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485 lines
18 KiB
Python
485 lines
18 KiB
Python
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################################################################################
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#
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# TRIQS: a Toolbox for Research in Interacting Quantum Systems
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#
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# Copyright (C) 2011 by M. Ferrero, O. Parcollet
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#
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# DFT tools: Copyright (C) 2011 by M. Aichhorn, L. Pourovskii, V. Vildosola
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#
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# PLOVasp: Copyright (C) 2015 by O. E. Peil
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#
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# TRIQS is free software: you can redistribute it and/or modify it under the
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# terms of the GNU General Public License as published by the Free Software
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# Foundation, either version 3 of the License, or (at your option) any later
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# version.
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#
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# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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# details.
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#
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# You should have received a copy of the GNU General Public License along with
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# TRIQS. If not, see <http://www.gnu.org/licenses/>.
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#
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################################################################################
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r"""
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plovasp.proj_group
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==================
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Storage and manipulation of projector groups.
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"""
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import numpy as np
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from .proj_shell import ComplementShell
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np.set_printoptions(suppress=True)
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################################################################################
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################################################################################
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#
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# class ProjectorGroup
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#
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################################################################################
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################################################################################
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class ProjectorGroup:
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"""
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Container of projectors defined within a certain energy window.
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The constructor selects a subset of projectors according to
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the parameters from the config-file (passed in `pars`).
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Parameters:
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- gr_pars (dict) : group parameters from the config-file
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- shells ([ProjectorShell]) : array of ProjectorShell objects
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- eigvals (numpy.array) : array of KS eigenvalues
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"""
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def __init__(self, gr_pars, shells, eigvals):
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"""
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Constructor
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"""
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self.emin, self.emax = gr_pars['ewindow']
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self.ishells = gr_pars['shells']
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self.ortho = gr_pars['normalize']
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self.normion = gr_pars['normion']
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self.complement = gr_pars['complement']
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self.shells = shells
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# Determine the minimum and maximum band numbers
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if 'bands' in gr_pars:
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nk, nband, ns_band = eigvals.shape
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ib_win = np.zeros((nk, ns_band, 2), dtype=int)
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ib_win[:,:,0] = gr_pars['bands'][0]-1
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ib_win[:,:,1] = gr_pars['bands'][1]-1
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ib_min = gr_pars['bands'][0] - 1
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ib_max = gr_pars['bands'][1] - 1
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else:
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ib_win, ib_min, ib_max = self.select_bands(eigvals)
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self.ib_win = ib_win
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self.ib_min = ib_min
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self.ib_max = ib_max
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self.nb_max = ib_max - ib_min + 1
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if self.complement:
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n_bands = self.ib_win[:,:,1] - self.ib_win[:,:,0]+1
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n_orbs = sum([x.ndim for x in self.shells])
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assert np.all( n_bands == n_bands[0,0] ), "At each band the same number of bands has to be selected for calculating the complement (to end up with an equal number of orbitals at each k-point)."
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if n_orbs == n_bands[0,0]:
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self.complement = False
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print("\nWARNING: The total number of orbitals in this group is ")
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print("equal to the number of bands. Setting COMPLEMENT to FALSE!\n")
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# Select projectors within the energy window
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for ish in self.ishells:
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shell = self.shells[ish]
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shell.select_projectors(ib_win, ib_min, ib_max)
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################################################################################
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#
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# nelect_window
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#
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################################################################################
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def nelect_window(self, el_struct):
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"""
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Determines the total number of electrons within the window.
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"""
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self.nelect = 0
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nk, ns_band, _ = self.ib_win.shape
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rspin = 2.0 if (ns_band == 1 and el_struct.nc_flag == False) else 1.0
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for isp in range(ns_band):
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for ik in range(nk):
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ib1 = self.ib_win[ik, isp, 0]
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ib2 = self.ib_win[ik, isp, 1]+1
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occ = el_struct.ferw[isp, ik, ib1:ib2]
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kwght = el_struct.kmesh['kweights'][ik]
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self.nelect += occ.sum() * kwght * rspin
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return self.nelect
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################################################################################
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#
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# orthogonalize
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#
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################################################################################
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def orthogonalize(self):
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"""
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Orthogonalize a group of projectors.
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There are two options for orthogonalizing projectors:
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1. one ensures orthogonality on each site (NORMION = True);
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2. one ensures orthogonality for subsets of sites (NORMION = False),
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as, e.g., in cluster calculations.
