def __init__(self, cell_cv, nk_c, txt=sys.stdout):
self.nk_c = nk_c
bigcell_cv = cell_cv * nk_c[:, np.newaxis]
L_c = (np.linalg.inv(bigcell_cv)**2).sum(0)**-0.5
rc = 0.5 * L_c.min()
print('Inner radius for %dx%dx%d Wigner-Seitz cell: %.3f Ang' %
(tuple(nk_c) + (rc * Bohr, )),
file=txt)
self.a = 5 / rc
print('Range-separation parameter: %.3f Ang^-1' % (self.a / Bohr),
file=txt)
# nr_c = [get_efficient_fft_size(2 * int(L * self.a * 1.5))
nr_c = [get_efficient_fft_size(2 * int(L * self.a * 3.0)) for L in L_c]
print('FFT size for calculating truncated Coulomb: %dx%dx%d' %
tuple(nr_c),
file=txt)
self.gd = GridDescriptor(nr_c, bigcell_cv, comm=mpi.serial_comm)
v_R = self.gd.empty()
v_i = v_R.ravel()
pos_iv = self.gd.get_grid_point_coordinates().reshape((3, -1)).T
corner_jv = np.dot(np.indices((2, 2, 2)).reshape((3, 8)).T, bigcell_cv)
for i, pos_v in enumerate(pos_iv):
r = ((pos_v - corner_jv)**2).sum(axis=1).min()**0.5
if r == 0:
v_i[i] = 2 * self.a / pi**0.5
else:
v_i[i] = erf(self.a * r) / r
self.K_Q = np.fft.fftn(v_R) * self.gd.dv
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()
class WignerSeitzTruncatedCoulomb:
def __init__(self, cell_cv, nk_c, txt=sys.stdout):
self.nk_c = nk_c
bigcell_cv = cell_cv * nk_c[:, np.newaxis]
L_c = (np.linalg.inv(bigcell_cv)**2).sum(0)**-0.5
rc = 0.5 * L_c.min()
prnt('Inner radius for %dx%dx%d Wigner-Seitz cell: %.3f Ang' %
(tuple(nk_c) + (rc * Bohr, )),
file=txt)
self.a = 5 / rc
prnt('Range-separation parameter: %.3f Ang^-1' % (self.a / Bohr),
file=txt)
# nr_c = [get_efficient_fft_size(2 * int(L * self.a * 1.5))
nr_c = [get_efficient_fft_size(2 * int(L * self.a * 3.0)) for L in L_c]
prnt('FFT size for calculating truncated Coulomb: %dx%dx%d' %
tuple(nr_c),
file=txt)
self.gd = GridDescriptor(nr_c, bigcell_cv, comm=mpi.serial_comm)
v_R = self.gd.empty()
v_i = v_R.ravel()
pos_iv = self.gd.get_grid_point_coordinates().reshape((3, -1)).T
corner_jv = np.dot(np.indices((2, 2, 2)).reshape((3, 8)).T, bigcell_cv)
for i, pos_v in enumerate(pos_iv):
r = ((pos_v - corner_jv)**2).sum(axis=1).min()**0.5
if r == 0:
v_i[i] = 2 * self.a / pi**0.5
else:
v_i[i] = erf(self.a * r) / r
self.K_Q = np.fft.fftn(v_R) * self.gd.dv
def get_potential(self, pd):
q_c = pd.kd.bzk_kc[0]
shift_c = (q_c * self.nk_c).round().astype(int)
max_c = self.gd.N_c // 2
K_G = pd.zeros()
N_c = pd.gd.N_c
for G, Q in enumerate(pd.Q_qG[0]):
Q_c = (np.unravel_index(Q, N_c) + N_c // 2) % N_c - N_c // 2
Q_c = Q_c * self.nk_c + shift_c
if (abs(Q_c) < max_c).all():
K_G[G] = self.K_Q[tuple(Q_c)]
G2_G = pd.G2_qG[0]
a = self.a
if pd.kd.gamma:
K_G[0] += pi / a**2
else:
K_G[0] += 4 * pi * (1 - np.exp(-G2_G[0] / (4 * a**2))) / G2_G[0]
K_G[1:] += 4 * pi * (1 - np.exp(-G2_G[1:] / (4 * a**2))) / G2_G[1:]
assert pd.dtype == complex
return K_G
class WignerSeitzTruncatedCoulomb:
def __init__(self, cell_cv, nk_c, txt=sys.stdout):
self.nk_c = nk_c
bigcell_cv = cell_cv * nk_c[:, np.newaxis]
L_c = (np.linalg.inv(bigcell_cv)**2).sum(0)**-0.5
rc = 0.5 * L_c.min()
prnt('Inner radius for %dx%dx%d Wigner-Seitz cell: %.3f Ang' %
(tuple(nk_c) + (rc * Bohr,)), file=txt)
self.a = 5 / rc
prnt('Range-separation parameter: %.3f Ang^-1' % (self.a / Bohr),
file=txt)
# nr_c = [get_efficient_fft_size(2 * int(L * self.a * 1.5))
nr_c = [get_efficient_fft_size(2 * int(L * self.a * 3.0))
for L in L_c]
prnt('FFT size for calculating truncated Coulomb: %dx%dx%d' %
tuple(nr_c), file=txt)
self.gd = GridDescriptor(nr_c, bigcell_cv, comm=mpi.serial_comm)
v_R = self.gd.empty()
v_i = v_R.ravel()
pos_iv = self.gd.get_grid_point_coordinates().reshape((3, -1)).T
corner_jv = np.dot(np.indices((2, 2, 2)).reshape((3, 8)).T, bigcell_cv)
for i, pos_v in enumerate(pos_iv):
r = ((pos_v - corner_jv)**2).sum(axis=1).min()**0.5
if r == 0:
v_i[i] = 2 * self.a / pi**0.5
else:
v_i[i] = erf(self.a * r) / r
self.K_Q = np.fft.fftn(v_R) * self.gd.dv
def get_potential(self, pd):
q_c = pd.kd.bzk_kc[0]
shift_c = (q_c * self.nk_c).round().astype(int)
max_c = self.gd.N_c // 2
K_G = pd.zeros()
N_c = pd.gd.N_c
for G, Q in enumerate(pd.Q_qG[0]):
Q_c = (np.unravel_index(Q, N_c) + N_c // 2) % N_c - N_c // 2
Q_c = Q_c * self.nk_c + shift_c
if (abs(Q_c) < max_c).all():
K_G[G] = self.K_Q[tuple(Q_c)]
G2_G = pd.G2_qG[0]
a = self.a
if pd.kd.gamma:
K_G[0] += pi / a**2
else:
K_G[0] += 4 * pi * (1 - np.exp(-G2_G[0] / (4 * a**2))) / G2_G[0]
K_G[1:] += 4 * pi * (1 - np.exp(-G2_G[1:] / (4 * a**2))) / G2_G[1:]
assert pd.dtype == complex
return K_G
def setUp(self):
for virtvar in ['boundaries']:
assert getattr(self,
virtvar) is not None, 'Virtual "%s"!' % virtvar
# Basic unit cell information:
res, N_c = shapeopt(100, self.G**3, 3, 0.2)
#N_c = 4*np.round(np.array(N_c)/4) # makes domain decomposition easier
cell_cv = self.h * np.diag(N_c)
pbc_c = {'zero' : (False,False,False), \
'periodic': (True,True,True), \
'mixed' : (True, False, True)}[self.boundaries]
# Create randomized gas-like atomic configuration on interim grid
tmpgd = GridDescriptor(N_c, cell_cv, pbc_c)
self.atoms = create_random_atoms(tmpgd)
# Create setups
Z_a = self.atoms.get_atomic_numbers()
assert 1 == self.nspins
self.setups = Setups(Z_a, p.setups, p.basis, p.lmax, xc)
self.natoms = len(self.setups)
# Decide how many kpoints to sample from the 1st Brillouin Zone
kpts_c = np.ceil(
(10 / Bohr) / np.sum(cell_cv**2, axis=1)**0.5).astype(int)
kpts_c = tuple(kpts_c * pbc_c + 1 - pbc_c)
self.bzk_kc = kpts2ndarray(kpts_c)
# Set up k-point descriptor
self.kd = KPointDescriptor(self.bzk_kc, self.nspins)
self.kd.set_symmetry(self.atoms, self.setups, p.usesymm)
# Set the dtype
if self.kd.gamma:
self.dtype = float
else:
self.dtype = complex
# Create communicators
parsize, parsize_bands = self.get_parsizes()
assert self.nbands % np.prod(parsize_bands) == 0
domain_comm, kpt_comm, band_comm = distribute_cpus(
parsize, parsize_bands, self.nspins, self.kd.nibzkpts)
self.kd.set_communicator(kpt_comm)
# Set up band descriptor:
self.bd = BandDescriptor(self.nbands, band_comm)
# Set up grid descriptor:
self.gd = GridDescriptor(N_c, cell_cv, pbc_c, domain_comm, parsize)
# Set up kpoint/spin descriptor (to be removed):
self.kd_old = KPointDescriptorOld(self.nspins, self.kd.nibzkpts,
kpt_comm, self.kd.gamma, self.dtype)
def setUp(self):
for virtvar in ['boundaries', 'celltype']:
assert getattr(self,virtvar) is not None, 'Virtual "%s"!' % virtvar
# Basic unit cell information:
pbc_c = {'zero' : (False,False,False), \
'periodic': (True,True,True), \
'mixed' : (True, False, True)}[self.boundaries]
a, b = self.a, 2**0.5*self.a
cell_cv = {'general' : np.array([[0,a,a],[a/2,0,a/2],[a/2,a/2,0]]),
'rotated' : np.array([[0,0,b],[b/2,0,0],[0,b/2,0]]),
'inverted' : np.array([[0,0,b],[0,b/2,0],[b/2,0,0]]),
'orthogonal': np.diag([b, b/2, b/2])}[self.celltype]
cell_cv = np.array([(4-3*pbc)*c_v for pbc,c_v in zip(pbc_c, cell_cv)])
# Decide how many kpoints to sample from the 1st Brillouin Zone
kpts_c = np.ceil((10/Bohr)/np.sum(cell_cv**2,axis=1)**0.5).astype(int)
kpts_c = tuple(kpts_c*pbc_c + 1-pbc_c)
bzk_kc = kpts2ndarray(kpts_c)
self.gamma = len(bzk_kc) == 1 and not bzk_kc[0].any()
#p = InputParameters()
#Z_a = self.atoms.get_atomic_numbers()
#xcfunc = XC(p.xc)
#setups = Setups(Z_a, p.setups, p.basis, p.lmax, xcfunc)
#symmetry, weight_k, self.ibzk_kc = reduce_kpoints(self.atoms, bzk_kc,
# setups, p.usesymm)
self.ibzk_kc = bzk_kc.copy() # don't use symmetry reduction of kpoints
self.nibzkpts = len(self.ibzk_kc)
self.ibzk_kv = kpoint_convert(cell_cv, skpts_kc=self.ibzk_kc)
