def test_overlap_inverse_before(self):
kpt = self.wfs.kpt_u[0]
kpt.P_ani = self.pt.dict(self.bd.mynbands)
ppo = ProjectorPairOverlap(self.wfs, self.atoms)
# Compare fingerprints across all processors
fingerprint = np.array([md5_array(ppo.B_aa, numeric=True)])
fingerprints = np.empty(world.size, np.int64)
world.all_gather(fingerprint, fingerprints)
if fingerprints.ptp(0).any():
raise RuntimeError('Distributed matrices are not identical!')
work_nG = np.empty_like(self.psit_nG)
self.pt.integrate(self.psit_nG, kpt.P_ani, kpt.q)
P0_ani = dict([(a,P_ni.copy()) for a,P_ni in kpt.P_ani.items()])
ppo.apply_inverse(self.psit_nG, work_nG, self.wfs, kpt, calculate_P_ani=False)
res_nG = np.empty_like(self.psit_nG)
ppo.apply(work_nG, res_nG, self.wfs, kpt, calculate_P_ani=True)
del work_nG
P_ani = self.pt.dict(self.bd.mynbands)
self.pt.integrate(res_nG, P_ani, kpt.q)
abserr = np.empty(1, dtype=float)
for n in range(self.nbands):
band_rank, myn = self.bd.who_has(n)
if band_rank == self.bd.comm.rank:
abserr[:] = np.abs(self.psit_nG[myn] - res_nG[myn]).max()
self.gd.comm.max(abserr)
self.bd.comm.broadcast(abserr, band_rank)
self.assertAlmostEqual(abserr.item(), 0, 10)
self.check_and_plot(P_ani, P0_ani, 10, 'overlap,inverse,before')
def test_extrapolate_inverse(self):
kpt = self.wfs.kpt_u[0]
ppo = ProjectorPairOverlap(self.wfs, self.atoms)
# Compare fingerprints across all processors
fingerprint = np.array([md5_array(ppo.B_aa, numeric=True)])
fingerprints = np.empty(world.size, np.int64)
world.all_gather(fingerprint, fingerprints)
if fingerprints.ptp(0).any():
raise RuntimeError('Distributed matrices are not identical!')
work_nG = np.empty_like(self.psit_nG)
P_ani = ppo.apply_inverse(self.psit_nG, work_nG, self.wfs, kpt, \
calculate_P_ani=True, extrapolate_P_ani=True)
P0_ani = self.pt.dict(self.bd.mynbands)
self.pt.integrate(work_nG, P0_ani, kpt.q)
del work_nG
self.check_and_plot(P_ani, P0_ani, 11, 'extrapolate,inverse')
def test_extrapolate_inverse(self):
kpt = self.wfs.kpt_u[0]
ppo = ProjectorPairOverlap(self.wfs, self.atoms)
# Compare fingerprints across all processors
fingerprint = np.array([md5_array(ppo.B_aa, numeric=True)])
fingerprints = np.empty(world.size, np.int64)
world.all_gather(fingerprint, fingerprints)
if fingerprints.ptp(0).any():
raise RuntimeError('Distributed matrices are not identical!')
work_nG = np.empty_like(self.psit_nG)
P_ani = ppo.apply_inverse(self.psit_nG, work_nG, self.wfs, kpt, \
calculate_P_ani=True, extrapolate_P_ani=True)
P0_ani = self.pt.dict(self.bd.mynbands)
self.pt.integrate(work_nG, P0_ani, kpt.q)
del work_nG
self.check_and_plot(P_ani, P0_ani, 11, 'extrapolate,inverse')
def test_overlap_inverse_before(self):
kpt = self.wfs.kpt_u[0]
kpt.P_ani = self.pt.dict(self.bd.mynbands)
ppo = ProjectorPairOverlap(self.wfs, self.atoms)
# Compare fingerprints across all processors
fingerprint = np.array([md5_array(ppo.B_aa, numeric=True)])
fingerprints = np.empty(world.size, np.int64)
world.all_gather(fingerprint, fingerprints)
if fingerprints.ptp(0).any():
raise RuntimeError('Distributed matrices are not identical!')
work_nG = np.empty_like(self.psit_nG)
self.pt.integrate(self.psit_nG, kpt.P_ani, kpt.q)
P0_ani = dict([(a,P_ni.copy()) for a,P_ni in kpt.P_ani.items()])
ppo.apply_inverse(self.psit_nG, work_nG, self.wfs, kpt, calculate_P_ani=False)
res_nG = np.empty_like(self.psit_nG)
ppo.apply(work_nG, res_nG, self.wfs, kpt, calculate_P_ani=True)
del work_nG
P_ani = self.pt.dict(self.bd.mynbands)
self.pt.integrate(res_nG, P_ani, kpt.q)
abserr = np.empty(1, dtype=float)
for n in range(self.nbands):
band_rank, myn = self.bd.who_has(n)
if band_rank == self.bd.comm.rank:
abserr[:] = np.abs(self.psit_nG[myn] - res_nG[myn]).max()
self.gd.comm.max(abserr)
self.bd.comm.broadcast(abserr, band_rank)
self.assertAlmostEqual(abserr.item(), 0, 10)
self.check_and_plot(P_ani, P0_ani, 10, 'overlap,inverse,before')
