from pytriqs.applications.dft.sumk_dft import * from pytriqs.applications.dft.converters import Wien2kConverter from pytriqs.gf.local import * from pytriqs.archive import * import pytriqs.utility.mpi as mpi import numpy import copy class TransBasis: """ Computates rotations into a new basis, using the condition that a given property is diagonal in the new basis. """ def __init__(self, SK=None, hdf_datafile=None): """ Initialization of the class. There are two ways to do so: - existing SumkLDA class : when you have an existing SumkLDA instance - from hdf5 archive : when you want to use data from hdf5 archive Giving the class instance overrides giving the string for the hdf5 archive. Parameters ---------- SK : class SumkLDA, optional Existing instance of SumkLDA class. hdf5_datafile : string, optional Name of hdf5 archive to be used. """ if SK is None: # build our own SK instance if hdf_datafile is None: mpi.report("trans_basis: give SK instance or HDF filename!") return 0 Converter = Wien2kConverter(filename=hdf_datafile, repacking=False) Converter.convert_dft_input() del Converter self.SK = SumkDFT(hdf_file=hdf_datafile + '.h5', use_dft_blocks=False) else: self.SK = SK self.T = copy.deepcopy(self.SK.T[0]) self.w = numpy.identity(SK.corr_shells[0]['dim']) def calculate_diagonalisation_matrix(self, prop_to_be_diagonal='eal'): """ Calculates the diagonalisation matrix w, and stores it as member of the class. Parameters ---------- prop_to_be_diagonal : string, optional Defines the property to be diagonalized. - 'eal' : local hamiltonian (i.e. crystal field) - 'dm' : local density matrix Returns ------- wsqr : double Measure for the degree of rotation done by the diagonalisation. wsqr=1 means no rotation. """ if prop_to_be_diagonal == 'eal': prop = self.SK.eff_atomic_levels()[0] elif prop_to_be_diagonal == 'dm': prop = self.SK.density_matrix(method='using_point_integration')[0] else: mpi.report( "trans_basis: not a valid quantitiy to be diagonal. Choices are 'eal' or 'dm'.") return 0 if self.SK.SO == 0: self.eig, self.w = numpy.linalg.eigh(prop['up']) # calculate new Transformation matrix self.T = numpy.dot(self.T.transpose().conjugate(), self.w).conjugate().transpose() else: self.eig, self.w = numpy.linalg.eigh(prop['ud']) # calculate new Transformation matrix self.T = numpy.dot(self.T.transpose().conjugate(), self.w).conjugate().transpose() # measure for the 'unity' of the transformation: wsqr = sum(abs(self.w.diagonal())**2) / self.w.diagonal().size return wsqr def rotate_gf(self, gf_to_rot): """ Uses the diagonalisation matrix w to rotate a given GF into the new basis. Parameters ---------- gf_to_rot : BlockGf Green's function block to rotate. Returns ------- gfreturn : BlockGf Green's function rotated into the new basis. """ # build a full GF gfrotated = BlockGf(name_block_generator=[(block, GfImFreq( indices=inner, mesh=gf_to_rot.mesh)) for block, inner in self.SK.gf_struct_sumk[0]], make_copies=False) # transform the CTQMC blocks to the full matrix: # ish is the index of the inequivalent shell corresponding to icrsh ish = self.SK.corr_to_inequiv[0] for block, inner in self.gf_struct_solver[ish].iteritems(): for ind1 in inner: for ind2 in inner: gfrotated[self.SK.solver_to_sumk_block[ish][block]][ ind1, ind2] << gf_to_rot[block][ind1, ind2] # Rotate using the matrix w for bname, gf in gfrotated: gfrotated[bname].from_L_G_R( self.w.transpose().conjugate(), gfrotated[bname], self.w) gfreturn = gf_to_rot.copy() # Put back into CTQMC basis: for block, inner in self.gf_struct_solver[ish].iteritems(): for ind1 in inner: for ind2 in inner: gfreturn[block][ind1, ind2] << gfrotated[ self.SK.solver_to_sumk_block[0][block]][ind1, ind2] return gfreturn def write_trans_file(self, filename): """ Writes the new transformation T into a file readable by dmftproj. By that, the requested quantity is diagonal already at input. Parameters ---------- filename : string Name of the file where the transformation is stored. """ f = open(filename, 'w') Tnew = self.T.conjugate() dim = self.SK.corr_shells[0]['dim'] if self.SK.SO == 0: for i in range(dim): st = '' for k in range(dim): st += " %9.6f" % (Tnew[i, k].real) st += " %9.6f" % (Tnew[i, k].imag) for k in range(2 * dim): st += " 0.0" if i < (dim - 1): f.write("%s\n" % (st)) else: st1 = st.replace(' ', '*', 1) f.write("%s\n" % (st1)) for i in range(dim): st = '' for k in range(2 * dim): st += " 0.0" for k in range(dim): st += " %9.6f" % (Tnew[i, k].real) st += " %9.6f" % (Tnew[i, k].imag) if i < (dim - 1): f.write("%s\n" % (st)) else: st1 = st.replace(' ', '*', 1) f.write("%s\n" % (st1)) else: for i in range(dim): st = '' for k in range(dim): st += " %9.6f" % (Tnew[i, k].real) st += " %9.6f" % (Tnew[i, k].imag) if i < (dim - 1): f.write("%s\n" % (st)) else: st1 = st.replace(' ', '*', 1) f.write("%s\n" % (st1)) f.close()