# Parse parallelization parameters and create suitable communicators.
#parsize, parsize_bands = create_parsize_minbands(self.nbands, world.size)
parsize, parsize_bands = world.size//gcd(world.size, self.nibzkpts), 1
assert self.nbands % np.prod(parsize_bands) == 0
domain_comm, kpt_comm, band_comm = distribute_cpus(parsize,
parsize_bands, self.nspins, self.nibzkpts)
# Set up band descriptor:
self.bd = BandDescriptor(self.nbands, band_comm)
# Set up grid descriptor:
N_c = np.round(np.sum(cell_cv**2, axis=1)**0.5 / self.h)
N_c += 4-N_c % 4 # makes domain decomposition easier
self.gd = GridDescriptor(N_c, cell_cv, pbc_c, domain_comm, parsize)
self.assertEqual(self.gamma, np.all(~self.gd.pbc_c))
# What to do about kpoints?
self.kpt_comm = kpt_comm
if debug and world.rank == 0:
comm_sizes = tuple([comm.size for comm in [world, self.bd.comm, \
self.gd.comm, self.kpt_comm]])
print '%d world, %d band, %d domain, %d kpt' % comm_sizes
def __init__(self, gd, spline_j, spos_c, index=None):
rcut = max([spline.get_cutoff() for spline in spline_j])
corner_c = np.ceil(spos_c * gd.N_c - rcut / gd.h_c).astype(int)
size_c = np.ceil(spos_c * gd.N_c + rcut / gd.h_c).astype(int) - corner_c
smallgd = GridDescriptor(N_c=size_c + 1, cell_cv=gd.h_c * (size_c + 1), pbc_c=False, comm=mpi.serial_comm)
lfc = LFC(smallgd, [spline_j])
lfc.set_positions((spos_c[np.newaxis, :] * gd.N_c - corner_c + 1) / smallgd.N_c)
ni = lfc.Mmax
f_iG = smallgd.zeros(ni)
lfc.add(f_iG, {0: np.eye(ni)})
LocalizedFunctions.__init__(self, gd, f_iG, corner_c, index=index)
def extend_grid(gd, N_cd):
N_cd = np.array(N_cd)
N_c = gd.N_c + N_cd.sum(axis=1)
cell_cv = gd.h_cv * N_c
move_c = gd.get_grid_spacings() * N_cd[:,0]
egd = GridDescriptor(N_c, cell_cv, gd.pbc_c, gd.comm)
egd.extend_N_cd = N_cd
return egd, cell_cv*Bohr, move_c*Bohr
def extend_grid(gd, N_cd):
N_cd = np.array(N_cd)
N_c = gd.N_c + N_cd.sum(axis=1)
cell_cv = gd.h_cv * N_c
move_c = gd.get_grid_spacings() * N_cd[:, 0]
egd = GridDescriptor(N_c, cell_cv, gd.pbc_c, gd.comm)
egd.extend_N_cd = N_cd
return egd, cell_cv * Bohr, move_c * Bohr
def main():
from gpaw.grid_descriptor import GridDescriptor
from gpaw.mpi import world
serial = world.new_communicator([world.rank])
# Generator which must run on all ranks
gen = np.random.RandomState(0)
# This one is just used by master
gen_serial = np.random.RandomState(17)
maxsize = 5
for i in range(1):
N1_c = gen.randint(1, maxsize, 3)
N2_c = gen.randint(1, maxsize, 3)
gd1 = GridDescriptor(N1_c, N1_c)
gd2 = GridDescriptor(N2_c, N2_c)
serial_gd1 = gd1.new_descriptor(comm=serial)
# serial_gd2 = gd2.new_descriptor(comm=serial)
a1_serial = serial_gd1.empty()
a1_serial.flat[:] = gen_serial.rand(a1_serial.size)
if world.rank == 0:
print('r0: a1 serial', a1_serial.ravel())
a1 = gd1.empty()
a1[:] = -1
grid2grid(world, serial_gd1, gd1, a1_serial, a1)
print(world.rank, 'a1 distributed', a1.ravel())
world.barrier()
a2 = gd2.zeros()
a2[:] = -2
grid2grid(world, gd1, gd2, a1, a2)
print(world.rank, 'a2 distributed', a2.ravel())
world.barrier()
#grid2grid(world, gd2, gd2_serial
gd1 = GridDescriptor(N1_c, N1_c * 0.2)
#serialgd = gd2.new_descriptor(
a1 = gd1.empty()
a1.flat[:] = gen.rand(a1.size)
#print a1
grid2grid(world, gd1, gd2, a1, a2)
def interpolate_2d(mat):
from gpaw.grid_descriptor import GridDescriptor
from gpaw.transformers import Transformer
nn = 10
N_c = np.zeros([3], dtype=int)
N_c[1:] = mat.shape[:2]
N_c[0] = nn
bmat = np.resize(mat, N_c)
gd = GridDescriptor(N_c, N_c)
finegd = GridDescriptor(N_c * 2, N_c)
interpolator = Transformer(gd, finegd, 3)
fine_bmat = finegd.zeros()
interpolator.apply(bmat, fine_bmat)
return fine_bmat[0]
def initialize_more_things(self):
if self.alphas:
from gpaw.mpi import SerialCommunicator
scale_c1 = (self.shape / (1.0 * self.gd.N_c))[:, np.newaxis]
gdfft = GridDescriptor(self.shape,
self.gd.cell_cv * scale_c1,
True,
comm=SerialCommunicator())
k_k = construct_reciprocal(gdfft)[0][:, :, :self.shape[2] // 2 +
1]**0.5
k_k[0, 0, 0] = 0.0
self.dj_k = k_k / (2 * pi / self.rcut)
self.j_k = self.dj_k.astype(int)
self.dj_k -= self.j_k
self.dj_k *= 2 * pi / self.rcut
if self.verbose:
print('VDW: density array size:',
self.gd.get_size_of_global_array())
print('VDW: zero-padded array size:', self.shape)
print(('VDW: maximum kinetic energy: %.3f Hartree' %
(0.5 * k_k.max()**2)))
assert self.j_k.max() < self.Nr // 2, ('Use larger Nr than %i.' %
self.Nr)
else:
self.dj_k = None
self.j_k = None
def read_3D(self, file, filetype=None):
"""Read the density from a 3D file"""
if filetype is None:
# estimate file type from name ending
filetype = file.split('.')[-1]
filetype.lower()
if filetype == 'plt':
data, cell = read_plt(file)
pbc_c = [True, True, True]
N_c = np.array(data.shape)
for c in range(3):
if N_c[c] % 2 == 1:
pbc_c[c] = False
N_c[c] += 1
self.gd = GridDescriptor(N_c, cell.diagonal() / Bohr, pbc_c)
self.offset_c = [int(not a) for a in self.gd.pbc_c]
else:
raise NotImplementedError('unknown file type "' + filetype + '"')
self.file = file
self.ldos = np.array(data * Bohr**3, np.float)
def __init__(self, cell_cv, nk_c, txt=sys.stdout):
self.nk_c = nk_c
bigcell_cv = cell_cv * nk_c[:, np.newaxis]
L_c = (np.linalg.inv(bigcell_cv)**2).sum(0)**-0.5
rc = 0.5 * L_c.min()
prnt('Inner radius for %dx%dx%d Wigner-Seitz cell: %.3f Ang' %
(tuple(nk_c) + (rc * Bohr,)), file=txt)
self.a = 5 / rc
prnt('Range-separation parameter: %.3f Ang^-1' % (self.a / Bohr),
file=txt)
# nr_c = [get_efficient_fft_size(2 * int(L * self.a * 1.5))
nr_c = [get_efficient_fft_size(2 * int(L * self.a * 3.0))
for L in L_c]
prnt('FFT size for calculating truncated Coulomb: %dx%dx%d' %
tuple(nr_c), file=txt)
self.gd = GridDescriptor(nr_c, bigcell_cv, comm=mpi.serial_comm)
v_R = self.gd.empty()
v_i = v_R.ravel()
pos_iv = self.gd.get_grid_point_coordinates().reshape((3, -1)).T
corner_jv = np.dot(np.indices((2, 2, 2)).reshape((3, 8)).T, bigcell_cv)
for i, pos_v in enumerate(pos_iv):
r = ((pos_v - corner_jv)**2).sum(axis=1).min()**0.5
if r == 0:
v_i[i] = 2 * self.a / pi**0.5
else:
v_i[i] = erf(self.a * r) / r
self.K_Q = np.fft.fftn(v_R) * self.gd.dv
def setUp(self):
for virtvar in ['boundaries']:
assert getattr(self,virtvar) is not None, 'Virtual "%s"!' % virtvar
parsize_domain, parsize_bands = create_parsize_minbands(self.nbands, world.size)
assert self.nbands % np.prod(parsize_bands) == 0
comms = distribute_cpus(parsize_domain,
parsize_bands, self.nspins, self.nibzkpts)
domain_comm, kpt_comm, band_comm, block_comm = \
[comms[name] for name in 'dkbK']
self.block_comm = block_comm
# Set up band descriptor:
self.bd = BandDescriptor(self.nbands, band_comm)
# Set up grid descriptor:
res, ngpts = shapeopt(300, self.G**3, 3, 0.2)
cell_c = self.h * np.array(ngpts)
pbc_c = {'zero' : False, \
'periodic': True, \
'mixed' : (True, False, True)}[self.boundaries]
self.gd = GridDescriptor(ngpts, cell_c, pbc_c, domain_comm, parsize_domain)
# What to do about kpoints?
self.kpt_comm = kpt_comm
def test_coulomb(N=2**6, a=20):
Nc = (N, N, N) # Number of grid point
gd = GridDescriptor(Nc, (a, a, a), True) # grid-descriptor object
xyz, r2 = coordinates(
gd) # matrix with the square of the radial coordinate
r = np.sqrt(r2) # matrix with the values of the radial coordinate
nH = np.exp(-2 * r) / pi # density of the hydrogen atom
C = Coulomb(gd) # coulomb calculator
if world.size > 1:
C.load('real')
t0 = time.time()
print('Processor %s of %s: %s Ha in %s sec' %
(gd.comm.rank + 1, gd.comm.size,
-0.5 * C.coulomb(nH, method='real'), time.time() - t0))
return
else:
C.load('recip_ewald')
C.load('recip_gauss')
C.load('real')
test = {}
t0 = time.time()
test['dual density'] = (-0.5 * C.coulomb(nH, nH.copy()),
time.time() - t0)
for method in ('real', 'recip_gauss', 'recip_ewald'):
t0 = time.time()
test[method] = (-0.5 * C.coulomb(nH, method=method),
time.time() - t0)
return test
def setUp(self):
for virtvar in ['dtype', 'parstride_bands']:
assert getattr(self,
virtvar) is not None, 'Virtual "%s"!' % virtvar
parsize, parsize_bands = create_parsize_maxbands(
self.nbands, world.size)
assert self.nbands % np.prod(parsize_bands) == 0
domain_comm, kpt_comm, band_comm = distribute_cpus(
parsize, parsize_bands, self.nspins, self.nibzkpts)
# Set up band descriptor:
self.bd = BandDescriptor(self.nbands, band_comm, self.parstride_bands)
# Set up grid descriptor:
res, ngpts = shapeopt(100, self.G**3, 3, 0.2)
cell_c = self.h * np.array(ngpts)
pbc_c = (True, False, True)
self.gd = GridDescriptor(ngpts, cell_c, pbc_c, domain_comm, parsize)
# Create Kohn-Sham layouts for these band and grid descriptors:
self.ksl = self.create_kohn_sham_layouts()
# What to do about kpoints?
self.kpt_comm = kpt_comm
def __init__(self, gd, spline_j, spos_c, index=None):
rcut = max([spline.get_cutoff() for spline in spline_j])
corner_c = np.ceil(spos_c * gd.N_c - rcut / gd.h_c).astype(int)
size_c = np.ceil(spos_c * gd.N_c + rcut / gd.h_c).astype(int) - corner_c
smallgd = GridDescriptor(N_c=size_c + 1,
cell_cv=gd.h_c * (size_c + 1),
pbc_c=False,
comm=mpi.serial_comm)
lfc = LFC(smallgd, [spline_j])
lfc.set_positions((spos_c[np.newaxis, :] * gd.N_c - corner_c + 1) /
smallgd.N_c)
ni = lfc.Mmax
f_iG = smallgd.zeros(ni)
lfc.add(f_iG, {0: np.eye(ni)})
LocalizedFunctions.__init__(self, gd, f_iG, corner_c,
index=index)
def set_grid_descriptor(self, gd):
if (self.gd is not None and (self.gd.N_c == gd.N_c).all()
and (self.gd.pbc_c == gd.pbc_c).all()
and (self.gd.cell_cv == gd.cell_cv).all()):
return
VDWFunctional.set_grid_descriptor(self, gd)
if self.size is None:
self.shape = gd.N_c.copy()
for c, n in enumerate(self.shape):
if not gd.pbc_c[c]:
self.shape[c] = int(2**ceil(log(n) / log(2)))
else:
self.shape = np.array(self.size)
scale_c1 = (self.shape / (1.0 * gd.N_c))[:, np.newaxis]
gdfft = GridDescriptor(self.shape, gd.cell_cv * scale_c1, True)
k_k = construct_reciprocal(gdfft)[0][:, :, :self.shape[2] // 2 +
1]**0.5
k_k[0, 0, 0] = 0.0
self.dj_k = k_k / (2 * pi / self.rcut)
self.j_k = self.dj_k.astype(int)
self.dj_k -= self.j_k
self.dj_k *= 2 * pi / self.rcut
assert self.j_k.max() < self.Nr // 2, 'Use larger Nr.'
if self.verbose:
print 'VDW: density array size:', gd.get_size_of_global_array()
print 'VDW: zero-padded array size:', self.shape
print('VDW: maximum kinetic energy: %.3f Hartree' %
(0.5 * k_k.max()**2))
def get_combined_data(self, qmdata=None, cldata=None, spacing=None):
if qmdata is None:
qmdata = self.density.rhot_g
if cldata is None:
cldata = self.classical_material.charge_density
if spacing is None:
spacing = self.cl.gd.h_cv[0, 0]
spacing_au = spacing / Bohr # from Angstroms to a.u.
