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https://github.com/triqs/dft_tools
synced 2024-12-22 12:23:41 +01:00
remove gf_struct_flatten function and replace with triqs version
This commit is contained in:
Alexander Hampel
parent
d28fe3c1ef
commit
320b2d2dfd
@ -23,11 +23,11 @@ DFTTools Version 3.1.0 is a release that
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* bugfix: This fix makes the function find_rot_mat() safer to use in case there are errors in finding the correct mapping. The converter will now abort if the agreement in mapping is below a user-definable threshold.
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### Change in gf_struct
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* In line with TRIQS 3.1.x, the form of the Green's function's structure (`gf_struct`) has been modified
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* In line with TRIQS 3.1.x, the form of the Green's function's structure (`gf_struct`) has been modified (see [triqs changelog](https://triqs.github.io/triqs/latest/ChangeLog.html#change-in-gf-struct-objects) for more information)
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* Instead of `gf_struct = [("up", [0, 1]), ("down", [0, 1])]`, the new convention uses `gf_struct = [("up", 2), ("down", 2)]`
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* This modifies the form of `gf_struct_solver` (and `sumk`) in `block_structure` and `SumkDFT` as well.
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* Backwards-compatibility with old, stored `block_structure` objects is given, however a warning is issued.
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* A helper-function `block_structure.gf_struct_flatten(...)` is provided to manually bring `gf_struct`s to the new form.
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* A helper-function `triqs.gf.block_gf.fix_gf_struct_type(gf_struct_old)` is provided in triqs to manually bring `gf_struct`s to the new form.
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### Documentation
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* change to read the docs sphinx theme
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@ -6,7 +6,6 @@ from triqs.gf import *
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import sys, triqs.version as triqs_version
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.sumk_dft_tools import *
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from triqs_dft_tools.block_structure import gf_struct_flatten
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from triqs.operators.util.hamiltonians import *
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from triqs.operators.util.U_matrix import *
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from triqs_cthyb import *
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@ -20,8 +19,8 @@ filename = 'nio'
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
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beta = 5.0
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beta = 5.0
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Sigma = SK.block_structure.create_gf(beta=beta)
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SK.put_Sigma([Sigma])
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G = SK.extract_G_loc()
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@ -41,7 +40,7 @@ spin_names = ['up','down']
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orb_names = [i for i in range(0,n_orb)]
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#gf_struct = set_operator_structure(spin_names, orb_names, orb_hyb)
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gf_struct = gf_struct_flatten(SK.gf_struct_solver[0])
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gf_struct = SK.gf_struct_solver_list[0]
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mpi.report('Sumk to Solver: %s'%SK.sumk_to_solver)
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mpi.report('GF struct sumk: %s'%SK.gf_struct_sumk)
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mpi.report('GF struct solver: %s'%SK.gf_struct_solver)
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@ -49,7 +48,7 @@ mpi.report('GF struct solver: %s'%SK.gf_struct_solver)
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S = Solver(beta=beta, gf_struct=gf_struct)
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# Construct the Hamiltonian and save it in Hamiltonian_store.txt
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H = Operator()
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H = Operator()
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U = 8.0
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J = 1.0
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@ -130,14 +129,14 @@ mpi.report('%s DMFT cycles requested. Starting with iteration %s.'%(n_iterations
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# The infamous DMFT self consistency cycle
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for it in range(iteration_offset, iteration_offset + n_iterations):
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mpi.report('Doing iteration: %s'%it)
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# Get G0
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S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
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# Solve the impurity problem
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S.solve(h_int = H, **p)
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if mpi.is_master_node():
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if mpi.is_master_node():
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ar['DMFT_input']['Iterations']['solver_dict_it'+str(it)] = p
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ar['DMFT_results']['Iterations']['Gimp_it'+str(it)] = S.G_iw
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ar['DMFT_results']['Iterations']['Gtau_it'+str(it)] = S.G_tau
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@ -150,13 +149,13 @@ for it in range(iteration_offset, iteration_offset + n_iterations):
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SK.put_Sigma(Sigma_imp=[S.Sigma_iw])
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SK.calc_mu(precision=0.01)
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S.G_iw << SK.extract_G_loc()[0]
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# print densities
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for sig,gf in S.G_iw:
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mpi.report("Orbital %s density: %.6f"%(sig,dm[sig][0,0]))
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mpi.report('Total charge of Gloc : %.6f'%S.G_iw.total_density())
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if mpi.is_master_node():
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if mpi.is_master_node():
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ar['DMFT_results']['iteration_count'] = it
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ar['DMFT_results']['Iterations']['Sigma_it'+str(it)] = S.Sigma_iw
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ar['DMFT_results']['Iterations']['Gloc_it'+str(it)] = S.G_iw
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@ -6,7 +6,6 @@ from triqs.gf import *
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import sys, triqs.version as triqs_version
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.sumk_dft_tools import *
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from triqs_dft_tools.block_structure import gf_struct_flatten
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from triqs.operators.util.hamiltonians import *
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from triqs.operators.util.U_matrix import *
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from triqs_cthyb import *
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@ -21,14 +20,14 @@ warnings.filterwarnings("ignore", category=FutureWarning)
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def dmft_cycle():
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filename = 'nio'
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Converter = VaspConverter(filename=filename)
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Converter.convert_dft_input()
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False)
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beta = 5.0
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beta = 5.0
