mirror of https://github.com/triqs/dft_tools
SumkDFT: analyse_block_structure_from_gf
This commit is contained in:
Gernot J. Kraberger
parent
8ebb0c3c29
commit
25218746f4
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@ -25,12 +25,15 @@ import numpy
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import pytriqs.utility.dichotomy as dichotomy
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from pytriqs.gf import *
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import pytriqs.utility.mpi as mpi
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from pytriqs.utility.comparison_tests import assert_arrays_are_close
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from pytriqs.archive import *
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from symmetry import *
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from block_structure import BlockStructure
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from sets import Set
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from itertools import product
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from warnings import warn
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from scipy import compress
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from scipy.optimize import minimize
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class SumkDFT(object):
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@ -848,6 +851,366 @@ class SumkDFT(object):
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elif (ind1 < 0) and (ind2 < 0):
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self.deg_shells[ish].append([block1, block2])
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def analyse_block_structure_from_gf(self, G, threshold=1.e-5, include_shells=None, analyse_deg_shells = True):
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r"""
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Determines the block structure of local Green's functions by analysing
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the structure of the corresponding non-interacting Green's function.
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The resulting block structures for correlated shells are
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stored in the :class:`SumkDFT.block_structure <dft.block_structure.BlockStructure>`
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attribute.
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This is a safer alternative to analyse_block_structure, because
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the full non-interacting Green's function is taken into account
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and not just the density matrix and Hloc.
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Parameters
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----------
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G : list of BlockGf of GfImFreq or GfImTime
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the non-interacting Green's function for each inequivalent correlated shell
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threshold : real, optional
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If the difference between matrix elements is below threshold,
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they are considered to be equal.
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include_shells : list of integers, optional
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List of correlated shells to be analysed.
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If include_shells is not provided all correlated shells will be analysed.
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analyse_deg_shells : bool
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Whether to call the analyse_deg_shells function
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after having finished the block structure analysis
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Returns
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-------
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G : list of BlockGf of GfImFreq or GfImTime
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the Green's function transformed into the new block structure
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"""
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# make a GfImTime from the supplied G
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if isinstance(G[0]._first(), GfImFreq):
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gf = [BlockGf(name_block_generator = [(name, GfImTime(beta=block.mesh.beta,
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indices=block.indices,n_points=len(block.mesh)+1)) for name, block in g_sh])
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for g_sh in G]
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for ish in range(len(gf)):
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for name, g in gf[ish]:
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g.set_from_inverse_fourier(G[ish][name])
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else:
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assert isinstance(G[0]._first(), GfImTime), "G must be a BlockGf of either GfImFreq or GfImTime"
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gf = G
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# initialize the variables
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self.gf_struct_solver = [{} for ish in range(self.n_inequiv_shells)]
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self.sumk_to_solver = [{} for ish in range(self.n_inequiv_shells)]
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self.solver_to_sumk = [{} for ish in range(self.n_inequiv_shells)]
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self.solver_to_sumk_block = [{}
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for ish in range(self.n_inequiv_shells)]
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# the maximum value of each matrix element of each block and shell
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max_gf = [{name:numpy.max(numpy.abs(g.data),0) for name, g in gf[ish]} for ish in range(self.n_inequiv_shells)]
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if include_shells is None:
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# include all shells
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include_shells = range(self.n_inequiv_shells)
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for ish in include_shells:
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for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
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n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim']
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# gives an index list of entries larger that threshold
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maxgf_bool = (abs(max_gf[ish][sp]) > threshold)
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# Determine off-diagonal entries in upper triangular part of the
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# Green's function
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offdiag = Set([])
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for i in range(n_orb):
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for j in range(i + 1, n_orb):
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if maxgf_bool[i, j]:
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offdiag.add((i, j))
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# Determine the number of non-hybridising blocks in the gf
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blocs = [[i] for i in range(n_orb)]
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while len(offdiag) != 0:
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pair = offdiag.pop()
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for b1, b2 in product(blocs, blocs):
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if (pair[0] in b1) and (pair[1] in b2):
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if blocs.index(b1) != blocs.index(b2): # In separate blocks?