# Collect data from different processes
cln = self.cl.gd.collect(cldata)
qmn = self.qm.gd.collect(qmdata)
clgd = GridDescriptor(self.cl.gd.N_c, self.cl.cell, False, serial_comm,
None)
if world.rank == 0:
cln *= self.classical_material.sign
# refine classical part
while clgd.h_cv[0, 0] > spacing_au * 1.50: # 45:
cln = Transformer(clgd, clgd.refine()).apply(cln)
clgd = clgd.refine()
# refine quantum part
qmgd = GridDescriptor(self.qm.gd.N_c, self.qm.cell, False,
serial_comm, None)
while qmgd.h_cv[0, 0] < clgd.h_cv[0, 0] * 0.95:
qmn = Transformer(qmgd, qmgd.coarsen()).apply(qmn)
qmgd = qmgd.coarsen()
assert np.all(qmgd.h_cv == clgd.h_cv
), " Spacings %.8f (qm) and %.8f (cl) Angstroms" % (
qmgd.h_cv[0][0] * Bohr, clgd.h_cv[0][0] * Bohr)
# now find the corners
r_gv_cl = clgd.get_grid_point_coordinates().transpose((1, 2, 3, 0))
cind = self.qm.corner1 / np.diag(clgd.h_cv) - 1
n = qmn.shape
# print 'Corner points: ', self.qm.corner1*Bohr, ' - ', self.qm.corner2*Bohr
# print 'Calculated points: ', r_gv_cl[tuple(cind)]*Bohr, ' - ', r_gv_cl[tuple(cind+n+1)]*Bohr
cln[cind[0] + 1:cind[0] + n[0] + 1, cind[1] + 1:cind[1] + n[1] + 1,
cind[2] + 1:cind[2] + n[2] + 1] += qmn
world.barrier()
return cln, clgd
def test(xc, tol=5e-10):
N_c = np.array([10, 6, 4])
# This test is totally serial.
gd = GridDescriptor(N_c,
N_c * 0.2,
pbc_c=(1, 0, 1),
comm=world.new_communicator([world.rank]))
actual_n = N_c - (1 - gd.pbc_c)
gen = np.random.RandomState(17)
n_sg = gd.zeros(2)
n_sg[:] = gen.rand(*n_sg.shape)
#sigma_xg = gd.zeros(3)
#sigma_xg[:] = gen.random.rand(*sigma_xg.shape)
if hasattr(xc, 'libvdwxc'):
xc._nspins = 2
xc.initialize_backend(gd)
v_sg = gd.zeros(2)
E = xc.calculate(gd, n_sg, v_sg)
print('E', E)
dn = 1e-6
all_indices = itertools.product(range(2), range(1, actual_n[0], 2),
range(0, actual_n[1], 2),
range(0, actual_n[2], 2))
for testindex in all_indices:
n1_sg = n_sg.copy()
n2_sg = n_sg.copy()
v = v_sg[testindex] * gd.dv
n1_sg[testindex] -= dn
n2_sg[testindex] += dn
E1 = xc.calculate(gd, n1_sg, v_sg.copy())
E2 = xc.calculate(gd, n2_sg, v_sg.copy())
dedn = 0.5 * (E2 - E1) / dn
err = abs(dedn - v)
print('{}{} v={} fd={} err={}'.format(xc.name, list(testindex), v,
dedn, err))
assert err < tol, err
def make_dummy_kpt_reference(l, function, k_c,
rcut=6., a=10., n=60, dtype=complex):
r = np.linspace(0., rcut, 300)
mcount = 2*l + 1
fcount = 1
kcount = 1
gd = GridDescriptor((n, n, n), (a, a, a), (True, True, True))
kpt = KPoint([], gd, 1., 0, 0, 0, k_c, dtype)
spline = Spline(l, r[-1], function(r))
center = (.5, .5, .5)
lf = create_localized_functions([spline], gd, center, dtype=dtype)
lf.set_phase_factors([kpt.k_c])
psit_nG = gd.zeros(mcount, dtype=dtype)
coef_xi = np.identity(mcount * fcount, dtype=dtype)
lf.add(psit_nG, coef_xi, k=0)
kpt.psit_nG = psit_nG
print 'Number of boxes', len(lf.box_b)
print 'Phase kb factors shape', lf.phase_kb.shape
return gd, kpt, center
def setUp(self):
for virtvar in ['boundaries', 'celltype']:
assert getattr(self,virtvar) is not None, 'Virtual "%s"!' % virtvar
# Basic unit cell information:
pbc_c = {'zero' : (False,False,False), \
'periodic': (True,True,True), \
'mixed' : (True, False, True)}[self.boundaries]
a, b = self.a, 2**0.5*self.a
cell_cv = {'general' : np.array([[0,a,a],[a/2,0,a/2],[a/2,a/2,0]]),
'rotated' : np.array([[0,0,b],[b/2,0,0],[0,b/2,0]]),
'inverted' : np.array([[0,0,b],[0,b/2,0],[b/2,0,0]]),
'orthogonal': np.diag([b, b/2, b/2])}[self.celltype]
cell_cv = np.array([(4-3*pbc)*c_v for pbc,c_v in zip(pbc_c, cell_cv)])
# Decide how many kpoints to sample from the 1st Brillouin Zone
kpts_c = np.ceil((10/Bohr)/np.sum(cell_cv**2,axis=1)**0.5).astype(int)
kpts_c = tuple(kpts_c*pbc_c + 1-pbc_c)
bzk_kc = kpts2ndarray(kpts_c)
self.gamma = len(bzk_kc) == 1 and not bzk_kc[0].any()
#p = InputParameters()
#Z_a = self.atoms.get_atomic_numbers()
#xcfunc = XC(p.xc)
#setups = Setups(Z_a, p.setups, p.basis, p.lmax, xcfunc)
#symmetry, weight_k, self.ibzk_kc = reduce_kpoints(self.atoms, bzk_kc,
# setups, p.usesymm)
self.ibzk_kc = bzk_kc.copy() # don't use symmetry reduction of kpoints
self.nibzkpts = len(self.ibzk_kc)
self.ibzk_kv = kpoint_convert(cell_cv, skpts_kc=self.ibzk_kc)
# Parse parallelization parameters and create suitable communicators.
#parsize_domain, parsize_bands = create_parsize_minbands(self.nbands, world.size)
parsize_domain, parsize_bands = world.size//gcd(world.size, self.nibzkpts), 1
assert self.nbands % np.prod(parsize_bands) == 0
domain_comm, kpt_comm, band_comm = distribute_cpus(parsize_domain,
parsize_bands, self.nspins, self.nibzkpts)
# Set up band descriptor:
self.bd = BandDescriptor(self.nbands, band_comm)
# Set up grid descriptor:
N_c = np.round(np.sum(cell_cv**2, axis=1)**0.5 / self.h)
N_c += 4-N_c % 4 # makes domain decomposition easier
self.gd = GridDescriptor(N_c, cell_cv, pbc_c, domain_comm, parsize_domain)
self.assertEqual(self.gamma, np.all(~self.gd.pbc_c))
# What to do about kpoints?
self.kpt_comm = kpt_comm
if debug and world.rank == 0:
comm_sizes = tuple([comm.size for comm in [world, self.bd.comm, \
self.gd.comm, self.kpt_comm]])
print '%d world, %d band, %d domain, %d kpt' % comm_sizes
def get_combined_data(self, qmdata=None, cldata=None, spacing=None):
if qmdata is None:
qmdata = self.density.rhot_g
if cldata is None:
cldata = self.classical_material.charge_density
if spacing is None:
spacing = self.cl.gd.h_cv[0, 0]
spacing_au = spacing / Bohr # from Angstroms to a.u.
# Collect data from different processes
cln = self.cl.gd.collect(cldata)
qmn = self.qm.gd.collect(qmdata)
clgd = GridDescriptor(self.cl.gd.N_c,
self.cl.cell,
False,
serial_comm,
None)
if world.rank == 0:
cln *= self.classical_material.sign
# refine classical part
while clgd.h_cv[0, 0] > spacing_au * 1.50: # 45:
cln = Transformer(clgd, clgd.refine()).apply(cln)
clgd = clgd.refine()
# refine quantum part
qmgd = GridDescriptor(self.qm.gd.N_c,
self.qm.cell,
False,
serial_comm,
None)
while qmgd.h_cv[0, 0] < clgd.h_cv[0, 0] * 0.95:
qmn = Transformer(qmgd, qmgd.coarsen()).apply(qmn)
qmgd = qmgd.coarsen()
assert np.all(qmgd.h_cv == clgd.h_cv), " Spacings %.8f (qm) and %.8f (cl) Angstroms" % (qmgd.h_cv[0][0] * Bohr, clgd.h_cv[0][0] * Bohr)
# now find the corners
r_gv_cl = clgd.get_grid_point_coordinates().transpose((1, 2, 3, 0))
cind = self.qm.corner1 / np.diag(clgd.h_cv) - 1
n = qmn.shape
# print 'Corner points: ', self.qm.corner1*Bohr, ' - ', self.qm.corner2*Bohr
# print 'Calculated points: ', r_gv_cl[tuple(cind)]*Bohr, ' - ', r_gv_cl[tuple(cind+n+1)]*Bohr
cln[cind[0] + 1:cind[0] + n[0] + 1,
cind[1] + 1:cind[1] + n[1] + 1,
cind[2] + 1:cind[2] + n[2] + 1] += qmn
world.barrier()
return cln, clgd
def test():
from gpaw.grid_descriptor import GridDescriptor
ngpts = 40
h = 1 / ngpts
N_c = (ngpts, ngpts, ngpts)
a = h * ngpts
gd = GridDescriptor(N_c, (a, a, a))
from gpaw.spline import Spline
a = np.array([1, 0.9, 0.8, 0.0])
s = Spline(0, 0.2, a)
x = LocalizedFunctionsCollection(gd, [[s], [s]])
x.set_positions([(0.5, 0.45, 0.5), (0.5, 0.55, 0.5)])
n_G = gd.zeros()
x.add(n_G)
import pylab as plt
plt.contourf(n_G[20, :, :])
plt.axis('equal')
plt.show()
def test():
from gpaw.grid_descriptor import GridDescriptor
ngpts = 40
h = 1.0 / ngpts
N_c = (ngpts, ngpts, ngpts)
a = h * ngpts
gd = GridDescriptor(N_c, (a, a, a))
from gpaw.spline import Spline
a = np.array([1, 0.9, 0.8, 0.0])
s = Spline(0, 0.2, a)
x = LocalizedFunctionsCollection(gd, [[s], [s]])
x.set_positions([(0.5, 0.45, 0.5), (0.5, 0.55, 0.5)])
n_G = gd.zeros()
x.add(n_G)
import pylab as plt
plt.contourf(n_G[20, :, :])
plt.axis('equal')
plt.show()
def solve_mesh(self, X, Y, Z, pot, dens0, permitivity):
gd = GridDescriptor([X.shape[0] + 1, X.shape[1] + 1, X.shape[2] + 1],
cell_cv=[
X[0, -1, 0] - X[0, 0, 0],
Y[-1, 0, 0] - Y[0, 0, 0],
Z[0, 0, -1] - Z[0, 0, 0]
],
pbc_c=False)
self.set_grid_descriptor(gd)
self.solve(pot, dens0, permitivity)
def interpolate_weight(calc, weight, h=0.05, n=2):
'''interpolates cdft weight function,
gd is the fine grid '''
gd = calc.density.finegd
weight = gd.collect(weight, broadcast=True)
weight = gd.zero_pad(weight)
w = np.zeros_like(weight)
gd1 = GridDescriptor(gd.N_c, gd.cell_cv, comm=serial_comm)
gd1.distribute(weight, w)
N_c = h2gpts(h / Bohr, gd.cell_cv)
N_c = np.array([get_efficient_fft_size(N, n) for N in N_c])
gd2 = GridDescriptor(N_c, gd.cell_cv, comm=serial_comm)
interpolator = Interpolator(gd1, gd2)
W = interpolator.interpolate(w)
return W
def go(comm, ngpts, repeat, narrays, out, prec):
N_c = np.array((ngpts, ngpts, ngpts))
a = 10.0
gd = GridDescriptor(N_c, (a, a, a), comm=comm))
gdcoarse = gd.coarsen()
gdfine = gd.refine()
kin1 = Laplace(gd, -0.5, 1).apply
laplace = Laplace(gd, -0.5, 2)
kin2 = laplace.apply
restrict = Transformer(gd, gdcoarse, 1).apply
interpolate = Transformer(gd, gdfine, 1).apply
precondition = Preconditioner(gd, laplace, np_float)
a1 = gd.empty(narrays)
a1[:] = 1.0
a2 = gd.empty(narrays)
c = gdcoarse.empty(narrays)
f = gdfine.empty(narrays)
T = [0, 0, 0, 0, 0]
for i in range(repeat):
comm.barrier()
kin1(a1, a2)
comm.barrier()
t0a = time()
kin1(a1, a2)
t0b = time()
comm.barrier()
t1a = time()
kin2(a1, a2)
t1b = time()
comm.barrier()
t2a = time()
for A, C in zip(a1,c):
restrict(A, C)
t2b = time()
comm.barrier()
t3a = time()
for A, F in zip(a1,f):
interpolate(A, F)
t3b = time()
comm.barrier()
if prec:
t4a = time()
for A in a1:
precondition(A, None, None, None)
t4b = time()
comm.barrier()
T[0] += t0b - t0a
T[1] += t1b - t1a
T[2] += t2b - t2a
T[3] += t3b - t3a
if prec:
T[4] += t4b - t4a
if mpi.rank == 0:
out.write(' %2d %2d %2d' % tuple(gd.parsize_c))
out.write(' %12.6f %12.6f %12.6f %12.6f %12.6f\n' %
tuple([t / repeat / narrays for t in T]))
out.flush()
def __init__(self, calc, h=0.05, n=2):
"""Create transformation object.