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Sigma = SK.block_structure.create_gf(beta=beta)
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SK.put_Sigma([Sigma])
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G = SK.extract_G_loc()
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@ -40,38 +39,38 @@ def dmft_cycle():
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mpi.report('block {0:d} consists of orbitals:'.format(iblock))
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for keys in list(SK.deg_shells[i_sh][iblock].keys()):
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mpi.report(' '+keys)
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# Setup CTQMC Solver
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n_orb = SK.corr_shells[0]['dim']
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spin_names = ['up','down']
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orb_names = [i for i in range(0,n_orb)]
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#gf_struct = set_operator_structure(spin_names, orb_names, orb_hyb)
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gf_struct = SK.gf_struct_solver[0]
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gf_struct = SK.gf_struct_solver_list[0]
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mpi.report('Sumk to Solver: %s'%SK.sumk_to_solver)
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mpi.report('GF struct sumk: %s'%SK.gf_struct_sumk)
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mpi.report('GF struct solver: %s'%SK.gf_struct_solver)
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S = Solver(beta=beta, gf_struct=gf_struct)
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# Construct the Hamiltonian and save it in Hamiltonian_store.txt
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H = Operator()
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H = Operator()
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U = 8.0
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J = 1.0
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U_sph = U_matrix(l=2, U_int=U, J_hund=J)
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U_cubic = transform_U_matrix(U_sph, spherical_to_cubic(l=2, convention=''))
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Umat, Upmat = reduce_4index_to_2index(U_cubic)
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H = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
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# Print some information on the master node
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mpi.report('Greens function structure is: %s '%gf_struct)
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mpi.report('U Matrix set to:\n%s'%Umat)
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mpi.report('Up Matrix set to:\n%s'%Upmat)
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# Parameters for the CTQMC Solver
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p = {}
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p["max_time"] = -1
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@ -84,14 +83,14 @@ def dmft_cycle():
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p["fit_min_n"] = 30
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p["fit_max_n"] = 50
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p["perform_tail_fit"] = True
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# Double Counting: 0 FLL, 1 Held, 2 AMF
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DC_type = 0
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DC_value = 59.0
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# Prepare hdf file and and check for previous iterations
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n_iterations = 1
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iteration_offset = 0
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if mpi.is_master_node():
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ar = HDFArchive(filename+'.h5','a')
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@ -119,33 +118,33 @@ def dmft_cycle():
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SK.dc_imp = mpi.bcast(SK.dc_imp)
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SK.dc_energ = mpi.bcast(SK.dc_energ)
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SK.chemical_potential = mpi.bcast(SK.chemical_potential)
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# Calc the first G0
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SK.symm_deg_gf(S.Sigma_iw, ish=0)
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SK.put_Sigma(Sigma_imp = [S.Sigma_iw])
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SK.calc_mu(precision=0.01)
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S.G_iw << SK.extract_G_loc()[0]
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SK.symm_deg_gf(S.G_iw, ish=0)
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#Init the DC term and the self-energy if no previous iteration was found
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if iteration_offset == 0:
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dm = S.G_iw.density()
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SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=DC_type,use_dc_value=DC_value)
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S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
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mpi.report('%s DMFT cycles requested. Starting with iteration %s.'%(n_iterations,iteration_offset))
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# The infamous DMFT self consistency cycle
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for it in range(iteration_offset, iteration_offset + n_iterations):
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mpi.report('Doing iteration: %s'%it)
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# Get G0
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S.G0_iw << inverse(S.Sigma_iw + inverse(S.G_iw))
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# Solve the impurity problem
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S.solve(h_int = H, **p)
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if mpi.is_master_node():
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if mpi.is_master_node():
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ar['DMFT_input']['Iterations']['solver_dict_it'+str(it)] = p
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ar['DMFT_results']['Iterations']['Gimp_it'+str(it)] = S.G_iw
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ar['DMFT_results']['Iterations']['Gtau_it'+str(it)] = S.G_tau
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@ -158,13 +157,13 @@ def dmft_cycle():
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SK.put_Sigma(Sigma_imp=[S.Sigma_iw])
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SK.calc_mu(precision=0.01)
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S.G_iw << SK.extract_G_loc()[0]
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# print densities
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for sig,gf in S.G_iw:
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mpi.report("Orbital %s density: %.6f"%(sig,dm[sig][0,0]))
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mpi.report('Total charge of Gloc : %.6f'%S.G_iw.total_density())
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if mpi.is_master_node():
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if mpi.is_master_node():
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ar['DMFT_results']['iteration_count'] = it
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ar['DMFT_results']['Iterations']['Sigma_it'+str(it)] = S.Sigma_iw
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ar['DMFT_results']['Iterations']['Gloc_it'+str(it)] = S.G_iw
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@ -172,31 +171,31 @@ def dmft_cycle():
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ar['DMFT_results']['Iterations']['dc_imp'+str(it)] = SK.dc_imp
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ar['DMFT_results']['Iterations']['dc_energ'+str(it)] = SK.dc_energ
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ar['DMFT_results']['Iterations']['chemical_potential'+str(it)] = SK.chemical_potential
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if mpi.is_master_node():
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print('calculating mu...')
