from triqs.utility.comparison_tests import * from triqs_dft_tools.sumk_dft import * import numpy as np def is_diagonal_matrix(M): return abs(np.sum(M-np.diag(np.diagonal(M)))) < 1e-10 def call_diagonalize(SK): SK.block_structure.transformation = None t_sumk_eal = SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal', calc_in_solver_blocks=False, write_to_blockstructure = True) SK.block_structure.transformation = None t_solver_eal = SK.calculate_diagonalization_matrix(prop_to_be_diagonal='eal', calc_in_solver_blocks=True, write_to_blockstructure = True) SK.block_structure.transformation = None t_solver_dm = SK.calculate_diagonalization_matrix(prop_to_be_diagonal='dm', calc_in_solver_blocks=False, write_to_blockstructure = True) SK.block_structure.transformation = None t_sumk_dm = SK.calculate_diagonalization_matrix(prop_to_be_diagonal='dm', calc_in_solver_blocks=True, write_to_blockstructure = True) SK.block_structure.transformation = None return t_sumk_eal, t_solver_eal, t_sumk_dm, t_solver_dm SK = SumkDFT(hdf_file = 'SrVO3.ref.h5', use_dft_blocks=True) # only eal and dm are allowed SK.block_structure.transformation = None assert not SK.calculate_diagonalization_matrix(prop_to_be_diagonal='test') # check for shell index assert not SK.calculate_diagonalization_matrix(shells = [15]) # calling the function twice leads to block_structure.transformation already being set SK.calculate_diagonalization_matrix() assert not SK.calculate_diagonalization_matrix() SK.block_structure.transformation = None # Check writing to block_structure SK.calculate_diagonalization_matrix(write_to_blockstructure=False) assert SK.block_structure.transformation is None SK.block_structure.transformation = None SK.calculate_diagonalization_matrix(write_to_blockstructure=True) assert SK.block_structure.transformation is not None SK.block_structure.transformation = None t_sumk_eal, t_solver_eal, t_sumk_dm, t_solver_dm = call_diagonalize(SK) # All matrices should be identities for orb in range(SK.n_corr_shells): for block in t_solver_eal[orb]: assert_arrays_are_close(t_sumk_eal[orb][block],np.identity(3), precision=1e-6) assert_arrays_are_close(t_sumk_dm[orb][block],np.identity(3), precision=1e-6) assert_arrays_are_close(t_solver_eal[orb][block],np.identity(3), precision=1e-6) assert_arrays_are_close(t_solver_dm[orb][block],np.identity(3), precision=1e-6) SK = SumkDFT(hdf_file = 'w90_convert_wannier.ref.h5', use_dft_blocks=True) t_sumk_eal, t_solver_eal, t_sumk_dm, t_solver_dm = call_diagonalize(SK) # In this example solver and sumk should be the same for orb in range(SK.n_corr_shells): for block in t_solver_eal[orb]: assert_arrays_are_close(t_sumk_eal[orb][block],t_solver_eal[orb][block], precision=1e-6) assert_arrays_are_close(t_sumk_dm[orb][block],t_solver_dm[orb][block], precision=1e-6) # Check if transformations make eal and dm really diagonal eal = SK.eff_atomic_levels()[0] for e in eal: assert is_diagonal_matrix(np.dot(np.dot(t_solver_eal[0][e], eal[e].conj().T),t_solver_eal[0][e].conj().T)) dm = SK.density_matrix(method='using_point_integration') for dmi in dm: for e in dmi: assert is_diagonal_matrix(np.dot(np.dot(t_solver_dm[0][e], dmi[e].conj().T),t_solver_dm[0][e].conj().T)) # Test convert_operator SK = SumkDFT(hdf_file = 'SrVO3.ref.h5', use_dft_blocks=True) BS = SK.block_structure from triqs.operators.util import h_int_slater, U_matrix, t2g_submatrix, transform_U_matrix U3x3 = t2g_submatrix(U_matrix(2, U_int=2, J_hund=0.2, basis='spheric')) BS.transformation = [{'up':np.eye(3), 'down': np.eye(3)}] H0 = h_int_slater(spin_names=['up','down'], orb_names=range(3), U_matrix=U3x3, off_diag=False) H1 = h_int_slater(spin_names=['up','down'], orb_names=range(3), U_matrix=U3x3, off_diag=True) assert( H0 == BS.convert_operator(H1) ) # Trafo Matrix switching index 1 & 2 BS.transformation = [{'up':np.array([[1,0,0],[0,0,1],[0,1,0]]), 'down': np.array([[1,0,0],[0,0,1],[0,1,0]])}] H2 = BS.convert_operator(h_int_slater(spin_names=['up','down'], orb_names=[0,2,1], U_matrix=U3x3, off_diag=True)) assert( H0 == H2 ) BS.transformation = [{'up':np.array([[1,0,0],[0,1/np.sqrt(2),1/np.sqrt(2)],[0,1/np.sqrt(2),-1/np.sqrt(2)]]), 'down': np.array([[1,0,0],[0,1/np.sqrt(2),1/np.sqrt(2)],[0,1/np.sqrt(2),-1/np.sqrt(2)]])}] H3 = BS.convert_operator(h_int_slater(spin_names=['up','down'], orb_names=[0,1,2], U_matrix=U3x3, off_diag=True)) for op in H3: for c_op in op[0]: assert(BS.solver_to_sumk[0][(c_op[1][0], c_op[1][1])] is not None) # This crashes with a key error if the operator structure is not the solver structure U_trafod = transform_U_matrix(U3x3, BS.transformation[0]['up'].conjugate()) # The notorious .conjugate() H4 = h_int_slater(spin_names=['up','down'], orb_names=range(3), U_matrix=U_trafod, map_operator_structure=BS.sumk_to_solver[0]) assert( H4 == H3 ) # check that convert_operator does the same as transform_U_matrix