Mixer(beta=beta, weight=weight)
self.mixer.initialize(self)
def estimate_magnetic_moments(self):
magmom_a = np.zeros_like(self.magmom_a)
if self.nspins == 2:
for a, D_sp in self.D_asp.items():
magmom_a[a] = np.dot(D_sp[0] - D_sp[1], self.setups[a].N0_p)
self.gd.comm.sum(magmom_a)
return magmom_a
def get_correction(self, a, spin):
"""Integrated atomic density correction.
Get the integrated correction to the pseuso density relative to
the all-electron density.
"""
setup = self.setups[a]
return sqrt(4 * pi) * (
np.dot(self.D_asp[a][spin], setup.Delta_pL[:, 0])
+ setup.Delta0 / self.nspins)
def get_density_array(self):
XXX
# XXX why not replace with get_spin_density and get_total_density?
"""Return pseudo-density array."""
if self.nspins == 2:
return self.nt_sG
else:
return self.nt_sG[0]
def get_all_electron_density(self, atoms, gridrefinement=2):
"""Return real all-electron density array."""
# Refinement of coarse grid, for representation of the AE-density
if gridrefinement == 1:
gd = self.gd
n_sg = self.nt_sG.copy()
elif gridrefinement == 2:
gd = self.finegd
if self.nt_sg is None:
self.interpolate()
n_sg = self.nt_sg.copy()
elif gridrefinement == 4:
# Extra fine grid
gd = self.finegd.refine()
# Interpolation function for the density:
interpolator = Transformer(self.finegd, gd, 3)
# Transfer the pseudo-density to the fine grid:
n_sg = gd.empty(self.nspins)
if self.nt_sg is None:
self.interpolate()
for s in range(self.nspins):
interpolator.apply(self.nt_sg[s], n_sg[s])
else:
raise NotImplementedError
# Add corrections to pseudo-density to get the AE-density
splines = {}
phi_aj = []
phit_aj = []
nc_a = []
nct_a = []
for a, id in enumerate(self.setups.id_a):
if id in splines:
phi_j, phit_j, nc, nct = splines[id]
else:
# Load splines:
phi_j, phit_j, nc, nct = self.setups[a].get_partial_waves()[:4]
splines[id] = (phi_j, phit_j, nc, nct)
phi_aj.append(phi_j)
phit_aj.append(phit_j)
nc_a.append([nc])
nct_a.append([nct])
# Create localized functions from splines
phi = LFC(gd, phi_aj)
phit = LFC(gd, phit_aj)
nc = LFC(gd, nc_a)
nct = LFC(gd, nct_a)
spos_ac = atoms.get_scaled_positions() % 1.0
phi.set_positions(spos_ac)
phit.set_positions(spos_ac)
nc.set_positions(spos_ac)
nct.set_positions(spos_ac)
all_D_asp = []
for a, setup in enumerate(self.setups):
D_sp = self.D_asp.get(a)
if D_sp is None:
ni = setup.ni
D_sp = np.empty((self.nspins, ni * (ni + 1) // 2))
if gd.comm.size > 1:
gd.comm.broadcast(D_sp, self.rank_a[a])
all_D_asp.append(D_sp)
for s in range(self.nspins):
I_a = np.zeros(len(atoms))
nc.add1(n_sg[s], 1.0 / self.nspins, I_a)
nct.add1(n_sg[s], -1.0 / self.nspins, I_a)
phi.add2(n_sg[s], all_D_asp, s, 1.0, I_a)
phit.add2(n_sg[s], all_D_asp, s, -1.0, I_a)
for a, D_sp in self.D_asp.items():
setup = self.setups[a]
I_a[a] -= ((setup.Nc - setup.Nct) / self.nspins +
sqrt(4 * pi) *
np.dot(D_sp[s], setup.Delta_pL[:, 0]))
gd.comm.sum(I_a)
N_c = gd.N_c
g_ac = np.around(N_c * spos_ac).astype(int) % N_c - gd.beg_c
for I, g_c in zip(I_a, g_ac):
if (g_c >= 0).all() and (g_c < gd.n_c).all():
n_sg[s][tuple(g_c)] -= I / gd.dv
return n_sg, gd
def new_get_all_electron_density(self, atoms, gridrefinement=2):
"""Return real all-electron density array."""