calc: GPAW calculator object
The calcalator that has the wave functions.
h: float
Desired grid-spacing in Angstrom.
n: int
Force number of points to be a mulitiple of n.
"""
self.calc = calc
gd = calc.wfs.gd
gd1 = GridDescriptor(gd.N_c, gd.cell_cv, comm=serial_comm)
# Descriptor for the final grid:
N_c = h2gpts(h / Bohr, gd.cell_cv)
N_c = np.array([get_efficient_fft_size(N, n) for N in N_c])
gd2 = self.gd = GridDescriptor(N_c, gd.cell_cv, comm=serial_comm)
self.interpolator = Interpolator(gd1, gd2, self.calc.wfs.dtype)
self.dphi = None # PAW correction (will be initialized when needed)
def __init__(self, cell_cv, nk_c, txt=None):
txt = txt or sys.stdout
self.nk_c = nk_c
bigcell_cv = cell_cv * nk_c[:, np.newaxis]
L_c = (np.linalg.inv(bigcell_cv)**2).sum(0)**-0.5
rc = 0.5 * L_c.min()
print('Inner radius for %dx%dx%d Wigner-Seitz cell: %.3f Ang' %
(tuple(nk_c) + (rc * Bohr,)), file=txt)
self.a = 5 / rc
print('Range-separation parameter: %.3f Ang^-1' % (self.a / Bohr),
file=txt)
nr_c = [get_efficient_fft_size(2 * int(L * self.a * 3.0))
for L in L_c]
print('FFT size for calculating truncated Coulomb: %dx%dx%d' %
tuple(nr_c), file=txt)
self.gd = GridDescriptor(nr_c, bigcell_cv, comm=mpi.serial_comm)
v_ijk = self.gd.empty()
pos_ijkv = self.gd.get_grid_point_coordinates().transpose((1, 2, 3, 0))
corner_xv = np.dot(np.indices((2, 2, 2)).reshape((3, 8)).T, bigcell_cv)
# Ignore division by zero (in 0,0,0 corner):
with seterr(invalid='ignore'):
# Loop over first dimension to avoid too large ndarrays.
for pos_jkv, v_jk in zip(pos_ijkv, v_ijk):
# Distances to the 8 corners:
d_jkxv = pos_jkv[:, :, np.newaxis] - corner_xv
r_jk = (d_jkxv**2).sum(axis=3).min(2)**0.5
v_jk[:] = erf(self.a * r_jk) / r_jk
# Fix 0/0 corner value:
v_ijk[0, 0, 0] = 2 * self.a / pi**0.5
self.K_Q = np.fft.fftn(v_ijk) * self.gd.dv
def make_dummy_reference(l, function=None, rcut=6., a=12., n=60, dtype=float):
"""Make a mock reference wave function using a made-up radial function
as reference"""
#print 'Dummy reference: l=%d, rcut=%.02f, alpha=%.02f' % (l, rcut, alpha)
r = np.arange(0., rcut, .01)
if function is None:
function = QuasiGaussian(4., rcut)
norm = get_norm(r, function(r), l)
function.renormalize(norm)
#g = QuasiGaussian(alpha, rcut)
mcount = 2 * l + 1
fcount = 1
gd = GridDescriptor((n, n, n), (a, a, a), (False, False, False))
spline = Spline(l, r[-1], function(r), points=50)
center = (.5, .5, .5)
lf = create_localized_functions([spline], gd, center, dtype=dtype)
psit_k = gd.zeros(mcount, dtype=dtype)
coef_xi = np.identity(mcount * fcount, dtype=dtype)
lf.add(psit_k, coef_xi)
return gd, psit_k, center, function
def extended_grid_descriptor(gd,
extend_N_cd=None,
N_c=None, extcomm=None):
"""Create grid descriptor for extended grid.
Provide only either extend_N_cd or N_c.
Parameters:
extend_N_cd: ndarray, int
Number of extra grid points per axis (c) and
direction (d, left or right)
N_c: ndarray, int
Number of grid points in extended grid
extcomm:
Communicator for the extended grid, defaults to gd.comm
"""
if extcomm is None:
extcomm = gd.comm
if extend_N_cd is None:
assert N_c is not None, 'give only extend_N_cd or N_c'
N_c = np.array(N_c, dtype=np.int)
extend_N_cd = np.tile((N_c - gd.N_c) // 2, (2, 1)).T
else: # extend_N_cd is not None:
assert N_c is None, 'give only extend_N_cd or N_c'
extend_N_cd = np.array(extend_N_cd, dtype=np.int)
N_c = gd.N_c + extend_N_cd.sum(axis=1)
cell_cv = gd.h_cv * N_c
move_c = gd.get_grid_spacings() * extend_N_cd[:, 0]
egd = GridDescriptor(N_c, cell_cv, gd.pbc_c, extcomm)
egd.extend_N_cd = extend_N_cd
return egd, cell_cv * Bohr, move_c * Bohr
def rigorous_testing():
from itertools import product, permutations, cycle
from gpaw.mpi import world
gridpointcounts = [1, 2, 10, 21]
cpucounts = np.arange(1, world.size + 1)
pbc = cycle(product([0, 1], [0, 1], [0, 1]))
# This yields all possible parallelizations!
for parsize_c in product(cpucounts, cpucounts, cpucounts):
if np.prod(parsize_c) != world.size:
continue
# All possible grid point counts
for N_c in product(gridpointcounts, gridpointcounts, gridpointcounts):
# We simply can't be bothered to also do all possible
# combinations with PBCs. Trying every possible set of
# boundary conditions at least ones should be quite fine
# enough.
pbc_c = next(pbc)
for dirs in permutations([0, 1, 2]):
independent_dir, distribute_dir, reduce_dir = dirs
parsize2_c = list(parsize_c)
parsize2_c[reduce_dir] = 1
parsize2_c[distribute_dir] *= parsize_c[reduce_dir]
parsize2_c = tuple(parsize2_c)
assert np.prod(parsize2_c) == np.prod(parsize_c)
try:
gd = GridDescriptor(N_c=N_c,
pbc_c=pbc_c,
cell_cv=0.2 * np.array(N_c),
parsize_c=parsize_c)
gd2 = get_compatible_grid_descriptor(
gd, distribute_dir, reduce_dir)
#gd2 = gd.new_descriptor(parsize_c=parsize2_c)
except ValueError: # Skip illegal distributions
continue
if gd.comm.rank == 1:
#print(gd, gd2)
print(
'N_c=%s[%s] redist %s -> %s [ind=%d dist=%d red=%d]' %
(N_c, pbc_c, parsize_c, parsize2_c, independent_dir,
distribute_dir, reduce_dir))
gd.comm.barrier()
test(N_c, gd, gd2, reduce_dir, distribute_dir, verbose=False)
def make_dummy_kpt_reference(l,
function,
k_c,
rcut=6.,
a=10.,
n=60,
dtype=complex):
r = np.linspace(0., rcut, 300)
mcount = 2 * l + 1
fcount = 1
kcount = 1
gd = GridDescriptor((n, n, n), (a, a, a), (True, True, True))
kpt = KPoint([], gd, 1., 0, 0, 0, k_c, dtype)
spline = Spline(l, r[-1], function(r))
center = (.5, .5, .5)
lf = create_localized_functions([spline], gd, center, dtype=dtype)
lf.set_phase_factors([kpt.k_c])
psit_nG = gd.zeros(mcount, dtype=dtype)
coef_xi = np.identity(mcount * fcount, dtype=dtype)
lf.add(psit_nG, coef_xi, k=0)
kpt.psit_nG = psit_nG
print 'Number of boxes', len(lf.box_b)
print 'Phase kb factors shape', lf.phase_kb.shape
return gd, kpt, center
def apply_t(self):
"""Apply kinetic energy operator and return new object."""
