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https://github.com/triqs/dft_tools
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Implement calculate_diagonalization_matrix method into SK
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@ -1334,6 +1334,80 @@ class SumkDFT(object):
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# a block was found, break out of the loop
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# a block was found, break out of the loop
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break
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break
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def calculate_diagonalization_matrix(self, prop_to_be_diagonal='eal', calc_in_solver_blocks=False, write_to_blockstructure = True, ish=0):
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"""
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Calculates the diagonalisation matrix, and (optionally) stores it in the BlockStructure.
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Parameters
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----------
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prop_to_be_diagonal : string, optional
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Defines the property to be diagonalized.
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- 'eal' : local hamiltonian (i.e. crystal field)
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- 'dm' : local density matrix
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calc_in_solver_blocks : bool, optional
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Whether the property shall be diagonalized in the
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full sumk structure, or just in the solver structure.
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write_to_blockstructure : bool, optional
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Whether the diagonalization matrix shall be written to
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the BlockStructure directly.
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ish : int, optional
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Number of the correlated shell to be diagonalized.
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Returns
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-------
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trafo : dict
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The transformation matrix for each spin-block in the correlated shell
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"""
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trafo = {}
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if prop_to_be_diagonal == 'eal':
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prop = self.eff_atomic_levels()[ish]
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elif prop_to_be_diagonal == 'dm':
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prop = self.density_matrix(method='using_point_integration')[ish]
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else:
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mpi.report(
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"calculate_diagonalization_matrix: not a valid quantitiy to be diagonal. Choices are 'eal' or 'dm'.")
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return 0
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if calc_in_solver_blocks:
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trafo_tmp = self.block_structure.transformation
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self.block_structure.transformation = None
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prop_solver = self.block_structure.convert_matrix(prop, space_from='sumk', space_to='solver')
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t= {}
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for name in prop_solver:
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t[name] = numpy.linalg.eigh(prop_solver[name])[1].conjugate().transpose()
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trafo = self.block_structure.convert_matrix(t, space_from='solver', space_to='sumk')
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#self.T = numpy.dot(self.T.transpose().conjugate(),
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# self.w).conjugate().transpose()
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self.block_structure.transformation = trafo_tmp
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else:
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for name in prop:
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t = numpy.linalg.eigh(prop[name])[1].conjugate().transpose()
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trafo[name] = t
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# calculate new Transformation matrix
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#self.T = numpy.dot(self.T.transpose().conjugate(),
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# self.w).conjugate().transpose()
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# measure for the 'unity' of the transformation:
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#wsqr = sum(abs(self.w.diagonal())**2) / self.w.diagonal().size
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#return wsqr
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if write_to_blockstructure:
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if self.block_structure.transformation == None:
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self.block_structure.transformation = [{} for icrsh in range(self.n_corr_shells)]
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for sp in self.spin_block_names[self.corr_shells[icrsh]['SO']]:
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self.block_structure.transformation[icrsh][sp] = numpy.eye(self.corr_shells[icrsh]['dim'], self.corr_shells[icrsh]['dim'], numpy.complex_)
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self.block_structure.transformation[ish] = trafo
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return trafo
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def density_matrix(self, method='using_gf', beta=40.0):
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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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"""Calculate density matrices in one of two ways.
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