# Refinement of coarse grid, for representation of the AE-density
if gridrefinement == 1:
gd = self.gd
n_sg = self.nt_sG.copy()
elif gridrefinement == 2:
gd = self.finegd
if self.nt_sg is None:
self.interpolate()
n_sg = self.nt_sg.copy()
elif gridrefinement == 4:
# Extra fine grid
gd = self.finegd.refine()
# Interpolation function for the density:
interpolator = Transformer(self.finegd, gd, 3)
# Transfer the pseudo-density to the fine grid:
n_sg = gd.empty(self.nspins)
if self.nt_sg is None:
self.interpolate()
for s in range(self.nspins):
interpolator.apply(self.nt_sg[s], n_sg[s])
else:
raise NotImplementedError
# Add corrections to pseudo-density to get the AE-density
splines = {}
phi_aj = []
phit_aj = []
nc_a = []
nct_a = []
for a, id in enumerate(self.setups.id_a):
if id in splines:
phi_j, phit_j, nc, nct = splines[id]
else:
# Load splines:
phi_j, phit_j, nc, nct = self.setups[a].get_partial_waves()[:4]
splines[id] = (phi_j, phit_j, nc, nct)
phi_aj.append(phi_j)
phit_aj.append(phit_j)
nc_a.append([nc])
nct_a.append([nct])
# Create localized functions from splines
phi = BasisFunctions(gd, phi_aj)
phit = BasisFunctions(gd, phit_aj)
nc = LFC(gd, nc_a)
nct = LFC(gd, nct_a)
spos_ac = atoms.get_scaled_positions() % 1.0
phi.set_positions(spos_ac)
phit.set_positions(spos_ac)
nc.set_positions(spos_ac)
nct.set_positions(spos_ac)
I_sa = np.zeros((self.nspins, len(atoms)))
a_W = np.empty(len(phi.M_W), np.int32)
W = 0
for a in phi.atom_indices:
nw = len(phi.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
rho_MM = np.zeros((phi.Mmax, phi.Mmax))
for s, I_a in enumerate(I_sa):
M1 = 0
for a, setup in enumerate(self.setups):
ni = setup.ni
D_sp = self.D_asp.get(a)
if D_sp is None:
D_sp = np.empty((self.nspins, ni * (ni + 1) // 2))
else:
I_a[a] = ((setup.Nct - setup.Nc) / self.nspins -
sqrt(4 * pi) *
np.dot(D_sp[s], setup.Delta_pL[:, 0]))
if gd.comm.size > 1:
gd.comm.broadcast(D_sp, self.rank_a[a])
M2 = M1 + ni
rho_MM[M1:M2, M1:M2] = unpack2(D_sp[s])
M1 = M2
phi.lfc.ae_valence_density_correction(rho_MM, n_sg[s], a_W, I_a)
phit.lfc.ae_valence_density_correction(-rho_MM, n_sg[s], a_W, I_a)
a_W = np.empty(len(nc.M_W), np.int32)
W = 0
for a in nc.atom_indices:
nw = len(nc.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
scale = 1.0 / self.nspins
for s, I_a in enumerate(I_sa):
nc.lfc.ae_core_density_correction(scale, n_sg[s], a_W, I_a)
nct.lfc.ae_core_density_correction(-scale, n_sg[s], a_W, I_a)
gd.comm.sum(I_a)
N_c = gd.N_c
g_ac = np.around(N_c * spos_ac).astype(int) % N_c - gd.beg_c
for I, g_c in zip(I_a, g_ac):
if (g_c >= 0).all() and (g_c < gd.n_c).all():
n_sg[s][tuple(g_c)] -= I / gd.dv
return n_sg, gd
if extra_parameters.get('usenewlfc', True):
get_all_electron_density = new_get_all_electron_density
def estimate_memory(self, mem):
nspins = self.nspins
nbytes = self.gd.bytecount()
nfinebytes = self.finegd.bytecount()
arrays = mem.subnode('Arrays')
for name, size in [('nt_sG', nbytes * nspins),
('nt_sg', nfinebytes * nspins),
('nt_g', nfinebytes),
('rhot_g', nfinebytes),
('nct_G', nbytes)]:
arrays.subnode(name, size)
lfs = mem.subnode('Localized functions')
for name, obj in [('nct', self.nct),
('ghat', self.ghat)]:
obj.estimate_memory(lfs.subnode(name))
self.mixer.estimate_memory(mem.subnode('Mixer'), self.gd)
# TODO
# The implementation of interpolator memory use is not very
# accurate; 20 MiB vs 13 MiB estimated in one example, probably
# worse for parallel calculations.
self.interpolator.estimate_memory(mem.subnode('Interpolator'))
def get_spin_contamination(self, atoms, majority_spin=0):
"""Calculate the spin contamination.