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In order to handle various cases the strategy is first to build a
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mapping that selects appropriate blocks of raw projectors, forms a
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matrix consisting of these blocks, orthogonalize the matrix, and use
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the mapping again to write the orthogonalized projectors back to the
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projector arrays. Note that the blocks can comprise several projector arrays
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contained in different projector shells.
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The construction of block maps is performed in 'self.get_block_matrix_map()'.
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"""
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# Quick exit if no normalization is requested
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if not self.ortho:
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return
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block_maps, ndim = self.get_block_matrix_map()
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_, ns, nk, _, _ = self.shells[0].proj_win.shape
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p_mat = np.zeros((ndim, self.nb_max), dtype=complex)
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# Note that 'ns' and 'nk' are the same for all shells
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for isp in range(ns):
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for ik in range(nk):
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nb = self.ib_win[ik, isp, 1] - self.ib_win[ik, isp, 0] + 1
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# Combine all projectors of the group to one block projector
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for bl_map in block_maps:
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p_mat[:, :] = 0.0j # !!! Clean-up from the last k-point and block!
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for ibl, block in enumerate(bl_map):
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i1, i2 = block['bmat_range']
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ish, ion = block['shell_ion']
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nlm = i2 - i1 + 1
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shell = self.shells[ish]
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p_mat[i1:i2, :nb] = shell.proj_win[ion, isp, ik, :nlm, :nb]
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# Now orthogonalize the obtained block projector
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ibl_max = i2
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p_orth, overl, eig = self.orthogonalize_projector_matrix(p_mat[:ibl_max, :nb])
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# Distribute projectors back using the same mapping
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for ibl, block in enumerate(bl_map):
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i1, i2 = block['bmat_range']
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ish, ion = block['shell_ion']
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nlm = i2 - i1 + 1
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shell = self.shells[ish]
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shell.proj_win[ion, isp, ik, :nlm, :nb] = p_orth[i1:i2, :nb]
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################################################################################
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#
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# calc_hk
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#
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################################################################################
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def calc_hk(self, eigvals):
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"""
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Calculate H(k) for a group by applying the projectors P
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to the eigenvalues eps.
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H_ij(k) = sum_l P*_il eps_l P_lj
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"""
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# here we abuse the get_block_matrix_map(), however, it only works
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# if self.normion is false
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temp = self.normion
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self.normion = False
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block_maps, ndim = self.get_block_matrix_map()
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self.normion = temp
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_, ns, nk, _, _ = self.shells[0].proj_win.shape
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self.hk = np.zeros((ns,nk,ndim,ndim), dtype=complex)
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# Note that 'ns' and 'nk' are the same for all shells
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for isp in range(ns):
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for ik in range(nk):
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bmin = self.ib_win[ik, isp, 0]
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bmax = self.ib_win[ik, isp, 1]+1
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nb = bmax - bmin
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p_mat = np.zeros((ndim, nb), dtype=complex)
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#print(bmin,bmax,nb)
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# Combine all projectors of the group to one block projector
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for bl_map in block_maps:
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p_mat[:, :] = 0.0j # !!! Clean-up from the last k-point and block!
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for ibl, block in enumerate(bl_map):
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i1, i2 = block['bmat_range']
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ish, ion = block['shell_ion']
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nlm = i2 - i1 + 1
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shell = self.shells[ish]
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p_mat[i1:i2, :nb] = shell.proj_win[ion, isp, ik, :nlm, :nb]
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self.hk[isp,ik,:,:] = np.dot(p_mat*eigvals[ik,bmin:bmax,isp],
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p_mat.transpose().conjugate())
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################################################################################
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#
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# complement
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#
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################################################################################
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def calc_complement(self,eigvals):
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"""
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Calculate the complement for a group of projectors.
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This leads to quadtratic projectors P = <l|n> by using a Gram-Schmidt.
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The projector on the orthogonal complement of the existing projectors
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|l> is P^u = 1 - sum_l |l><l|
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We get candidates for complement projectors by applying P^u to a Bloch
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state |n>: |l*> = P^u |n>. For numerical stability we select that Bloch
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state which leads to the |l*> with the largest norm (that corresponds to
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that Bloch state with the smallest overlap with the space spanned by |l>)
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We normalize |l*> and add it to |l>. We do so untill we have as many
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|l> states as we have |n> states.