p = 2 # padding
newsize_c = self.size_c + 2 * p
gd = GridDescriptor(N_c=newsize_c + 1,
cell_cv=self.gd.h_c * (newsize_c + 1),
pbc_c=False,
comm=mpi.serial_comm)
T = Laplace(gd, scale =1/2., n=p)
f_ig = np.zeros((len(self.f_iG),) + tuple(newsize_c))
f_ig[:, p:-p, p:-p, p:-p] = self.f_iG
Tf_iG = np.empty_like(f_ig)
T.apply(f_ig, Tf_iG)
return LocalizedFunctions(self.gd, Tf_iG, self.corner_c - p,
self.index)
def make_dummy_reference(l, function=None, rcut=6., a=12., n=60,
dtype=float):
"""Make a mock reference wave function using a made-up radial function
as reference"""
#print 'Dummy reference: l=%d, rcut=%.02f, alpha=%.02f' % (l, rcut, alpha)
r = np.arange(0., rcut, .01)
if function is None:
function = QuasiGaussian(4., rcut)
norm = get_norm(r, function(r), l)
function.renormalize(norm)
#g = QuasiGaussian(alpha, rcut)
mcount = 2*l + 1
fcount = 1
gd = GridDescriptor((n, n, n), (a, a, a), (False, False, False))
spline = Spline(l, r[-1], function(r), points=50)
center = (.5, .5, .5)
lf = create_localized_functions([spline], gd, center, dtype=dtype)
psit_k = gd.zeros(mcount, dtype=dtype)
coef_xi = np.identity(mcount * fcount, dtype=dtype)
lf.add(psit_k, coef_xi)
return gd, psit_k, center, function
def get_electrostatic_potential(self, ae=True, rcgauss=0.02):
ham = self.calc.hamiltonian
if ham.vHt_g is None:
self.calc.restore_state()
gd = ham.finegd
v_r = gd.zero_pad(ham.vHt_g)
gd1 = GridDescriptor(gd.N_c, gd.cell_cv, comm=serial_comm)
interpolator = Interpolator(gd1, self.gd)
v_R = interpolator.interpolate(v_r)
if ae:
alpha = 1 / (rcgauss / Bohr)**2
self.add_potential_correction(v_R, alpha)
return v_R * Hartree
def set_grid_descriptor(self, gd):
if self.size is None:
self.shape = gd.N_c.copy()
for c, n in enumerate(self.shape):
if not gd.pbc_c[c]:
# self.shape[c] = get_efficient_fft_size(n)
self.shape[c] = int(2**ceil(log(n) / log(2)))
else:
self.shape = np.array(self.size)
for c, n in enumerate(self.shape):
if gd.pbc_c[c]:
assert n == gd.N_c[c]
else:
assert n >= gd.N_c[c]
if self.alphas:
scale_c1 = (self.shape / (1.0 * gd.N_c))[:, np.newaxis]
gdfft = GridDescriptor(self.shape, gd.cell_cv * scale_c1, True)
k_k = construct_reciprocal(gdfft)[0][:, :, :self.shape[2] // 2 +
1]**0.5
k_k[0, 0, 0] = 0.0
self.dj_k = k_k / (2 * pi / self.rcut)
self.j_k = self.dj_k.astype(int)
self.dj_k -= self.j_k
self.dj_k *= 2 * pi / self.rcut
if self.verbose:
print('VDW: density array size:',
gd.get_size_of_global_array())
print('VDW: zero-padded array size:', self.shape)
print(('VDW: maximum kinetic energy: %.3f Hartree' %
(0.5 * k_k.max()**2)))
assert self.j_k.max(
) < self.Nr // 2, 'Use larger Nr than %i.' % self.Nr
else:
self.dj_k = None
self.j_k = None
def f(n, p):
N = 2 * n
gd = GridDescriptor((N, N, N), (L, L, L))
a = gd.zeros()
print(a.shape)
#p = PoissonSolver(nn=1, relax=relax)
p.set_grid_descriptor(gd)
p.initialize()
cut = N / 2.0 * 0.9
s = Spline(l=0, rmax=cut, f_g=np.array([1, 0.5, 0.0]))
c = LFC(gd, [[s], [s]])
c.set_positions([(0, 0, 0), (0.5, 0.5, 0.5)])
c.add(a)
I0 = gd.integrate(a)
a -= gd.integrate(a) / L**3
I = gd.integrate(a)
b = gd.zeros()
p.solve(b, a, charge=0)#, eps=1e-20)
return gd.collect(b, broadcast=1)
from time import time
from gpaw.transformers import Transformer
from gpaw.grid_descriptor import GridDescriptor
from gpaw.mpi import world
ngpts = 80
N_c = (ngpts, ngpts, ngpts)
a = 10.0
gd = GridDescriptor(N_c, (a, a, a))
gdfine = gd.refine()
interpolate = Transformer(gd, gdfine, 3).apply
a1 = gd.empty()
a1[:] = 1.0
f = gdfine.empty()
ta = time()
r = 600
for i in range(r):
interpolate(a1, f)
tb = time()
n = 8 * (1 + 2 + 4) * ngpts**3
print '%.3f GFlops' % (r * n / (tb - ta) * 1e-9)
"""
python: 0.330 GFlops
mpirun -np 2 gpaw-python: 0.500 GFlops
gpaw-python + OMP: 0.432 GFlops
"""
n[-1, i] -= 0.000002
Em = xc.calculate_spherical(rgd, n, v)
x2 = (Ep - Em) / 0.000002
print(name, nspins, E, x, x2, x - x2)
equal(x, x2, 1e-9)
n[-1, i] += 0.000001
if nspins == 1:
ns = rgd.empty(2)
ns[:] = n / 2
Es = xc.calculate_spherical(rgd, ns, 0 * ns)
equal(E, Es, 1e-13)
N = 20
a = 1.0
gd = GridDescriptor((N, N, N), (a, a, a))
for name in ['LDA', 'PBE']:
xc = XC(name)
for nspins in [1, 2]:
n = gd.empty(nspins)
n.fill(0.03)
z = np.arange(gd.beg_c[2], gd.end_c[2]) * a / N
n[:] += 0.01 * np.sin(2 * pi * z / a)
if nspins == 2:
n[1] += 0.01 * np.cos(2 * pi * z / a)
n /= nspins
v = 0.0 * n
E = xc.calculate(gd, n, v)
from gpaw.transformers import Transformer import numpy.random as ra from gpaw.grid_descriptor import GridDescriptor p = 0 n = 20 gd1 = GridDescriptor((n, n, n), (8.0, 8.0, 8.0), pbc_c=p) a1 = gd1.zeros() ra.seed(8) a1[:] = ra.random(a1.shape) gd2 = gd1.refine() a2 = gd2.zeros() i = Transformer(gd1, gd2).apply i(a1, a2) assert abs(gd1.integrate(a1) - gd2.integrate(a2)) < 1e-10 r = Transformer(gd2, gd1).apply a2[0] = 0.0 a2[:, 0] = 0.0 a2[:, :, 0] = 0.0 a2[-1] = 0.0 a2[:, -1] = 0.0 a2[:, :, -1] = 0.0 r(a2, a1) assert abs(gd1.integrate(a1) - gd2.integrate(a2)) < 1e-10
class DF(CHI):
"""This class defines dielectric function related physical quantities."""
def __init__(self,
calc=None,
nbands=None,
w=None,
q=None,
eshift=None,
ecut=10.,
density_cut=None,
G_plus_q=False,
eta=0.2,
rpad=None,
vcut=None,
ftol=1e-7,
txt=None,
xc='ALDA',
print_xc_scf=False,
hilbert_trans=True,
time_ordered=False,
optical_limit=False,
comm=None,
kcommsize=None):
CHI.__init__(self, calc=calc, nbands=nbands, w=w, q=q, eshift=eshift,
ecut=ecut, density_cut=density_cut,
G_plus_q=G_plus_q, eta=eta, rpad=rpad, vcut=vcut,
ftol=ftol, txt=txt, xc=xc, hilbert_trans=hilbert_trans,
time_ordered=time_ordered, optical_limit=optical_limit,
comm=comm, kcommsize=kcommsize)
self.df_flag = False
self.print_bootstrap = print_xc_scf
self.df1_w = None # NLF RPA
self.df2_w = None # LF RPA
self.df3_w = None # NLF ALDA
self.df4_w = None # LF ALDA
def get_dielectric_matrix(self,
xc='RPA',
overwritechi0=False,
symmetric=True,
chi0_wGG=None,
calc=None,
vcut=None,
dir=None):
if self.chi0_wGG is None and chi0_wGG is None:
self.initialize()
self.calculate()
elif self.chi0_wGG is None and chi0_wGG is not None:
#Read from file and reinitialize
self.xc = xc
from gpaw.response.parallel import par_read
self.chi0_wGG = par_read(chi0_wGG, 'chi0_wGG')
self.nvalbands = self.nbands
#self.parallel_init() # parallelization not yet implemented
self.Nw_local = self.Nw # parallelization not yet implemented
if self.calc is None:
from gpaw import GPAW
self.calc = GPAW(calc,txt=None)
if self.xc == 'ALDA' or self.xc == 'ALDA_X':
from gpaw.response.kernel import calculate_Kxc
from gpaw.grid_descriptor import GridDescriptor
from gpaw.mpi import world, rank, size, serial_comm
self.pbc = self.calc.atoms.pbc
self.gd = GridDescriptor(self.calc.wfs.gd.N_c*self.rpad, self.acell_cv,
pbc_c=True, comm=serial_comm)
R_av = self.calc.atoms.positions / Bohr
nt_sg = self.calc.density.nt_sG
if (self.rpad > 1).any() or (self.pbc - True).any():
nt_sG = self.gd.zeros(self.nspins)
#nt_sG = np.zeros([self.nspins, self.nG[0], self.nG[1], self.nG[2]])
for s in range(self.nspins):
nt_G = self.pad(nt_sg[s])
nt_sG[s] = nt_G
else:
nt_sG = nt_sg
self.Kxc_sGG = calculate_Kxc(self.gd,
nt_sG,
self.npw, self.Gvec_Gc,
self.gd.N_c, self.vol,
self.bcell_cv, R_av,
self.calc.wfs.setups,
self.calc.density.D_asp,
functional=self.xc,
density_cut=self.density_cut)
if overwritechi0:
dm_wGG = self.chi0_wGG
else:
dm_wGG = np.zeros_like(self.chi0_wGG)
if dir is None:
q_c = self.q_c
else:
q_c = np.diag((1,1,1))[dir] * self.qopt
self.chi0_wGG[:,0,:] = self.chi00G_wGv[:,:,dir]
self.chi0_wGG[:,:,0] = self.chi0G0_wGv[:,:,dir]
from gpaw.response.kernel import calculate_Kc, CoulombKernel
kernel = CoulombKernel(vcut=self.vcut,
pbc=self.calc.atoms.pbc,
cell=self.acell_cv)
self.Kc_GG = kernel.calculate_Kc(q_c,
self.Gvec_Gc,
self.bcell_cv,
symmetric=symmetric)
#self.Kc_GG = calculate_Kc(q_c,
# self.Gvec_Gc,
# self.acell_cv,
# self.bcell_cv,
# self.pbc,
# self.vcut,
# symmetric=symmetric)
tmp_GG = np.eye(self.npw, self.npw)
if xc == 'RPA':
self.printtxt('Use RPA.')
for iw in range(self.Nw_local):
dm_wGG[iw] = tmp_GG - self.Kc_GG * self.chi0_wGG[iw]
elif xc == 'ALDA':
self.printtxt('Use ALDA kernel.')
# E_LDA = 1 - v_c chi0 (1-fxc chi0)^-1
# http://prb.aps.org/pdf/PRB/v33/i10/p7017_1 eq. 4
A_wGG = self.chi0_wGG.copy()
for iw in range(self.Nw_local):
A_wGG[iw] = np.dot(self.chi0_wGG[iw], np.linalg.inv(tmp_GG - np.dot(self.Kxc_sGG[0], self.chi0_wGG[iw])))
for iw in range(self.Nw_local):
dm_wGG[iw] = tmp_GG - self.Kc_GG * A_wGG[iw]
return dm_wGG
def get_inverse_dielectric_matrix(self, xc='RPA'):
dm_wGG = self.get_dielectric_matrix(xc=xc)
dminv_wGG = np.zeros_like(dm_wGG)
for iw in range(self.Nw_local):
dminv_wGG[iw] = np.linalg.inv(dm_wGG[iw])
return dminv_wGG
def get_chi(self, xc='RPA'):
"""Solve Dyson's equation."""
if self.chi0_wGG is None:
self.initialize()
self.calculate()
else:
pass # read from file and re-initializing .... need to be implemented
kernel_GG = np.zeros((self.npw, self.npw), dtype=complex)
chi_wGG = np.zeros_like(self.chi0_wGG)
# Coulomb kernel
for iG in range(self.npw):
qG = np.dot(self.q_c + self.Gvec_Gc[iG], self.bcell_cv)
kernel_GG[iG,iG] = 4 * pi / np.dot(qG, qG)
if xc == 'ALDA':
kernel_GG += self.Kxc_sGG[0]
for iw in range(self.Nw_local):
tmp_GG = np.eye(self.npw, self.npw) - np.dot(self.chi0_wGG[iw], kernel_GG)
chi_wGG[iw] = np.dot(np.linalg.inv(tmp_GG) , self.chi0_wGG[iw])
return chi_wGG
def get_dielectric_function(self, xc='RPA', dir=None):
"""Calculate the dielectric function. Returns df1_w and df2_w.