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# Merge two blocks
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b1.extend(blocs.pop(blocs.index(b2)))
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break # Move on to next pair in offdiag
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# Set the gf_struct for the solver accordingly
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num_blocs = len(blocs)
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for i in range(num_blocs):
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blocs[i].sort()
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self.gf_struct_solver[ish].update(
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[('%s_%s' % (sp, i), range(len(blocs[i])))])
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# Construct sumk_to_solver taking (sumk_block, sumk_index) --> (solver_block, solver_inner)
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# and solver_to_sumk taking (solver_block, solver_inner) -->
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# (sumk_block, sumk_index)
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for i in range(num_blocs):
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for j in range(len(blocs[i])):
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block_sumk = sp
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inner_sumk = blocs[i][j]
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block_solv = '%s_%s' % (sp, i)
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inner_solv = j
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self.sumk_to_solver[ish][(block_sumk, inner_sumk)] = (
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block_solv, inner_solv)
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self.solver_to_sumk[ish][(block_solv, inner_solv)] = (
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block_sumk, inner_sumk)
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self.solver_to_sumk_block[ish][block_solv] = block_sumk
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# transform G to the new structure
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full_structure = BlockStructure.full_structure(
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[{sp:range(self.corr_shells[self.inequiv_to_corr[ish]]['dim'])
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for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]}
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for ish in range(self.n_inequiv_shells)],None)
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G_transformed = [
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self.block_structure.convert_gf(G[ish],
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full_structure, ish, beta=G[ish].mesh.beta, show_warnings=threshold)
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for ish in range(self.n_inequiv_shells)]
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if analyse_deg_shells:
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self.analyse_deg_shells(G_transformed, threshold, include_shells)
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return G_transformed
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def analyse_deg_shells(self, G, threshold=1.e-5, include_shells=None):
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r"""
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Determines the degenerate shells of local Green's functions by analysing
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the structure of the corresponding non-interacting Green's function.
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The results are stored in the
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:class:`SumkDFT.block_structure <dft.block_structure.BlockStructure>`
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attribute.
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Parameters
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----------
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G : list of BlockGf of GfImFreq or GfImTime
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the non-interacting Green's function for each inequivalent correlated shell
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threshold : real, optional
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If the difference between matrix elements is below threshold,
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they are considered to be equal.
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include_shells : list of integers, optional
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List of correlated shells to be analysed.
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If include_shells is not provided all correlated shells will be analysed.
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"""
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# initialize
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self.deg_shells = [[] for ish in range(self.n_inequiv_shells)]
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# helper function
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def null(A, eps=1e-15):
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""" Calculate the null-space of matrix A """
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u, s, vh = numpy.linalg.svd(A)
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null_mask = (s <= eps)
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null_space = compress(null_mask, vh, axis=0)
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return null_space.conjugate().transpose()
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# make a GfImTime from the supplied G
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if isinstance(G[0]._first(), GfImFreq):
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gf = [BlockGf(name_block_generator = [(name, GfImTime(beta=block.mesh.beta,
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indices=block.indices,n_points=len(block.mesh)+1)) for name, block in g_sh])
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for g_sh in G]
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for ish in range(len(gf)):
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for name, g in gf[ish]:
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g.set_from_inverse_fourier(G[ish][name])
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else:
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assert isinstance(G[0]._first(), GfImTime), "G must be a BlockGf of either GfImFreq or GfImTime"
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gf = G
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if include_shells is None:
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# include all shells
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include_shells = range(self.n_inequiv_shells)
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# We consider two blocks equal, if their Green's functions obey
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# maybe_conjugate1( v1^dagger G1 v1 ) = maybe_conjugate2( v2^dagger G2 v2 )
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# where maybe_conjugate is a function that conjugates the Green's
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# function if the flag 'conjugate' is set and the v are unitary
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# matrices
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#
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# for each pair of blocks, we check whether there is a transformation
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# maybe_conjugate( T G1 T^dagger ) = G2
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# where our goal is to find T
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# we just try whether there is such a T with and without conjugation
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for ish in include_shells:
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for block1 in self.gf_struct_solver[ish].iterkeys():
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for block2 in self.gf_struct_solver[ish].iterkeys():
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if block1==block2: continue
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# check if the blocks are already present in the deg_shells
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ind1 = -1
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ind2 = -2
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for n, ind in enumerate(self.deg_shells[ish]):
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if block1 in ind:
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ind1 = n
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v1 = ind[block1]
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if block2 in ind:
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ind2 = n
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v2 = ind[block2]
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# if both are already present, go to the next pair of blocks
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if ind1 >= 0 and ind2 >= 0:
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continue
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gf1 = gf[ish][block1]
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gf2 = gf[ish][block2]
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# the two blocks have to have the same shape
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if gf1.target_shape != gf2.target_shape:
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continue
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# Instead of directly comparing the two blocks, we
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# compare its eigenvalues. As G(tau) is Hermitian,
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# they are real and the eigenvector matrix is unitary.