Spin contamination is defined as the integral over the
spin density difference, where it is negative (i.e. the
minority spin density is larger than the majority spin density.
"""
if majority_spin == 0:
smaj = 0
smin = 1
else:
smaj = 1
smin = 0
nt_sg, gd = self.get_all_electron_density(atoms)
dt_sg = nt_sg[smin] - nt_sg[smaj]
dt_sg = np.where(dt_sg > 0, dt_sg, 0.0)
return gd.integrate(dt_sg)
class Density:
"""Density object.
Attributes:
=============== =====================================================
``gd`` Grid descriptor for coarse grids.
``finegd`` Grid descriptor for fine grids.
``interpolate`` Function for interpolating the electron density.
``mixer`` ``DensityMixer`` object.
=============== =====================================================
Soft and smooth pseudo functions on uniform 3D grids:
========== =========================================
``nt_sG`` Electron density on the coarse grid.
``nt_sg`` Electron density on the fine grid.
``nt_g`` Electron density on the fine grid.
``rhot_g`` Charge density on the fine grid.
``nct_G`` Core electron-density on the coarse grid.
========== =========================================
"""
def __init__(self, gd, finegd, nspins, charge, collinear=True):
"""Create the Density object."""
self.gd = gd
self.finegd = finegd
self.nspins = nspins
self.charge = float(charge)
self.collinear = collinear
self.ncomp = 1 if collinear else 2
self.ns = self.nspins * self.ncomp**2
self.charge_eps = 1e-7
self.D_asp = None
self.Q_aL = None
self.nct_G = None
self.nt_sG = None
self.rhot_g = None
self.nt_sg = None
self.nt_g = None
self.rank_a = None
self.atom_partition = None
self.mixer = BaseMixer()
self.timer = nulltimer
def initialize(self, setups, timer, magmom_av, hund):
self.timer = timer
self.setups = setups
self.hund = hund
self.magmom_av = magmom_av
def reset(self):
# TODO: reset other parameters?
self.nt_sG = None
def set_positions(self, spos_ac, rank_a):
atom_partition = AtomPartition(self.gd.comm, rank_a)
self.nct.set_positions(spos_ac)
self.ghat.set_positions(spos_ac)
self.mixer.reset()
#self.nt_sG = None
self.nt_sg = None
self.nt_g = None
self.rhot_g = None
self.Q_aL = None
# If both old and new atomic ranks are present, start a blank dict if
# it previously didn't exist but it will needed for the new atoms.
assert rank_a is not None
if (self.rank_a is not None and
self.D_asp is None and (rank_a == self.gd.comm.rank).any()):
self.D_asp = {}
if (self.rank_a is not None and self.D_asp is not None
and not isinstance(self.gd.comm, SerialCommunicator)):
self.timer.start('Redistribute')
def get_empty(a):
ni = self.setups[a].ni
return np.empty((self.ns, ni * (ni + 1) // 2))
self.atom_partition.redistribute(atom_partition, self.D_asp,
get_empty)
self.timer.stop('Redistribute')
self.rank_a = rank_a
self.atom_partition = atom_partition
def calculate_pseudo_density(self, wfs):
"""Calculate nt_sG from scratch.
nt_sG will be equal to nct_G plus the contribution from
wfs.add_to_density().