Parameters:
df1_w: ndarray
Dielectric function without local field correction.
df2_w: ndarray
Dielectric function with local field correction.
"""
if not self.optical_limit:
assert dir is None
if self.df_flag is False:
dm_wGG = self.get_dielectric_matrix(xc=xc, dir=dir)
Nw_local = dm_wGG.shape[0]
dfNLF_w = np.zeros(Nw_local, dtype = complex)
dfLFC_w = np.zeros(Nw_local, dtype = complex)
df1_w = np.zeros(self.Nw, dtype = complex)
df2_w = np.zeros(self.Nw, dtype = complex)
for iw in range(Nw_local):
tmp_GG = dm_wGG[iw]
dfLFC_w[iw] = 1. / np.linalg.inv(tmp_GG)[0, 0]
dfNLF_w[iw] = tmp_GG[0, 0]
self.wcomm.all_gather(dfNLF_w, df1_w)
self.wcomm.all_gather(dfLFC_w, df2_w)
if xc == 'RPA':
self.df1_w = df1_w
self.df2_w = df2_w
elif xc=='ALDA' or xc=='ALDA_X':
self.df3_w = df1_w
self.df4_w = df2_w
if xc == 'RPA':
return self.df1_w, self.df2_w
elif xc == 'ALDA' or xc=='ALDA_X':
return self.df3_w, self.df4_w
def get_surface_response_function(self, z0=0., filename='surf_EELS'):
"""Calculate surface response function."""
if self.chi0_wGG is None:
self.initialize()
self.calculate()
g_w2 = np.zeros((self.Nw,2), dtype=complex)
assert self.acell_cv[0, 2] == 0. and self.acell_cv[1, 2] == 0.
Nz = self.gd.N_c[2] # number of points in z direction
tmp = np.zeros(Nz, dtype=int)
nGz = 0 # number of G_z
for i in range(self.npw):
if self.Gvec_Gc[i, 0] == 0 and self.Gvec_Gc[i, 1] == 0:
tmp[nGz] = self.Gvec_Gc[i, 2]
nGz += 1
assert (np.abs(self.Gvec_Gc[:nGz, :2]) < 1e-10).all()
for id, xc in enumerate(['RPA', 'ALDA']):
chi_wGG = self.get_chi(xc=xc)
# The first nGz are all Gx=0 and Gy=0 component
chi_wgg_LFC = chi_wGG[:, :nGz, :nGz]
del chi_wGG
chi_wzz_LFC = np.zeros((self.Nw_local, Nz, Nz), dtype=complex)
# Fourier transform of chi_wgg to chi_wzz
Gz_g = tmp[:nGz] * self.bcell_cv[2,2]
z_z = np.linspace(0, self.acell_cv[2,2]-self.gd.h_cv[2,2], Nz)
phase1_zg = np.exp(1j * np.outer(z_z, Gz_g))
phase2_gz = np.exp(-1j * np.outer(Gz_g, z_z))
for iw in range(self.Nw_local):
chi_wzz_LFC[iw] = np.dot(np.dot(phase1_zg, chi_wgg_LFC[iw]), phase2_gz)
chi_wzz_LFC /= self.acell_cv[2,2]
# Get surface response function
z_z -= z0 / Bohr
q_v = np.dot(self.q_c, self.bcell_cv)
qq = sqrt(np.inner(q_v, q_v))
phase1_1z = np.array([np.exp(qq*z_z)])
phase2_z1 = np.exp(qq*z_z)
tmp_w = np.zeros(self.Nw_local, dtype=complex)
for iw in range(self.Nw_local):
tmp_w[iw] = np.dot(np.dot(phase1_1z, chi_wzz_LFC[iw]), phase2_z1)[0]
tmp_w *= -2 * pi / qq * self.gd.h_cv[2,2]**2
g_w = np.zeros(self.Nw, dtype=complex)
self.wcomm.all_gather(tmp_w, g_w)
g_w2[:, id] = g_w
if rank == 0:
f = open(filename,'w')
for iw in range(self.Nw):
energy = iw * self.dw * Hartree
print >> f, energy, np.imag(g_w2[iw, 0]), np.imag(g_w2[iw, 1])
f.close()
# Wait for I/O to finish
self.comm.barrier()
def check_sum_rule(self, df1_w=None, df2_w=None):
"""Check f-sum rule."""
if df1_w is None:
df1_w = self.df1_w
df2_w = self.df2_w
N1 = N2 = 0
for iw in range(self.Nw):
w = iw * self.dw
N1 += np.imag(df1_w[iw]) * w
N2 += np.imag(df2_w[iw]) * w
N1 *= self.dw * self.vol / (2 * pi**2)
N2 *= self.dw * self.vol / (2 * pi**2)
self.printtxt('')
self.printtxt('Sum rule for ABS:')
nv = self.nvalence
self.printtxt('Without local field: N1 = %f, %f %% error' %(N1, (N1 - nv) / nv * 100) )
self.printtxt('Include local field: N2 = %f, %f %% error' %(N2, (N2 - nv) / nv * 100) )
N1 = N2 = 0
for iw in range(self.Nw):
w = iw * self.dw
N1 -= np.imag(1/df1_w[iw]) * w
N2 -= np.imag(1/df2_w[iw]) * w
N1 *= self.dw * self.vol / (2 * pi**2)
N2 *= self.dw * self.vol / (2 * pi**2)
self.printtxt('')
self.printtxt('Sum rule for EELS:')
nv = self.nvalence
self.printtxt('Without local field: N1 = %f, %f %% error' %(N1, (N1 - nv) / nv * 100) )
self.printtxt('Include local field: N2 = %f, %f %% error' %(N2, (N2 - nv) / nv * 100) )
def get_macroscopic_dielectric_constant(self, xc='RPA'):
"""Calculate macroscopic dielectric constant. Returns eM1 and eM2
Macroscopic dielectric constant is defined as the real part of dielectric function at w=0.
Parameters:
eM1: float
Dielectric constant without local field correction. (RPA, ALDA)
eM2: float
Dielectric constant with local field correction.
"""
assert self.optical_limit
self.printtxt('')
self.printtxt('%s Macroscopic Dielectric Constant:' % xc)
dirstr = ['x', 'y', 'z']
for dir in range(3):
eM = np.zeros(2)
df1, df2 = self.get_dielectric_function(xc=xc, dir=dir)
eps0 = np.real(df1[0])
eps = np.real(df2[0])
self.printtxt(' %s direction' %(dirstr[dir]))
self.printtxt(' Without local field: %f' % eps0 )
self.printtxt(' Include local field: %f' % eps )
return eps0, eps
def get_absorption_spectrum(self, filename='Absorption.dat'):
"""Calculate optical absorption spectrum. By default, generate a file 'Absorption.dat'.
Optical absorption spectrum is obtained from the imaginary part of dielectric function.
"""
assert self.optical_limit
for dir in range(3):
df1, df2 = self.get_dielectric_function(xc='RPA', dir=dir)
if self.xc == 'ALDA':
df3, df4 = self.get_dielectric_function(xc='ALDA', dir=dir)
if self.xc is 'ALDA_X':
df3, df4 = self.get_dielectric_function(xc='ALDA_X', dir=dir)
Nw = df1.shape[0]
if self.xc == 'Bootstrap':
# bootstrap doesnt support all direction spectra yet
from gpaw.response.fxc import Bootstrap
Kc_GG = np.zeros((self.npw, self.npw))
q_c = np.diag((1, 1, 1))[dir] * self.qopt
for iG in range(self.npw):
qG = np.dot(q_c + self.Gvec_Gc[iG], self.bcell_cv)
Kc_GG[iG,iG] = 4 * pi / np.dot(qG, qG)
from gpaw.mpi import world
assert self.wcomm.size == world.size
df3 = Bootstrap(self.chi0_wGG, Nw, Kc_GG, self.printtxt, self.print_bootstrap, self.wcomm)
if rank == 0:
f = open('%s.%s' % (filename, 'xyz'[dir]), 'w')
#f = open(filename+'.%s'%(dirstr[dir]),'w') # ????
for iw in range(Nw):
energy = iw * self.dw * Hartree
if self.xc == 'RPA':
print >> f, energy, np.real(df1[iw]), np.imag(df1[iw]), \
np.real(df2[iw]), np.imag(df2[iw])
elif self.xc == 'ALDA':
print >> f, energy, np.real(df1[iw]), np.imag(df1[iw]), \
np.real(df2[iw]), np.imag(df2[iw]), \
np.real(df3[iw]), np.imag(df3[iw]), \
np.real(df4[iw]), np.imag(df4[iw])
elif self.xc == 'Bootstrap':
print >> f, energy, np.real(df1[iw]), np.imag(df1[iw]), \
np.real(df2[iw]), np.imag(df2[iw]), \
np.real(df3[iw]), np.imag(df3[iw])
f.close()
# Wait for I/O to finish
self.comm.barrier()
def get_EELS_spectrum(self, filename='EELS.dat'):
"""Calculate EELS spectrum. By default, generate a file 'EELS.dat'.
EELS spectrum is obtained from the imaginary part of the inverse of dielectric function.
"""
# calculate RPA dielectric function
df1, df2 = self.get_dielectric_function(xc='RPA')
if self.xc == 'ALDA':
df3, df4 = self.get_dielectric_function(xc='ALDA')
Nw = df1.shape[0]
if rank == 0:
f = open(filename,'w')
for iw in range(self.Nw):
energy = iw * self.dw * Hartree
if self.xc == 'RPA':
print >> f, energy, -np.imag(1./df1[iw]), -np.imag(1./df2[iw])
elif self.xc == 'ALDA':
print >> f, energy, -np.imag(1./df1[iw]), -np.imag(1./df2[iw]), \
-np.imag(1./df3[iw]), -np.imag(1./df4[iw])
f.close()
# Wait for I/O to finish
self.comm.barrier()
def get_jdos(self, f_skn, e_skn, kd, kq, dw, Nw, sigma):
"""Calculate Joint density of states"""
JDOS_w = np.zeros(Nw)
nbands = f_skn[0].shape[1]
for k in range(kd.nbzkpts):
print k
ibzkpt1 = kd.bz2ibz_k[k]
ibzkpt2 = kd.bz2ibz_k[kq[k]]
for n in range(nbands):
for m in range(nbands):
focc = f_skn[0][ibzkpt1, n] - f_skn[0][ibzkpt2, m]
w0 = e_skn[0][ibzkpt2, m] - e_skn[0][ibzkpt1, n]
if focc > 0 and w0 >= 0:
w0_id = int(w0 / dw)
if w0_id + 1 < Nw:
alpha = (w0_id + 1 - w0/dw) / dw
JDOS_w[w0_id] += focc * alpha
alpha = (w0/dw-w0_id) / dw
JDOS_w[w0_id+1] += focc * alpha
w = np.arange(Nw) * dw * Hartree
return w, JDOS_w
def calculate_induced_density(self, q, w):
""" Evaluate induced density for a certain q and w.
Parameters:
q: ndarray
Momentum tranfer at reduced coordinate.
w: scalar
Energy (eV).
"""
if type(w) is int:
iw = w
w = self.wlist[iw] / Hartree
elif type(w) is float:
w /= Hartree
iw = int(np.round(w / self.dw))
else:
raise ValueError('Frequency not correct !')
self.printtxt('Calculating Induced density at q, w (iw)')
self.printtxt('(%f, %f, %f), %f(%d)' %(q[0], q[1], q[2], w*Hartree, iw))
# delta_G0
delta_G = np.zeros(self.npw)
delta_G[0] = 1.