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"""
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print('\nCalculating complement\n')
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block_maps, ndim = self.get_block_matrix_map()
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_, ns, nk, _, _ = self.shells[0].proj_win.shape
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p_mat = np.zeros((ndim, self.nb_max), dtype=complex)
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p_full = np.zeros((1,ns,nk,self.nb_max, self.nb_max), dtype=complex)
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# Note that 'ns' and 'nk' are the same for all shells
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for isp in range(ns):
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for ik in range(nk):
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bmin = self.ib_win[ik, isp, 0]
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bmax = self.ib_win[ik, isp, 1]+1
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nb = bmax - bmin
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# Combine all projectors of the group to one block projector
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for bl_map in block_maps:
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p_mat[:, :] = 0.0j # !!! Clean-up from the last k-point and block!
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for ibl, block in enumerate(bl_map):
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i1, i2 = block['bmat_range']
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ish, ion = block['shell_ion']
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nlm = i2 - i1 + 1
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shell = self.shells[ish]
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p_mat[i1:i2, :nb] = shell.proj_win[ion, isp, ik, :nlm, :nb]
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orbs_done = 1*ndim
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p_full[0,isp,ik,:ndim,:] = p_mat
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while orbs_done < self.nb_max:
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#We calculate the overlap of all bloch states: sum_l <n|l><l|m>
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overlap = np.dot(p_full[0,isp,ik,:orbs_done,:].transpose().conjugate(),p_full[0,isp,ik,:orbs_done,:])
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# work is the projector onto the orthogonal complment <n| ( 1 - sum_l |l><l| ) |m>
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work = np.eye(self.nb_max) - overlap
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# calculate the norm of the projected bloch function
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norm = np.sqrt(np.sum(work*work.transpose(),axis=1))
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# select the bloch function leading to the largest norm
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max_ind = np.argmax(norm)
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# normalize and put it to the projectors
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p_full[0,isp,ik,orbs_done,:] = work[:,max_ind].conjugate()/norm[max_ind]
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orbs_done += 1
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sh_pars = {}
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sh_pars['lshell'] = -1
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sh_pars['ions'] = {'nion':1,'ion_list':[[1]]}
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sh_pars['user_index'] = 'complement'
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sh_pars['corr'] = False
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sh_pars['ib_min'] = bmin
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sh_pars['ib_max'] = bmax
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sh_pars['ib_win'] = self.ib_win
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self.shells.append(ComplementShell(sh_pars,p_full[:,:,:,ndim:,:],False))
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self.ishells.append(self.ishells[-1]+1)
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################################################################################
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#
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# gen_block_matrix_map
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#
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################################################################################
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def get_block_matrix_map(self):
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"""
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Generates a map from a set of projectors belonging to different shells
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and ions onto a set of block projector matrices, each of which is
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orthonormalized.
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Returns the map and the maximum orbital dimension of the block projector
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matrix.
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Mapping is defined as a list of 'block_maps' corresponding to subsets
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of projectors to be orthogonalized. Each subset corresponds to a subset of sites
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and spans all orbital indices. defined by 'bl_map' as
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bl_map = [((i1_start, i1_end), (i1_shell, i1_ion)),
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((i2_start, i2_end), (i2_shell, i2_ion)),
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...],
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where `iX_start`, `iX_end` is the range of indices of the block matrix
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(in Python convention `iX_end = iX_last + 1`, with `iX_last` being the last index
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of the range),
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`iX_shell` and `iX_ion` the shell and site indices. The length of the range
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should be consistent with 'nlm' dimensions of a corresponding shell, i.e.,
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`iX_end - iX_start = nlm[iX_shell]`.
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Consider particular cases:
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1. Orthogonality is ensured on each site (NORMION = True).
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For each site 'ion' we have the following mapping:
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block_maps = [bl_map[ion] for ion in range(shell.nion)
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for shell in shells]
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bl_map = [((i1_start, i1_end), (i1_shell, ion)),
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((i2_start, i2_end), (i2_shell, ion)),
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...],
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2. Orthogonality is ensured on all sites within the group (NORMION = False).
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The mapping:
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block_maps = [bl_map]
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bl_map = [((i1_start, i1_end), (i1_shell, i1_shell.ion1)),
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((i1_start, i1_end), (i1_shell, i1_shell.ion2)),
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...