"""
wfs.calculate_density_contribution(self.nt_sG)
self.nt_sG[:self.nspins] += self.nct_G
def update(self, wfs):
self.timer.start('Density')
self.timer.start('Pseudo density')
self.calculate_pseudo_density(wfs)
self.timer.stop('Pseudo density')
self.timer.start('Atomic density matrices')
wfs.calculate_atomic_density_matrices(self.D_asp)
self.timer.stop('Atomic density matrices')
self.timer.start('Multipole moments')
comp_charge = self.calculate_multipole_moments()
self.timer.stop('Multipole moments')
if isinstance(wfs, LCAOWaveFunctions):
self.timer.start('Normalize')
self.normalize(comp_charge)
self.timer.stop('Normalize')
self.timer.start('Mix')
self.mix(comp_charge)
self.timer.stop('Mix')
self.timer.stop('Density')
def normalize(self, comp_charge=None):
"""Normalize pseudo density."""
if comp_charge is None:
comp_charge = self.calculate_multipole_moments()
pseudo_charge = self.gd.integrate(self.nt_sG[:self.nspins]).sum()
if pseudo_charge + self.charge + comp_charge != 0:
if pseudo_charge != 0:
x = -(self.charge + comp_charge) / pseudo_charge
self.nt_sG *= x
else:
# Use homogeneous background:
volume = self.gd.get_size_of_global_array().prod() * self.gd.dv
self.nt_sG[:self.nspins] = -(self.charge +
comp_charge) / volume
def mix(self, comp_charge):
if not self.mixer.mix_rho:
self.mixer.mix(self)
comp_charge = None
self.interpolate_pseudo_density(comp_charge)
self.calculate_pseudo_charge()
if self.mixer.mix_rho:
self.mixer.mix(self)
def calculate_multipole_moments(self):
"""Calculate multipole moments of compensation charges.
Returns the total compensation charge in units of electron
charge, so the number will be negative because of the
dominating contribution from the nuclear charge."""
comp_charge = 0.0
self.Q_aL = {}
for a, D_sp in self.D_asp.items():
Q_L = self.Q_aL[a] = np.dot(D_sp[:self.nspins].sum(0),
self.setups[a].Delta_pL)
Q_L[0] += self.setups[a].Delta0
comp_charge += Q_L[0]
return self.gd.comm.sum(comp_charge) * sqrt(4 * pi)
def initialize_from_atomic_densities(self, basis_functions):
"""Initialize D_asp, nt_sG and Q_aL from atomic densities.
nt_sG is initialized from atomic orbitals, and will
be constructed with the specified magnetic moments and
obeying Hund's rules if ``hund`` is true."""
# XXX does this work with blacs? What should be distributed?
# Apparently this doesn't use blacs at all, so it's serial
# with respect to the blacs distribution. That means it works
# but is not particularly efficient (not that this is a time
# consuming step)
self.D_asp = {}
f_asi = {}
for a in basis_functions.atom_indices:
c = self.charge / len(self.setups) # distribute on all atoms
M_v = self.magmom_av[a]
M = (M_v**2).sum()**0.5
f_si = self.setups[a].calculate_initial_occupation_numbers(
M, self.hund, charge=c, nspins=self.nspins * self.ncomp)
if self.collinear:
if M_v[2] < 0:
f_si = f_si[::-1].copy()
else:
f_i = f_si.sum(axis=0)
fm_i = f_si[0] - f_si[1]
f_si = np.zeros((4, len(f_i)))
f_si[0] = f_i
if M > 0:
f_si[1:4] = np.outer(M_v / M, fm_i)
if a in basis_functions.my_atom_indices:
self.D_asp[a] = self.setups[a].initialize_density_matrix(f_si)
f_asi[a] = f_si
self.nt_sG = self.gd.zeros(self.ns)
basis_functions.add_to_density(self.nt_sG, f_asi)
self.nt_sG[:self.nspins] += self.nct_G
self.calculate_normalized_charges_and_mix()
def initialize_from_wavefunctions(self, wfs):
"""Initialize D_asp, nt_sG and Q_aL from wave functions."""
self.timer.start("Density initialize from wavefunctions")
self.nt_sG = self.gd.empty(self.ns)
self.calculate_pseudo_density(wfs)
self.D_asp = {}
my_atom_indices = np.argwhere(wfs.rank_a == self.gd.comm.rank).ravel()
for a in my_atom_indices:
ni = self.setups[a].ni
self.D_asp[a] = np.empty((self.nspins, ni * (ni + 1) // 2))
wfs.calculate_atomic_density_matrices(self.D_asp)
self.calculate_normalized_charges_and_mix()
self.timer.stop("Density initialize from wavefunctions")
def initialize_directly_from_arrays(self, nt_sG, D_asp):
"""Set D_asp and nt_sG directly."""