# coef is (q+G)**2 / 4pi
coef_G = np.zeros(self.npw)
for iG in range(self.npw):
qG = np.dot(q + self.Gvec_Gc[iG], self.bcell_cv)
coef_G[iG] = np.dot(qG, qG)
coef_G /= 4 * pi
# obtain chi_G0(q,w)
dm_wGG = self.get_RPA_dielectric_matrix()
tmp_GG = dm_wGG[iw]
del dm_wGG
chi_G = (np.linalg.inv(tmp_GG)[:, 0] - delta_G) * coef_G
gd = self.gd
r = gd.get_grid_point_coordinates()
# calculate dn(r,q,w)
drho_R = gd.zeros(dtype=complex)
for iG in range(self.npw):
qG = np.dot(q + self.Gvec_Gc[iG], self.bcell_cv)
qGr_R = np.inner(qG, r.T).T
drho_R += chi_G[iG] * np.exp(1j * qGr_R)
# phase = sum exp(iq.R_i)
return drho_R
def get_induced_density_z(self, q, w):
"""Get induced density on z axis (summation over xy-plane). """
drho_R = self.calculate_induced_density(q, w)
drho_z = np.zeros(self.gd.N_c[2],dtype=complex)
# dxdy = np.cross(self.h_c[0], self.h_c[1])
for iz in range(self.gd.N_c[2]):
drho_z[iz] = drho_R[:,:,iz].sum()
return drho_z
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 project_chi_to_LCAO_pair_orbital(self, orb_MG):
nLCAO = orb_MG.shape[0]
N = np.zeros((self.Nw, nLCAO, nLCAO), dtype=complex)
kcoulinv_GG = np.zeros((self.npw, self.npw))
for iG in range(self.npw):
qG = np.dot(self.q_c + self.Gvec_Gc[iG], self.bcell_cv)
kcoulinv_GG[iG, iG] = np.dot(qG, qG)
kcoulinv_GG /= 4.*pi
dm_wGG = self.get_RPA_dielectric_matrix()
for mu in range(nLCAO):
for nu in range(nLCAO):
pairorb_R = orb_MG[mu] * orb_MG[nu]
if not (pairorb_R * pairorb_R.conj() < 1e-10).all():
tmp_G = np.fft.fftn(pairorb_R) * self.vol / self.nG0
pairorb_G = np.zeros(self.npw, dtype=complex)
for iG in range(self.npw):
index = self.Gindex[iG]
pairorb_G[iG] = tmp_G[index[0], index[1], index[2]]
for iw in range(self.Nw):
chi_GG = (dm_wGG[iw] - np.eye(self.npw)) * kcoulinv_GG
N[iw, mu, nu] = (np.outer(pairorb_G.conj(), pairorb_G) * chi_GG).sum()
# N[iw, mu, nu] = np.inner(pairorb_G.conj(),np.inner(pairorb_G, chi_GG))
return N
def write(self, filename, all=False):
"""Dump essential data"""
data = {'nbands': self.nbands,
'acell': self.acell_cv, #* Bohr,
'bcell': self.bcell_cv, #/ Bohr,
'h_cv' : self.gd.h_cv, #* Bohr,
'nG' : self.gd.N_c,
'nG0' : self.nG0,
'vol' : self.vol, #* Bohr**3,
'BZvol': self.BZvol, #/ Bohr**3,
'nkpt' : self.kd.nbzkpts,
'ecut' : self.ecut, #* Hartree,
'npw' : self.npw,
'eta' : self.eta, #* Hartree,
'ftol' : self.ftol,
'Nw' : self.Nw,
'NwS' : self.NwS,
'dw' : self.dw, # * Hartree,
'q_red': self.q_c,
'q_car': self.qq_v, # / Bohr,
'qmod' : np.dot(self.qq_v, self.qq_v), # / Bohr
'vcut' : self.vcut,
'pbc' : self.pbc,
'nvalence' : self.nvalence,
'hilbert_trans' : self.hilbert_trans,
'optical_limit' : self.optical_limit,
'e_skn' : self.e_skn, # * Hartree,
'f_skn' : self.f_skn,
'bzk_kc' : self.kd.bzk_kc,
'ibzk_kc' : self.kd.ibzk_kc,
'kq_k' : self.kq_k,
'Gvec_Gc' : self.Gvec_Gc,
'dfNLFRPA_w' : self.df1_w,
'dfLFCRPA_w' : self.df2_w,
'dfNLFALDA_w' : self.df3_w,
'dfLFCALDA_w' : self.df4_w,
'df_flag' : True}
if all:
from gpaw.response.parallel import par_write
par_write('chi0' + filename,'chi0_wGG',self.wcomm,self.chi0_wGG)
if rank == 0:
pickle.dump(data, open(filename, 'w'), -1)
self.comm.barrier()
def read(self, filename):
"""Read data from pickle file"""
data = pickle.load(open(filename))
self.nbands = data['nbands']
self.acell_cv = data['acell']
self.bcell_cv = data['bcell']
self.nG0 = data['nG0']
self.vol = data['vol']
self.BZvol = data['BZvol']
self.ecut = data['ecut']
self.npw = data['npw']
self.eta = data['eta']
self.ftol = data['ftol']
self.Nw = data['Nw']
self.NwS = data['NwS']
self.dw = data['dw']
self.q_c = data['q_red']
self.qq_v = data['q_car']
self.qmod = data['qmod']
#self.vcut = data['vcut']
#self.pbc = data['pbc']
self.hilbert_trans = data['hilbert_trans']
self.optical_limit = data['optical_limit']
self.e_skn = data['e_skn']
self.f_skn = data['f_skn']
self.nvalence= data['nvalence']
self.kq_k = data['kq_k']
self.Gvec_Gc = data['Gvec_Gc']
self.df1_w = data['dfNLFRPA_w']
self.df2_w = data['dfLFCRPA_w']
self.df3_w = data['dfNLFALDA_w']
self.df4_w = data['dfLFCALDA_w']
self.df_flag = data['df_flag']
self.printtxt('Read succesfully !')
from gpaw.fd_operators import Gradient
import numpy as np
from gpaw.grid_descriptor import GridDescriptor
from gpaw.mpi import world
if world.size > 4:
# Grid is so small that domain decomposition cannot exceed 4 domains
assert world.size % 4 == 0
group, other = divmod(world.rank, 4)
ranks = np.arange(4*group, 4*(group+1))
domain_comm = world.new_communicator(ranks)
else:
domain_comm = world
gd = GridDescriptor((8, 1, 1), (8.0, 1.0, 1.0), comm=domain_comm)
a = gd.zeros()
dadx = gd.zeros()
a[:, 0, 0] = np.arange(gd.beg_c[0], gd.end_c[0])
gradx = Gradient(gd, v=0)
print a.itemsize, a.dtype, a.shape
print dadx.itemsize, dadx.dtype, dadx.shape
gradx.apply(a, dadx)
# a = [ 0. 1. 2. 3. 4. 5. 6. 7.]
#
# da
# -- = [-2.5 1. 1. 1. 1. 1. 1. -2.5]
# dx
dadx = gd.collect(dadx, broadcast=True)
assert dadx[3, 0, 0] == 1.0 and np.sum(dadx[:, 0, 0]) == 0.0
assert np.prod(D) * B == world.size, 'D=%s, B=%d, W=%d' % (D,B,world.size)
# Set up communicators:
comms = distribute_cpus(parsize_domain=D,
parsize_bands=B,
nspins=1, nibzkpts=1)
domain_comm, kpt_comm, band_comm, block_comm = \
[comms[name] for name in ['d', 'k', 'b', 'K']]
assert kpt_comm.size == 1
if world.rank == 0:
print('MPI: %d domains, %d band groups' % (domain_comm.size, band_comm.size))
# Set up band and grid descriptors:
bd = BandDescriptor(N, band_comm, False)
gd = GridDescriptor((G, G, G), (a, a, a), True, domain_comm, parsize=D)
ksl = BandLayouts(gd, bd, block_comm, float)
# Random wave functions:
psit_mG = gd.empty(M)
for m in range(M):
np.random.seed(world.rank * M + m)
psit_mG[m] = np.random.uniform(-0.5, 0.5, tuple(gd.n_c))
if world.rank == 0:
print('Size of wave function array:', psit_mG.shape)
P_ani = {0: psit_mG[:, :2, 0, 0].copy(),
1: psit_mG[:, -1, -1, -3:].copy()}
kin = Laplace(gd, -0.5, 2).apply
vt_G = gd.empty()
vt_G.fill(0.567)
from time import time
import numpy as np
from gpaw.mpi import world, size, rank
from gpaw.utilities.blas import gemm
from gpaw.mic.micblas import gemm as mic_gemm
import pymic
import sys
device = pymic.devices[0]
stream = device.get_default_stream()
gpts = int(sys.argv[1])
nbands = int(sys.argv[2])
repeats = 10
gd = GridDescriptor([gpts, gpts, gpts], comm=world, parsize=size)
a = gd.empty(nbands)
b = gd.empty(nbands)
np.random.seed(10)
a[:] = np.random.random(a.shape)
b[:] = np.random.random(b.shape)
c = np.zeros((nbands, nbands))
# warm-up
for i in range(3):
gd.integrate(a, b, hermitian=False, _transposed_result=c)
gemm(1.0, a, c, 0.0, b)
# equal(np.sum(c), 3600.89536641, 1e-6)
t0 = time()
from gpaw.test import equal from gpaw.grid_descriptor import GridDescriptor from gpaw.spline import Spline import gpaw.mpi as mpi from gpaw.lfc import LocalizedFunctionsCollection as LFC s = Spline(0, 1.0, [1.0, 0.5, 0.0]) n = 40 a = 8.0 gd = GridDescriptor((n, n, n), (a, a, a), comm=mpi.serial_comm) c = LFC(gd, [[s], [s], [s]]) c.set_positions([(0.5, 0.5, 0.25 + 0.25 * i) for i in [0, 1, 2]]) b = gd.zeros() c.add(b) x = gd.integrate(b) gd = GridDescriptor((n, n, n), (a, a, a), comm=mpi.serial_comm) c = LFC(gd, [[s], [s], [s]]) c.set_positions([(0.5, 0.5, 0.25 + 0.25 * i) for i in [0, 1, 2]]) b = gd.zeros() c.add(b) y = gd.integrate(b) equal(x, y, 1e-13)
import numpy as np
from gpaw.lfc import LocalizedFunctionsCollection as LFC
from gpaw.grid_descriptor import GridDescriptor
from gpaw.spline import Spline
a = 4.0
gd = GridDescriptor(N_c=[16, 20, 20], cell_cv=[a, a + 1, a + 2],
pbc_c=(0, 1, 1))
spos_ac = np.array([[0.25, 0.15, 0.35], [0.5, 0.5, 0.5]])
kpts_kc = None
s = Spline(l=0, rmax=2.0, f_g=np.array([1, 0.9, 0.1, 0.0]))
p = Spline(l=1, rmax=2.0, f_g=np.array([1, 0.9, 0.1, 0.0]))
spline_aj = [[s], [s, p]]
c = LFC(gd, spline_aj, cut=True, forces=True)
c.set_positions(spos_ac)
C_ani = c.dict(3, zero=True)
if 1 in C_ani:
C_ani[1][:, 1:] = np.eye(3)
psi = gd.zeros(3)
c.add(psi, C_ani)
c.integrate(psi, C_ani)
if 1 in C_ani:
d = C_ani[1][:, 1:].diagonal()
assert d.ptp() < 4e-6
C_ani[1][:, 1:] -= np.diag(d)
assert abs(C_ani[1]).max() < 5e-17
d_aniv = c.dict(3, derivative=True)
c.derivative(psi, d_aniv)
if 1 in d_aniv:
for v in range(3):
assert abs(d_aniv[1][v - 1, 0, v] + 0.2144) < 5e-5
d_aniv[1][v - 1, 0, v] = 0
from gpaw.grid_descriptor import GridDescriptor
from gpaw.fd_operators import Laplace
from gpaw.test import equal
import numpy as np
import time
import timeit
import os
import pyMIC as mic
offload_enabled = os.environ.get("GPAW_OFFLOAD");
device = mic.devices[0]
gpts = 64
gd = GridDescriptor([gpts, gpts, gpts])
a = gd.empty() # source
b = gd.empty() # result
np.random.seed(10)
a[:] = np.random.random(a.shape)
b[:] = np.zeros(b.shape)
op = Laplace(gd, 1.0, 3)
if offload_enabled:
offl_a = device.bind(a)
offl_b = device.bind(b)
print "--------------------------------------------------------------"
from gpaw.utilities.gauss import Gaussian
from gpaw.grid_descriptor import GridDescriptor
from gpaw.test import equal
from gpaw.mpi import world
from gpaw.poisson import PoissonSolver
def norm(a):
return np.sqrt(np.sum(a.ravel() ** 2)) / len(a.ravel())
# Initialize classes
a = 20 # Size of cell
N = 48 # Number of grid points
Nc = (N, N, N) # Number of grid points along each axis
gd = GridDescriptor(Nc, (a, a, a), 0) # Grid-descriptor object
solver = PoissonSolver(nn=3) # Numerical poisson solver
solver.set_grid_descriptor(gd)
solver.initialize()
solve = solver.solve
xyz, r2 = coordinates(gd) # Matrix with the square of the radial coordinate
print(r2.shape)
r = np.sqrt(r2) # Matrix with the values of the radial coordinate
nH = np.exp(-2 * r) / pi # Density of the hydrogen atom
gauss = Gaussian(gd) # An instance of Gaussian
# /------------------------------------------------\
# | Check if Gaussian densities are made correctly |
# \------------------------------------------------/
for gL in range(2, 9):
g = gauss.get_gauss(gL) # a gaussian of gL'th order
from math import pi
from gpaw.grid_descriptor import GridDescriptor
from gpaw.xc import XC
from gpaw.xc.noncollinear import NonCollinearLDAKernel, NonCollinearFunctional
import numpy as np
from gpaw.test import equal
N = 20
a = 1.0
gd = GridDescriptor((N, N, N), (a, a, a))
for xc in [XC('LDA'), XC('PBE')]:
n = gd.empty(2)
n.fill(0.03)
z = np.arange(gd.beg_c[2], gd.end_c[2]) * a / N
n[:] += 0.01 * np.sin(2 * pi * z / a)
n[1] += 0.02 + 0.01 * np.cos(2 * pi * z / a)
n /= 2
v = 0.0 * n
E = xc.calculate(gd, n, v)
here = (gd.beg_c[0] <= 1 < gd.end_c[0] and
gd.beg_c[1] <= 2 < gd.end_c[1] and
gd.beg_c[2] <= 3 < gd.end_c[2])
if here:
x = v[-1, 1, 2, 3] * gd.dv
n[-1, 1, 2, 3] += 0.000001
Ep = xc.calculate(gd, n, v)
if here:
n[-1, 1, 2, 3] -= 0.000002
def abstract_boundary(self):
# Abtract the effective potential, hartree potential, and average density
#out from the electrode calculation.
