def __init__(self, ecut, diagksl, orthoksl, initksl, gd, nvalence, setups,
bd, world, kd, timer):
self.ecut = ecut / units.Hartree
# Set dtype=complex and gamma=False:
kd.gamma = False
FDPWWaveFunctions.__init__(self, diagksl, orthoksl, initksl, gd,
nvalence, setups, bd, complex, world, kd,
timer)
orthoksl.gd = self.pd
self.matrixoperator = MatrixOperator(orthoksl)
self.wd = self.pd
def __init__(self, stencil, diagksl, orthoksl, initksl, gd, nvalence,
setups, bd, dtype, world, kd, kptband_comm, timer):
FDPWWaveFunctions.__init__(self, diagksl, orthoksl, initksl, gd,
nvalence, setups, bd, dtype, world, kd,
kptband_comm, timer)
# Kinetic energy operator:
self.kin = Laplace(self.gd, -0.5, stencil, self.dtype)
self.matrixoperator = MatrixOperator(self.orthoksl)
self.taugrad_v = None # initialized by MGGA functional
def __init__(self, ecut, fftwflags,
diagksl, orthoksl, initksl,
gd, nvalence, setups, bd, dtype,
world, kd, kptband_comm, timer):
self.ecut = ecut
self.fftwflags = fftwflags
self.ng_k = None # number of G-vectors for all IBZ k-points
FDPWWaveFunctions.__init__(self, diagksl, orthoksl, initksl,
gd, nvalence, setups, bd, dtype,
world, kd, kptband_comm, timer)
self.orthoksl.gd = self.pd
self.matrixoperator = MatrixOperator(self.orthoksl)
def run(psit_mG):
overlap = MatrixOperator(ksl, K)
if 0:
overlap.work1_xG = work1_xG
overlap.work2_xG = work2_xG
#S_nn = np.empty((N, N))
def S(x):
return x
dS_aii = {0: np.ones((2, 2)) * 0.123, 1: np.ones((3, 3)) * 0.321}
def dS(a, P_ni):
return np.dot(P_ni, dS_aii[a])
S_nn = overlap.calculate_matrix_elements(psit_mG, P_ani, S, dS)
t1 = time()
if world.rank == 0:
print(S_nn.round(5))
inverse_cholesky(S_nn)
C_nn = S_nn
t2 = time()
if world.rank == 0:
print('Cholesky Time %f' % (t2 - t1))
# Distribute matrix:
world.broadcast(C_nn, 0)
psit_mG = overlap.matrix_multiply(C_nn, psit_mG, P_ani)
if world.rank == 0:
print('Made it past matrix multiply')
# Check:
S_nn = overlap.calculate_matrix_elements(psit_mG, P_ani, S, dS)
assert not (P_ani[0] - psit_mG[:, :2, 0, 0]).round(10).any()
assert not (P_ani[1] - psit_mG[:, -1, -1, -3:]).round(10).any()
if world.rank == 0:
for n in range(N):
assert abs(S_nn[n, n] - 1.0) < 1e-10
assert not S_nn[n + 1:, n].round(10).any()
return psit_mG
def run(psit_mG):
overlap = MatrixOperator(ksl, J)
def H(psit_xG):
Htpsit_xG = np.empty_like(psit_xG)
kin(psit_xG, Htpsit_xG)
for psit_G, y_G in zip(psit_xG, Htpsit_xG):
y_G += vt_G * psit_G
return Htpsit_xG
dH_aii = {0: np.ones((2, 2)) * 0.123, 1: np.ones((3, 3)) * 0.321}
def dH(a, P_ni):
return np.dot(P_ni, dH_aii[a])
H_nn = overlap.calculate_matrix_elements(psit_mG, P_ani, H, dH)
t1 = time()
if world.rank == 0:
eps_n, H_nn = np.linalg.eigh(H_nn)
H_nn = np.ascontiguousarray(H_nn.T)
t2 = time()
if world.rank == 0:
print('Diagonalization Time %f' % (t2 - t1))
print(eps_n)
# Distribute matrix:
world.broadcast(H_nn, 0)
psit_mG = overlap.matrix_multiply(H_nn, psit_mG, P_ani)
if world.rank == 0:
print('Made it past matrix multiply')
# Check:
assert not (P_ani[0] - psit_mG[:, :2, 0, 0]).round(10).any()
assert not (P_ani[1] - psit_mG[:, -1, -1, -3:]).round(10).any()
H_nn = overlap.calculate_matrix_elements(psit_mG, P_ani, H, dH)
if world.rank == 0:
for n in range(N):
assert abs(H_nn[n, n] - eps_n[n]) < 2e-8