self.nt_sG = nt_sG
self.D_asp = D_asp
#self.calculate_normalized_charges_and_mix()
# No calculate multipole moments? Tests will fail because of
# improperly initialized mixer
def calculate_normalized_charges_and_mix(self):
comp_charge = self.calculate_multipole_moments()
self.normalize(comp_charge)
self.mix(comp_charge)
def set_mixer(self, mixer):
if mixer is not None:
if self.nspins == 1 and isinstance(mixer, MixerSum):
raise RuntimeError('Cannot use MixerSum with nspins==1')
self.mixer = mixer
else:
if self.gd.pbc_c.any():
beta = 0.05
history = 5
weight = 50.0
else:
beta = 0.25
history = 3
weight = 1.0
if self.nspins == 2:
self.mixer = MixerSum(beta, history, weight)
else:
self.mixer = Mixer(beta, history, weight)
self.mixer.initialize(self)
def estimate_magnetic_moments(self):
magmom_av = np.zeros_like(self.magmom_av)
if self.nspins == 2 or not self.collinear:
for a, D_sp in self.D_asp.items():
if self.collinear:
magmom_av[a, 2] = np.dot(D_sp[0] - D_sp[1],
self.setups[a].N0_p)
else:
magmom_av[a] = np.dot(D_sp[1:4], self.setups[a].N0_p)
self.gd.comm.sum(magmom_av)
return magmom_av
def get_correction(self, a, spin):
"""Integrated atomic density correction.
Get the integrated correction to the pseuso density relative to
the all-electron density.
"""
setup = self.setups[a]
return sqrt(4 * pi) * (
np.dot(self.D_asp[a][spin], setup.Delta_pL[:, 0])
+ setup.Delta0 / self.nspins)
def get_all_electron_density(self, atoms=None, gridrefinement=2, spos_ac=None):
"""Return real all-electron density array.
Usage: Either get_all_electron_density(atoms) or
get_all_electron_density(spos_ac=spos_ac) """
if spos_ac is None:
spos_ac = atoms.get_scaled_positions() % 1.0
# Refinement of coarse grid, for representation of the AE-density
if gridrefinement == 1:
gd = self.gd
n_sg = self.nt_sG.copy()
elif gridrefinement == 2:
gd = self.finegd
if self.nt_sg is None:
self.interpolate_pseudo_density()
n_sg = self.nt_sg.copy()
elif gridrefinement == 4:
# Extra fine grid
gd = self.finegd.refine()
# Interpolation function for the density:
interpolator = Transformer(self.finegd, gd, 3)
# Transfer the pseudo-density to the fine grid:
n_sg = gd.empty(self.nspins)
if self.nt_sg is None:
self.interpolate_pseudo_density()
for s in range(self.nspins):
interpolator.apply(self.nt_sg[s], n_sg[s])
else:
raise NotImplementedError
# Add corrections to pseudo-density to get the AE-density
splines = {}
phi_aj = []
phit_aj = []
nc_a = []
nct_a = []
for a, id in enumerate(self.setups.id_a):
if id in splines:
phi_j, phit_j, nc, nct = splines[id]
else:
# Load splines:
phi_j, phit_j, nc, nct = self.setups[a].get_partial_waves()[:4]
splines[id] = (phi_j, phit_j, nc, nct)
phi_aj.append(phi_j)
phit_aj.append(phit_j)
nc_a.append([nc])
nct_a.append([nct])
# Create localized functions from splines
phi = BasisFunctions(gd, phi_aj)
phit = BasisFunctions(gd, phit_aj)
nc = LFC(gd, nc_a)
nct = LFC(gd, nct_a)
phi.set_positions(spos_ac)
phit.set_positions(spos_ac)
nc.set_positions(spos_ac)
nct.set_positions(spos_ac)
I_sa = np.zeros((self.nspins, len(spos_ac)))
a_W = np.empty(len(phi.M_W), np.intc)
W = 0
for a in phi.atom_indices:
nw = len(phi.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
x_W = phi.create_displacement_arrays()[0]
rho_MM = np.zeros((phi.Mmax, phi.Mmax))