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# Thus, if the eigenvalues are equal we can transform
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# one block to make it equal to the other (at least
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# for tau=0).
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e1 = numpy.linalg.eigvalsh(gf1.data[0])
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e2 = numpy.linalg.eigvalsh(gf2.data[0])
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if numpy.any(abs(e1-e2) > threshold): continue
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for conjugate in [False,True]:
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if conjugate:
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gf2 = gf2.conjugate()
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# we want T gf1 T^dagger = gf2
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# while for a given tau, T could be calculated
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# by diagonalizing gf1 and gf2, this does not
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# work for all taus simultaneously because of
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# numerical imprecisions
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# rather, we rewrite the equation to
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# T gf1 = gf2 T
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# which is the Sylvester equation.
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# For that equation, one can use the Kronecker
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# product to get a linear problem, which consists
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# of finding the null space of M vec T = 0.
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M = numpy.kron(numpy.eye(*gf1.target_shape),gf2.data[0])-numpy.kron(gf1.data[0].transpose(),numpy.eye(*gf1.target_shape))
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N = null(M, threshold)
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# now we get the intersection of the null spaces
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# of all values of tau
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for i in range(1,len(gf1.data)):
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M = numpy.kron(numpy.eye(*gf1.target_shape),gf2.data[i])-numpy.kron(gf1.data[i].transpose(),numpy.eye(*gf1.target_shape))
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# transform M into current null space
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M = numpy.dot(M, N)
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N = numpy.dot(N, null(M, threshold))
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if numpy.size(N) == 0:
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break
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# no intersection of the null spaces -> no symmetry
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if numpy.size(N) == 0: continue
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# reshape N: it then has the indices matrix, matrix, number of basis vectors of the null space
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N = N.reshape(gf1.target_shape[0], gf1.target_shape[1], -1).transpose([1, 0, 2])
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"""
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any matrix in the null space can now be constructed as
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M = 0
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for i in range(N.shape[-1]):
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M += y[i]*N[:,:,i]
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with coefficients (complex numbers) y[i].
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We want to get a set of coefficients y so that M is unitary.
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Unitary means M M^dagger = 1.
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Thus,
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sum y[i] N[:,:,i] y[j].conjugate() N[:,:,j].conjugate().transpose() = eye.
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The object N[:,:,i] N[:,:,j] is a four-index object which we call Z.
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"""
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Z = numpy.einsum('aci,bcj->abij', N, N.conjugate())
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"""
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function chi2
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This function takes a real parameter vector y and reinterprets it as complex.
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Then, it calculates the chi2 of
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sum y[i] N[:,:,i] y[j].conjugate() N[:,:,j].conjugate().transpose() - eye.
