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Refactored mapping onto block matrix in 'orthogonalize()'

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The implementation of the mapping of a set of projectors (belonging
to different shells and ions) onto a block matrix in the
orthogonalization routine has been generalized. Now, an implementation
of the choice between the full orthogoanlization and per-site one
is straightforward: it is just a matter of defining a proper mapping.
The mapping scheme itself is described in the doc-string of method
'ProjectorGroup.orthogonalize()'
This commit is contained in:
Oleg E. Peil 2015-11-19 11:47:59 +01:00
parent 79f4c33458
commit b3e1dd915a
1 changed files with 33 additions and 29 deletions
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62
python/vasp/proj_group.py
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@ -118,7 +118,7 @@ class ProjectorGroup:
((i2_start, i2_end), (i2_shell, ion)), ((i2_start, i2_end), (i2_shell, ion)),
...], ...],
2. Orthogonality is ensured on all sites within the group (NORMION = True). 2. Orthogonality is ensured on all sites within the group (NORMION = False).
The mapping: The mapping:
block_maps = [bl_map] block_maps = [bl_map]
@ -135,24 +135,30 @@ class ProjectorGroup:
if not self.ortho: if not self.ortho:
return return
# TODO: add the case of 'normion = True'
assert not self.normion, "'NORMION = True' is not yet implemented"
# Determine the dimension of the projector matrix # Determine the dimension of the projector matrix
# and map the blocks to the big matrix # and map the blocks to the big matrix
i1_bl = 0 if self.normion:
bl_map = [{} for ish in self.ishells] # TODO: add the case of 'normion = True'
for ish in self.ishells: raise NotImplementedError("'NORMION = True' is not yet implemented")
_shell = self.shells[ish]
nion, ns, nk, nlm, nb_max = _shell.proj_win.shape
bmat_bl = [] # indices corresponding to a big block matrix
for ion in xrange(nion):
i2_bl = i1_bl + nlm
bmat_bl.append((i1_bl, i2_bl))
i1_bl = i2_bl
bl_map[ish]['bmat_blocks'] = bmat_bl
ndim = i2_bl else:
block_maps = []
bl_map = []
i1_bl = 0
for ish in self.ishells:
_shell = self.shells[ish]
nion, ns, nk, nlm, nb_max = _shell.proj_win.shape
for ion in xrange(nion):
i2_bl = i1_bl + nlm
block = {'bmat_range': (i1_bl, i2_bl)}
block['shell_ion'] = (ish, ion)
bl_map.append(block)
i1_bl = i2_bl
ndim = i2_bl
block_maps.append(bl_map)
print block_maps
p_mat = np.zeros((ndim, nb_max), dtype=np.complex128) p_mat = np.zeros((ndim, nb_max), dtype=np.complex128)
# Note that 'ns' and 'nk' are the same for all shells # Note that 'ns' and 'nk' are the same for all shells
for isp in xrange(ns): for isp in xrange(ns):
@ -160,22 +166,20 @@ class ProjectorGroup:
nb = self.ib_win[ik, isp, 1] - self.ib_win[ik, isp, 0] + 1 nb = self.ib_win[ik, isp, 1] - self.ib_win[ik, isp, 0] + 1
# Combine all projectors of the group to one block projector # Combine all projectors of the group to one block projector
p_mat[:, :] = 0.0j # !!! Clean-up from the last k-point! p_mat[:, :] = 0.0j # !!! Clean-up from the last k-point!
for ish in self.ishells: for bl_map in block_maps:
shell = self.shells[ish] for ibl, block in enumerate(bl_map):
blocks = bl_map[ish]['bmat_blocks'] i1, i2 = block['bmat_range']
nion = shell.nion # !!! ish, ion = block['shell_ion']
for ion in xrange(nion): shell = self.shells[ish]
i1, i2 = blocks[ion]
p_mat[i1:i2, :nb] = shell.proj_win[ion, isp, ik, :nlm, :nb] p_mat[i1:i2, :nb] = shell.proj_win[ion, isp, ik, :nlm, :nb]
# Now orthogonalize the obtained block projector # Now orthogonalize the obtained block projector
p_orth, overl, eig = self.orthogonalize_projector_matrix(p_mat) p_orth, overl, eig = self.orthogonalize_projector_matrix(p_mat)
# Distribute back projectors in the same order # Distribute projectors back using the same mapping
for ish in self.ishells: for bl_map in block_maps:
shell = self.shells[ish] for ibl, block in enumerate(bl_map):
blocks = bl_map[ish]['bmat_blocks'] i1, i2 = block['bmat_range']
nion = shell.nion # !!! ish, ion = block['shell_ion']
for ion in xrange(nion): shell = self.shells[ish]
i1, i2 = blocks[ion]
shell.proj_win[ion, isp, ik, :nlm, :nb] = p_orth[i1:i2, :nb] shell.proj_win[ion, isp, ik, :nlm, :nb] = p_orth[i1:i2, :nb]
################################################################################ ################################################################################