for s, I_a in enumerate(I_sa):
M1 = 0
for a, setup in enumerate(self.setups):
ni = setup.ni
D_sp = self.D_asp.get(a)
if D_sp is None:
D_sp = np.empty((self.nspins, ni * (ni + 1) // 2))
else:
I_a[a] = ((setup.Nct - setup.Nc) / self.nspins -
sqrt(4 * pi) *
np.dot(D_sp[s], setup.Delta_pL[:, 0]))
if gd.comm.size > 1:
gd.comm.broadcast(D_sp, self.rank_a[a])
M2 = M1 + ni
rho_MM[M1:M2, M1:M2] = unpack2(D_sp[s])
M1 = M2
phi.lfc.ae_valence_density_correction(rho_MM, n_sg[s], a_W, I_a,
x_W)
phit.lfc.ae_valence_density_correction(-rho_MM, n_sg[s], a_W, I_a,
x_W)
a_W = np.empty(len(nc.M_W), np.intc)
W = 0
for a in nc.atom_indices:
nw = len(nc.sphere_a[a].M_w)
a_W[W:W + nw] = a
W += nw
scale = 1.0 / self.nspins
for s, I_a in enumerate(I_sa):
nc.lfc.ae_core_density_correction(scale, n_sg[s], a_W, I_a)
nct.lfc.ae_core_density_correction(-scale, n_sg[s], a_W, I_a)
gd.comm.sum(I_a)
N_c = gd.N_c
g_ac = np.around(N_c * spos_ac).astype(int) % N_c - gd.beg_c
for I, g_c in zip(I_a, g_ac):
if (g_c >= 0).all() and (g_c < gd.n_c).all():
n_sg[s][tuple(g_c)] -= I / gd.dv
return n_sg, gd
def estimate_memory(self, mem):
nspins = self.nspins
nbytes = self.gd.bytecount()
nfinebytes = self.finegd.bytecount()
arrays = mem.subnode('Arrays')
for name, size in [('nt_sG', nbytes * nspins),
('nt_sg', nfinebytes * nspins),
('nt_g', nfinebytes),
('rhot_g', nfinebytes),
('nct_G', nbytes)]:
arrays.subnode(name, size)
lfs = mem.subnode('Localized functions')
for name, obj in [('nct', self.nct),
('ghat', self.ghat)]:
obj.estimate_memory(lfs.subnode(name))
self.mixer.estimate_memory(mem.subnode('Mixer'), self.gd)
# TODO
# The implementation of interpolator memory use is not very
# accurate; 20 MiB vs 13 MiB estimated in one example, probably
# worse for parallel calculations.
def get_spin_contamination(self, atoms, majority_spin=0):
"""Calculate the spin contamination.
Spin contamination is defined as the integral over the
spin density difference, where it is negative (i.e. the
minority spin density is larger than the majority spin density.
"""
if majority_spin == 0:
smaj = 0
smin = 1
else:
smaj = 1
smin = 0
nt_sg, gd = self.get_all_electron_density(atoms)
dt_sg = nt_sg[smin] - nt_sg[smaj]
dt_sg = np.where(dt_sg > 0, dt_sg, 0.0)
return gd.integrate(dt_sg)
def read(self, reader, parallel, kptband_comm):
if reader['version'] > 0.3:
density_error = reader['DensityError']
if density_error is not None:
self.mixer.set_charge_sloshing(density_error)
if not reader.has_array('PseudoElectronDensity'):
return
hdf5 = hasattr(reader, 'hdf5')
nt_sG = self.gd.empty(self.nspins)
if hdf5:
# Read pseudoelectron density on the coarse grid
# and broadcast on kpt_comm and band_comm:
indices = [slice(0, self.nspins)] + self.gd.get_slice()
do_read = (kptband_comm.rank == 0)
reader.get('PseudoElectronDensity', out=nt_sG, parallel=parallel,
read=do_read, *indices) # XXX read=?
kptband_comm.broadcast(nt_sG, 0)
else:
for s in range(self.nspins):
self.gd.distribute(reader.get('PseudoElectronDensity', s),
nt_sG[s])
# Read atomic density matrices
D_asp = {}
natoms = len(self.setups)
self.rank_a = np.zeros(natoms, int)
all_D_sp = reader.get('AtomicDensityMatrices', broadcast=True)
if self.gd.comm.rank == 0:
D_asp = read_atomic_matrices(all_D_sp, self.setups)
self.initialize_directly_from_arrays(nt_sG, D_asp)