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"""
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def chi2(y):
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# reinterpret y as complex number
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y = y.view(numpy.complex_)
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ret = 0.0
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for a in range(Z.shape[0]):
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for b in range(Z.shape[1]):
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ret += numpy.abs(numpy.dot(y, numpy.dot(Z[a, b], y.conjugate()))
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- (1.0 if a == b else 0.0))**2
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return ret
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# use the minimization routine from scipy
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res = minimize(chi2, numpy.ones(2 * N.shape[-1]))
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# if the minimization fails, there is probably no symmetry
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if not res.success: continue
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# check if the minimization returned zero within the tolerance
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if res.fun > threshold: continue
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# reinterpret the solution as a complex number
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y = res.x.view(numpy.complex_)
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# reconstruct the T matrix
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T = numpy.zeros(N.shape[:-1], dtype=numpy.complex_)
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for i in range(len(y)):
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T += N[:, :, i] * y[i]
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# transform gf1 using T
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G_transformed = gf1.copy()
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G_transformed.from_L_G_R(T, gf1, T.conjugate().transpose())
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# it does not make sense to check the tails for an
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# absolute error because it will usually not hold;
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# we could just check the relative error
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# (here, we ignore it, reasoning that if the data
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# is the same, the tails have to coincide as well)
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try:
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assert_arrays_are_close(G_transformed.data, gf2.data, threshold)
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except (RuntimeError, AssertionError):
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# the symmetry does not hold
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continue
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# Now that we have found a valid T, we have to
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# rewrite it to match the convention that
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# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2),
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# where C conjugates if the flag is True
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# For each group of degenerate shells, the list
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# SK.deg_shells[ish] contains a dict. The keys
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# of the dict are the block names, the values
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# are tuples. The first entry of the tuple is
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# the transformation matrix v, the second entry
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# is the conjugation flag
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# the second block is already present
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# set v1 and C1 so that they are compatible with
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# C(T gf1 T^dagger) = gf2
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# and with
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# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
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if (ind1 < 0) and (ind2 >= 0):
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if conjugate:
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self.deg_shells[ish][ind2][block1] = numpy.dot(T.conjugate().transpose(), v2[0].conjugate()), not v2[1]
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else:
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self.deg_shells[ish][ind2][block1] = numpy.dot(T.conjugate().transpose(), v2[0]), v2[1]
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# the first block is already present
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# set v2 and C2 so that they are compatible with
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# C(T gf1 T^dagger) = gf2
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# and with
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# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
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elif (ind1 >= 0) and (ind2 < 0):
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if conjugate:
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self.deg_shells[ish][ind1][block2] = numpy.dot(T.conjugate(), v1[0].conjugate()), not v1[1]
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else:
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self.deg_shells[ish][ind1][block2] = numpy.dot(T, v1[0]), v1[1]
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# the blocks are not already present
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# we arbitrarily choose v1=eye and C1=False and
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# set v2 and C2 so that they are compatible with
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# C(T gf1 T^dagger) = gf2
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# and with
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# C1(v1^dagger G1 v1) = C2(v2^dagger G2 v2)
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elif (ind1 < 0) and (ind2 < 0):
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d = dict()
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d[block1] = numpy.eye(*gf1.target_shape), False
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if conjugate:
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d[block2] = T.conjugate(), True
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else:
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d[block2] = T, False
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self.deg_shells[ish].append(d)
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def density_matrix(self, method='using_gf', beta=40.0):
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"""Calculate density matrices in one of two ways.
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@ -1616,3 +1979,38 @@ class SumkDFT(object):
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def __set_deg_shells(self,value):
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self.block_structure.deg_shells = value
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deg_shells = property(__get_deg_shells,__set_deg_shells)
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# a helper function
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def conjugate_in_tau(gf_im_freq, in_place=False):
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""" Calculate the conjugate in tau of a GfImFreq
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Parameters
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----------
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gf_im_freq : GfImFreq of BlockGf
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the Green's function
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in_place : whether to modify the gf_im_freq object (True) or return a copy (False)
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Returns
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-------
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ret : GfImFreq of BlockGf
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the Green's function that has been FT to G(tau), conjugated, and
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FT back
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"""
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if in_place:
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ret = gf_im_freq
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else:
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ret = gf_im_freq.copy()
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if isinstance(ret, BlockGf):
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for name, gf in ret:
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conjugate_in_tau(gf, in_place=True)
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else:
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""" there is an easier way to do this, namely to make
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ret.data[:,:,:] = gf_im_freq.data[::-1,:,:].conjugate()
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ret.tail.data[:,:,:] = gf_im_freq.tail.data.conjugate()
|
||||
but this relies on symmetric Matsubara meshes and is maybe
|
||||
not safe enough"""
|
||||
G_tau = GfImTime(beta=gf_im_freq.mesh.beta,
|
||||
indices=gf_im_freq.indices,n_points=len(gf_im_freq.mesh)+1)
|
||||
G_tau.set_from_inverse_fourier(gf_im_freq)
|
||||
ret.set_from_fourier(G_tau.conjugate())
|
||||
return ret
|
||||
|
|
|
|||
Loading…
Reference in New Issue