map = {'-': '0', '+': '1'}
data = self.tio.read_data(filename='Lead_' +
map[self.direction], option='Lead')
nn = data['fine_N_c'][2]
ns = data['nspins']
N_c = data['N_c']
cell_cv = data['cell_cv']
pbc_c = data['pbc_c']
#parsize_c = data['parsize_c']
if type(parsize_domain) is int:
parsize_c = None
assert parsize_domain == self.domain_comm.size
else:
parsize_c = parsize_domain
if parsize_c is None:
parsize_c = decompose_domain(N_c, self.domain_comm.size)
parsize_c = np.array(parsize_c)
d1 = N_c[0] // 2
d2 = N_c[1] // 2
vHt_g = data['vHt_g']
vt_sg = data['vt_sg']
nt_sg = data['nt_sg']
rhot_g = data['rhot_g']
vt_sG = data['vt_sG']
nt_sG = data['nt_sG']
self.D_asp = data['D_asp']
self.dH_asp = data['dH_asp']
gd = GridDescriptor(N_c, cell_cv, pbc_c, self.domain_comm, parsize_c)
finegd = gd.refine()
self.boundary_vHt_g = None
self.boundary_vHt_g1 = None
self.boundary_vt_sg_line = None
self.boundary_nt_sg = None
self.boundary_rhot_g_line = None
self.boundary_vt_sG = None
self.boundary_nt_sG = None
if self.tio.domain_comm.rank == 0:
self.boundary_vHt_g = self.slice(nn, vHt_g)
self.boundary_nt_sg = self.slice(nn, nt_sg)
if self.direction == '-':
other_direction= '+'
else:
other_direction= '-'
h = self.h_cz / 2.0
b_vHt_g0 = self.boundary_vHt_g.copy()
b_vHt_g1 = self.boundary_vHt_g.copy()
self.boundary_vHt_g = interpolate_array(b_vHt_g0,
finegd, h,
self.direction)
self.boundary_vHt_g1 = interpolate_array(b_vHt_g1,
finegd, h,
other_direction)
vt_sg = interpolate_array(vt_sg, finegd,
h, self.direction)
self.boundary_vt_sg_line = aa1d(vt_sg)
self.boundary_nt_sg = interpolate_array(self.boundary_nt_sg,
finegd, h, self.direction)
rhot_g = interpolate_array(rhot_g, finegd, h, self.direction)
self.boundary_rhot_g_line = aa1d(rhot_g)
nn /= 2
h *= 2
self.boundary_vt_sG = self.slice(nn, vt_sG)
self.boundary_nt_sG = self.slice(nn, nt_sG)
self.boundary_vt_sG = interpolate_array(self.boundary_vt_sG,
gd, h, self.direction)
self.boundary_nt_sG = interpolate_array(self.boundary_nt_sG,
gd, h, self.direction)
if size == 1:
for name, D, cell in cells:
if name == 'Jelver':
# Strange one!
continue
print('------------------')
print(name, D)
print(cell[0])
print(cell[1])
print(cell[2])
for n in range(1, 6):
N = 2 * n + 2
gd = GridDescriptor((N, N, N), cell)
b_g = gd.zeros()
r_gv = gd.get_grid_point_coordinates().transpose((1, 2, 3, 0))
c_v = gd.cell_cv.sum(0) / 2
r_gv -= c_v
lap = Laplace(gd, n=n)
grad_v = [Gradient(gd, v, n=n) for v in range(3)]
assert lap.npoints == D * 2 * n + 1
for m in range(0, 2 * n + 1):
for ix in range(m + 1):
for iy in range(m - ix + 1):
iz = m - ix - iy
a_g = (r_gv**(ix, iy, iz)).prod(3)
if ix + iy + iz == 2 and max(ix, iy, iz) == 2:
r = 2.0
else:
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:]
h = 0.2 # grid spacing
a = h * G # side length of box
M = N // B # number of bands per block
assert M * B == N
D = world.size // B # number of domains
assert D * B == world.size
# Set up communicators:
r = world.rank // D * D
domain_comm = world.new_communicator(np.arange(r, r + D))
band_comm = world.new_communicator(np.arange(world.rank % D, world.size, D))
# Set up grid descriptor:
gd = GridDescriptor((G, G, G), (a, a, a), True, domain_comm, parsize)
# Random wave functions:
np.random.seed(world.rank)
psit_mG = np.random.uniform(-0.5, 0.5, size=(M,) + tuple(gd.n_c))
if world.rank == 0:
print 'Size of wave function array:', psit_mG.shape
# Send and receive buffers:
send_mG = gd.empty(M)
recv_mG = gd.empty(M)
def run():
S_nn = overlap(psit_mG, send_mG, recv_mG)
t1 = time()
def get_dielectric_matrix(self,
xc='RPA',
overwritechi0=False,
symmetric=True,
chi0_wGG=None,
calc=None,
vcut=None,
dir=None):
if self.chi0_wGG is None and chi0_wGG is None:
self.initialize()
self.calculate()
elif self.chi0_wGG is None and chi0_wGG is not None:
#Read from file and reinitialize
self.xc = xc
from gpaw.response.parallel import par_read
self.chi0_wGG = par_read(chi0_wGG, 'chi0_wGG')
self.nvalbands = self.nbands
#self.parallel_init() # parallelization not yet implemented
self.Nw_local = self.Nw # parallelization not yet implemented
if self.calc is None:
from gpaw import GPAW
self.calc = GPAW(calc,txt=None)
if self.xc == 'ALDA' or self.xc == 'ALDA_X':
from gpaw.response.kernel import calculate_Kxc
from gpaw.grid_descriptor import GridDescriptor
from gpaw.mpi import world, rank, size, serial_comm
self.pbc = self.calc.atoms.pbc
self.gd = GridDescriptor(self.calc.wfs.gd.N_c*self.rpad, self.acell_cv,
pbc_c=True, comm=serial_comm)
R_av = self.calc.atoms.positions / Bohr
nt_sg = self.calc.density.nt_sG
if (self.rpad > 1).any() or (self.pbc - True).any():
nt_sG = self.gd.zeros(self.nspins)
#nt_sG = np.zeros([self.nspins, self.nG[0], self.nG[1], self.nG[2]])
for s in range(self.nspins):
nt_G = self.pad(nt_sg[s])
nt_sG[s] = nt_G
else:
nt_sG = nt_sg
self.Kxc_sGG = calculate_Kxc(self.gd,
nt_sG,
self.npw, self.Gvec_Gc,
self.gd.N_c, self.vol,
self.bcell_cv, R_av,
self.calc.wfs.setups,
self.calc.density.D_asp,
functional=self.xc,
density_cut=self.density_cut)
if overwritechi0:
dm_wGG = self.chi0_wGG
else:
dm_wGG = np.zeros_like(self.chi0_wGG)
if dir is None:
q_c = self.q_c
else:
q_c = np.diag((1,1,1))[dir] * self.qopt
self.chi0_wGG[:,0,:] = self.chi00G_wGv[:,:,dir]
self.chi0_wGG[:,:,0] = self.chi0G0_wGv[:,:,dir]
from gpaw.response.kernel import calculate_Kc, CoulombKernel
kernel = CoulombKernel(vcut=self.vcut,
pbc=self.calc.atoms.pbc,
cell=self.acell_cv)
self.Kc_GG = kernel.calculate_Kc(q_c,
self.Gvec_Gc,
self.bcell_cv,
symmetric=symmetric)
#self.Kc_GG = calculate_Kc(q_c,
# self.Gvec_Gc,
# self.acell_cv,
# self.bcell_cv,
# self.pbc,
# self.vcut,
# symmetric=symmetric)
tmp_GG = np.eye(self.npw, self.npw)
if xc == 'RPA':
self.printtxt('Use RPA.')
for iw in range(self.Nw_local):
dm_wGG[iw] = tmp_GG - self.Kc_GG * self.chi0_wGG[iw]
elif xc == 'ALDA':
self.printtxt('Use ALDA kernel.')
# E_LDA = 1 - v_c chi0 (1-fxc chi0)^-1
# http://prb.aps.org/pdf/PRB/v33/i10/p7017_1 eq. 4
A_wGG = self.chi0_wGG.copy()
for iw in range(self.Nw_local):
A_wGG[iw] = np.dot(self.chi0_wGG[iw], np.linalg.inv(tmp_GG - np.dot(self.Kxc_sGG[0], self.chi0_wGG[iw])))
for iw in range(self.Nw_local):
dm_wGG[iw] = tmp_GG - self.Kc_GG * A_wGG[iw]
return dm_wGG
from time import time
from gpaw.grid_descriptor import GridDescriptor
from gpaw.fd_operators import Laplace
import gpaw.mpi as mpi
n = 96
h = 0.1
L = n * h
gd = GridDescriptor((n, n, n), [L, L, L])
# Allocate arrays:
a = gd.zeros(100) + 1.2
b = gd.empty(100)
o = Laplace(gd, 2).apply
t0 = time()
for r in range(10):
o(a, b)
if mpi.rank == 0:
print time() - t0, a.shape