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((i2_start, i2_end), (i2_shell, i2_shell.ion1)),
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((i2_start, i2_end), (i2_shell, i2_shell.ion2)),
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...],
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"""
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if self.normion:
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# Projectors for each site are mapped onto a separate block matrix
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block_maps = []
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ndim = 0
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for ish in self.ishells:
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_shell = self.shells[ish]
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nion, ns, nk, nlm, nb_max = _shell.proj_win.shape
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ndim = max(ndim, nlm)
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for ion in range(nion):
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i1_bl = 0
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i2_bl = nlm
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block = {'bmat_range': (i1_bl, i2_bl)}
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block['shell_ion'] = (ish, ion)
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bl_map = [block]
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block_maps.append(bl_map)
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else:
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# All projectors within a group are combined into one big block matrix
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block_maps = []
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bl_map = []
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i1_bl = 0
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for ish in self.ishells:
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_shell = self.shells[ish]
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nion, ns, nk, nlm, nb_max = _shell.proj_win.shape
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for ion in range(nion):
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i2_bl = i1_bl + nlm
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block = {'bmat_range': (i1_bl, i2_bl)}
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block['shell_ion'] = (ish, ion)
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bl_map.append(block)
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i1_bl = i2_bl
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ndim = i2_bl
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block_maps.append(bl_map)
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return block_maps, ndim
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################################################################################
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#
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# orthogonalize_projector_matrix()
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#
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################################################################################
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def orthogonalize_projector_matrix(self, p_matrix):
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"""
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Orthogonalizes a projector defined by a rectangular matrix `p_matrix`.
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Parameters
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----------
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p_matrix (numpy.array[complex]) : matrix `Nm x Nb`, where `Nm` is
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the number of orbitals, `Nb` number of bands
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Returns
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-------
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Orthogonalized projector matrix, initial overlap matrix and its eigenvalues.
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"""
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# TODO: check the precision of the calculations below,
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# it seems to be inferior to that of Fortran implementation
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# Overlap matrix O_{m m'} = \sum_{v} P_{m v} P^{*}_{v m'}
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overlap = np.dot(p_matrix, p_matrix.conj().T)
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# Calculate [O^{-1/2}]_{m m'}
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eig, eigv = np.linalg.eigh(overlap)
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assert np.all(eig > 0.0), ("Negative eigenvalues of the overlap matrix:"
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"projectors are ill-defined")
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sqrt_eig = 1.0 / np.sqrt(eig)
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shalf = np.dot(eigv * sqrt_eig, eigv.conj().T)
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# Apply \tilde{P}_{m v} = \sum_{m'} [O^{-1/2}]_{m m'} P_{m' v}
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p_ortho = np.dot(shalf, p_matrix)
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return (p_ortho, overlap, eig)
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################################################################################
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#
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# select_bands()
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#
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################################################################################
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def select_bands(self, eigvals):
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"""
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Select a subset of bands lying within a given energy window.
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The band energies are assumed to be sorted in an ascending order.
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Parameters
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----------
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eigvals (numpy.array) : all eigenvalues
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emin, emax (float) : energy window
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Returns
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-------
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ib_win, nb_min, nb_max : lowest and highest indices of the selected bands
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"""
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# Sanity check
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if self.emin > eigvals.max() or self.emax < eigvals.min():
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raise Exception("Energy window does not overlap with the band structure")
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nk, nband, ns_band = eigvals.shape
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ib_win = np.zeros((nk, ns_band, 2), dtype=int)
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ib_min = 10000000
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ib_max = 0
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for isp in range(ns_band):
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for ik in range(nk):
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for ib in range(nband):
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en = eigvals[ik, ib, isp]
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if en >= self.emin:
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break
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ib1 = ib
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for ib in range(ib1, nband):
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en = eigvals[ik, ib, isp]
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if en > self.emax:
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break
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else:
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# If we reached the last band add 1 to get the correct bound
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ib += 1
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ib2 = ib - 1
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assert ib1 <= ib2, "No bands inside the window for ik = %s"%(ik)
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ib_win[ik, isp, 0] = ib1
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ib_win[ik, isp, 1] = ib2
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ib_min = min(ib_min, ib1)
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ib_max = max(ib_max, ib2)
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return ib_win, ib_min, ib_max
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