mirror of https://github.com/triqs/dft_tools
239 lines
8.5 KiB
Python
239 lines
8.5 KiB
Python
##########################################################################
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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. Aichhorn, L. Pourovskii, V. Vildosola
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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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"""
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Module for orbital basis transformations
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"""
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.converters import Wien2kConverter
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from triqs.gf import *
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from h5 import *
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import triqs.utility.mpi as mpi
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import numpy
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import copy
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class TransBasis:
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"""
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Computates rotations into a new basis, using the condition that a given property is diagonal in the new basis.
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"""
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def __init__(self, SK=None, hdf_datafile=None):
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"""
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Initialization of the class. There are two ways to do so:
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- existing SumkLDA class : when you have an existing SumkLDA instance
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- from hdf5 archive : when you want to use data from hdf5 archive
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Giving the class instance overrides giving the string for the hdf5 archive.
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Parameters
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----------
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SK : class SumkLDA, optional
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Existing instance of SumkLDA class.
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hdf5_datafile : string, optional
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Name of hdf5 archive to be used.
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"""
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if SK is None:
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# build our own SK instance
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if hdf_datafile is None:
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mpi.report("trans_basis: give SK instance or HDF filename!")
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return 0
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Converter = Wien2kConverter(filename=hdf_datafile, repacking=False)
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Converter.convert_dft_input()
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del Converter
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self.SK = SumkDFT(hdf_file=hdf_datafile +
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'.h5', use_dft_blocks=False)
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else:
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self.SK = SK
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self.T = copy.deepcopy(self.SK.T[0])
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self.w = numpy.identity(SK.corr_shells[0]['dim'])
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def calculate_diagonalisation_matrix(self, prop_to_be_diagonal='eal', calc_in_solver_blocks = False):
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"""
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Calculates the diagonalisation matrix w, and stores it as member of the class.
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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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Returns
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-------
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wsqr : double
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Measure for the degree of rotation done by the diagonalisation. wsqr=1 means no rotation.
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"""
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if prop_to_be_diagonal == 'eal':
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prop = self.SK.eff_atomic_levels()[0]
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elif prop_to_be_diagonal == 'dm':
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prop = self.SK.density_matrix(method='using_point_integration')[0]
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else:
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mpi.report(
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"trans_basis: 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 = self.SK.block_structure.transformation
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self.SK.block_structure.transformation = None
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prop_solver = self.SK.block_structure.convert_matrix(prop, space_from='sumk', space_to='solver')
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v= {}
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for name in prop_solver:
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v[name] = numpy.linalg.eigh(prop_solver[name])[1]
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self.w = self.SK.block_structure.convert_matrix(v, space_from='solver', space_to='sumk')['ud' if self.SK.SO else 'up']
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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.SK.block_structure.transformation = trafo
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else:
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if self.SK.SO == 0:
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self.eig, self.w = numpy.linalg.eigh(prop['up'])
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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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else:
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self.eig, self.w = numpy.linalg.eigh(prop['ud'])
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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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def rotate_gf(self, gf_to_rot):
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"""
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Uses the diagonalisation matrix w to rotate a given GF into the new basis.
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Parameters
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----------
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gf_to_rot : BlockGf
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Green's function block to rotate.
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Returns
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-------
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gfreturn : BlockGf
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Green's function rotated into the new basis.
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"""
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# build a full GF
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gfrotated = BlockGf(name_block_generator=[(block, GfImFreq(
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target_shape=(block_dim, block_dim), mesh=gf_to_rot.mesh)) for block, block_dim in self.SK.gf_struct_sumk[0]], make_copies=False)
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# transform the CTQMC blocks to the full matrix:
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# ish is the index of the inequivalent shell corresponding to icrsh
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ish = self.SK.corr_to_inequiv[0]
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for block, block_dim in self.gf_struct_solver[ish].items():
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for ind1 in range(block_dim):
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for ind2 in range(block_dim):
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gfrotated[self.SK.solver_to_sumk_block[ish][block]][
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ind1, ind2] << gf_to_rot[block][ind1, ind2]
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# Rotate using the matrix w
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for bname, gf in gfrotated:
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gfrotated[bname].from_L_G_R(
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self.w.transpose().conjugate(), gfrotated[bname], self.w)
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gfreturn = gf_to_rot.copy()
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# Put back into CTQMC basis:
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for block, block_dim in self.gf_struct_solver[ish].items():
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for ind1 in range(block_dim):
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for ind2 in range(block_dim):
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gfreturn[block][ind1, ind2] << gfrotated[
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self.SK.solver_to_sumk_block[0][block]][ind1, ind2]
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return gfreturn
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def write_trans_file(self, filename):
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"""
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Writes the new transformation T into a file readable by dmftproj. By that, the requested quantity is
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diagonal already at input.
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Parameters
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----------
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filename : string
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Name of the file where the transformation is stored.
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"""
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f = open(filename, 'w')
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Tnew = self.T.conjugate()
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dim = self.SK.corr_shells[0]['dim']
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if self.SK.SO == 0:
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for i in range(dim):
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st = ''
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for k in range(dim):
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st += " %9.6f" % (Tnew[i, k].real)
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st += " %9.6f" % (Tnew[i, k].imag)
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for k in range(2 * dim):
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st += " 0.0"
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if i < (dim - 1):
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f.write("%s\n" % (st))
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else:
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st1 = st.replace(' ', '*', 1)
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f.write("%s\n" % (st1))
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for i in range(dim):
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st = ''
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for k in range(2 * dim):
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st += " 0.0"
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for k in range(dim):
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st += " %9.6f" % (Tnew[i, k].real)
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st += " %9.6f" % (Tnew[i, k].imag)
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if i < (dim - 1):
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f.write("%s\n" % (st))
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else:
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st1 = st.replace(' ', '*', 1)
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f.write("%s\n" % (st1))
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else:
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for i in range(dim):
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st = ''
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for k in range(dim):
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st += " %9.6f" % (Tnew[i, k].real)
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st += " %9.6f" % (Tnew[i, k].imag)
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if i < (dim - 1):
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f.write("%s\n" % (st))
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else:
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st1 = st.replace(' ', '*', 1)
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f.write("%s\n" % (st1))
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f.close()
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