assert not H_nn[n + 1:, n].round(8).any()
return psit_mG
def __init__(self,
stencil,
diagksl,
orthoksl,
initksl,
gd,
nvalence,
setups,
bd,
dtype,
world,
kd,
timer=None):
FDPWWaveFunctions.__init__(self, diagksl, orthoksl, initksl, gd,
nvalence, setups, bd, dtype, world, kd,
timer)
self.wd = self.gd # wave function descriptor
# Kinetic energy operator:
self.kin = Laplace(self.gd, -0.5, stencil, self.dtype, allocate=False)
self.matrixoperator = MatrixOperator(orthoksl)
def dscf_kpoint_overlaps(paw, phasemod=True, broadcast=True):
bd = paw.wfs.bd
gd = paw.wfs.gd
kd = paw.wfs.kd
operator = MatrixOperator(paw.wfs.orthoksl, hermitian=False)
atoms = paw.get_atoms()
# Find the kpoint with lowest kpt.k_c (closest to gamma point)
k0 = np.argmin(np.sum(paw.wfs.kd.ibzk_kc**2,axis=1)**0.5)
# Maintain list of a single global reference kpoint for each spin
kpt0_s = []
for s0 in range(kd.nspins):
q0 = k0 - kd.get_offset() % kd.nibzkpts
kpt0 = GlobalKPoint(None, s0, k0, q0, None)
kpt0.update(paw.wfs)
kpt0_s.append(kpt0)
if phasemod:
# Scaled grid point positions used for exponential with ibzk_kc
# cf. wavefunctions.py lines 90-91 rev 4500(ca)
# phase_cd = np.exp(2j * np.pi * sdisp_cd * ibzk_kc[k, :, np.newaxis])
r_cG = gd.empty(3)
for c, r_G in enumerate(r_cG):
slice_c2G = [np.newaxis, np.newaxis, np.newaxis]
slice_c2G[c] = slice(None) #this means ':'
r_G[:] = np.arange(gd.beg_c[c], gd.end_c[c], \
dtype=float)[slice_c2G] / gd.N_c[c]
X_unn = np.empty((kd.mynks, bd.nbands, bd.nbands), dtype=paw.wfs.dtype)
for myu, kpt in enumerate(paw.wfs.kpt_u):
u = kd.global_index(myu)
s, k = kd.what_is(u)
kpt0 = kpt0_s[s]
X_nn = X_unn[myu]
if phasemod:
assert paw.wfs.dtype == complex, 'Phase modification is complex!'
k0_c = kd.ibzk_kc[k0]
k_c = kd.ibzk_kc[k]
eirk_G = np.exp(2j*np.pi*np.sum(r_cG*(k_c-k0_c)[:,np.newaxis,np.newaxis,np.newaxis], axis=0))
psit0_nG = eirk_G[np.newaxis,...]*kpt0.psit_nG
P0_ani = paw.wfs.pt.dict(bd.mynbands)
spos_ac = atoms.get_scaled_positions() % 1.0
for a, P0_ni in P0_ani.items():
# Expanding the exponential exp(ikr)=exp(ikR)*exp(ik(r-R))
# and neglecting the changed P_ani integral exp(ik(r-R))~1
P0_ni[:] = np.exp(2j*np.pi*np.sum(spos_ac[a]*(k_c-k0_c), axis=0)) * kpt0.P_ani[a]
## NB: No exp(ikr) approximate here, but has a parallelization bug
#kpt0_rank, myu0 = kd.get_rank_and_index(kpt0.s, kpt0.k)
#if kd.comm.rank == kpt0_rank:
# paw.wfs.pt.integrate(psit0_nG, P0_ani, kpt0.q)
#for a, P0_ni in P0_ani.items():
# kd.comm.broadcast(P0_ni, kpt0_rank)
else:
psit0_nG = kpt0.psit_nG
P0_ani = kpt0.P_ani
"""
if paw.wfs.world.size == 1:
for n, psit_G in enumerate(kpt.psit_nG):
for n0, psit0_G in enumerate(psit0_nG):
X_nn[n,n0] = np.vdot(psit_G, psit0_G)*gd.dv
for a in range(len(paw.get_atoms())):
P_ni, P0_ni, dO_ii = kpt.P_ani[a], P0_ani[a], paw.wfs.setups[a].dO_ii
for n, P_i in enumerate(P_ni):
for n0, P0_i in enumerate(P0_ni):
X_nn[n,n0] += np.vdot(P_i, np.dot(dO_ii, P0_i))
"""
X = lambda psit_nG, g=SliceGen(psit0_nG, operator): next(g)
dX = lambda a, P_ni: np.dot(P0_ani[a], paw.wfs.setups[a].dO_ii)
X_nn[:] = operator.calculate_matrix_elements(kpt.psit_nG, kpt.P_ani, X, dX).T
if broadcast:
if bd.comm.rank == 0:
gd.comm.broadcast(X_unn, 0)
bd.comm.broadcast(X_unn, 0)
return kpt0_s, X_unn