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SK.chemical_potential = SK.calc_mu( precision = 0.000001 )
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if mpi.is_master_node():
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print('calculating GAMMA')
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SK.calc_density_correction(dm_type='vasp')
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if mpi.is_master_node():
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print('calculating energy corrections')
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correnerg = 0.5 * (S.G_iw * S.Sigma_iw).total_density()
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dm = S.G_iw.density() # compute the density matrix of the impurity problem
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SK.calc_dc(dm, U_interact=U, J_hund=J, orb=0, use_dc_formula=DC_type,use_dc_value=DC_value)
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dc_energ = SK.dc_energ[0]
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if mpi.is_master_node():
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if mpi.is_master_node():
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ar['DMFT_results']['Iterations']['corr_energy_it'+str(it)] = correnerg
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ar['DMFT_results']['Iterations']['dc_energy_it'+str(it)] = dc_energ
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if mpi.is_master_node(): del ar
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return correnerg, dc_energ
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@ -120,7 +120,7 @@ class BlockStructure(object):
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deg_shells=None,
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corr_to_inequiv = None,
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transformation=None):
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# Ensure backwards-compatibility with pre-3.1.x gf_structs
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show_gf_struct_warning = False
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if gf_struct_sumk != None:
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@ -625,7 +625,7 @@ class BlockStructure(object):
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for k in list(self.sumk_to_solver[ish].keys()):
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if not k in su2so:
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su2so[k] = (None, None)
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for new_block in gf_struct:
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assert all(np.sort(gf_struct[new_block]) == list(range(len(gf_struct[new_block])))) ,\
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"New gf_struct does not have valid 0-based indices!"
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@ -1190,45 +1190,5 @@ class BlockStructure(object):
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s += str(self.transformation)
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return s
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def gf_struct_flatten(gf_struct):
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'''
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flattens gf_struct objecti
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input gf_struct can looks like this:
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[('up', [0, 1, 2]), ('down', [0, 1, 2])]
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and will be returned as
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[('up', 3), ('down', 3)]
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Same for dict but replacing the values. This is for compatibility with the upcoming triqs releases.
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Parameters
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----------
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gf_struct: list of tuple or dict representing the Gf structure
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__Returns:__
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gf_struct_flat: flattens the values of the dict or the tuple representing the Gf indices by replacing them with the len of the list of indices
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'''
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if isinstance(gf_struct, list):
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# create a copy of the original list
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gf_struct_flat = gf_struct.copy()
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for idx, block in enumerate(gf_struct_flat):
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# exchange list of indices with length of list
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gf_struct_flat[idx] = (block[0], len(block[1]))
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elif isinstance(gf_struct, dict):
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# create a copy of the original dict
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gf_struct_flat = dict(gf_struct)
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for key, value in gf_struct_flat.items():
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# exchange list of indices with length of list
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gf_struct_flat[key] = len(value)
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else:
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raise Exception('gf_struct input needs to be list or dict')
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return gf_struct_flat
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from h5.formats import register_class
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register_class(BlockStructure)
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@ -3,7 +3,7 @@ from triqs.utility.h5diff import h5diff, compare, failures
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from triqs.gf import *
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from triqs.utility.comparison_tests import assert_block_gfs_are_close
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from scipy.linalg import expm
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from triqs_dft_tools.block_structure import BlockStructure, gf_struct_flatten
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from triqs_dft_tools.block_structure import BlockStructure
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import numpy as np
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