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[doc] Clean and merge python example scripts
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Manuel
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@ -4,19 +4,38 @@ from pytriqs.archive import HDFArchive
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from triqs_cthyb import *
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from pytriqs.gf import *
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.converters.wien2k_converter import *
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dft_filename='SrVO3'
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U = 4.0
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J = 0.65
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beta = 40
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loops = 15 # Number of DMFT sc-loops
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sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
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delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
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dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
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use_blocks = True # use bloc structure from DFT input
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prec_mu = 0.0001
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h_field = 0.0
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## KANAMORI DENSITY-DENSITY (for full Kanamori use h_int_kanamori)
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# Define interaction paramters, DC and Hamiltonian
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U = 4.0
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J = 0.65
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dc_type = 1 # DC type: 0 FLL, 1 Held, 2 AMF
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# Construct U matrix for density-density calculations
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Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
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# Construct density-density Hamiltonian
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h_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat)
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## SLATER HAMILTONIAN
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## Define interaction paramters, DC and Hamiltonian
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#U = 9.6
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#J = 0.8
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#dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
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## Construct Slater U matrix
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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='wien2k'))
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#Umat = t2g_submatrix(U_cubic, convention='wien2k')
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## Construct Slater Hamiltonian
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#h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
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# Solver parameters
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p = {}
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p["max_time"] = -1
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@ -46,7 +65,6 @@ if mpi.is_master_node():
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previous_runs = ar['iterations']
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else:
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f.create_group('dmft_output')
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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@ -60,11 +78,7 @@ orb_names = [i for i in range(n_orb)]
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# Use GF structure determined by DFT blocks
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gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
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# Construct U matrix for density-density calculations
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Umat, Upmat = U_matrix_kanamori(n_orb=n_orb, U_int=U, J_hund=J)
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# Construct density-density Hamiltonian and solver
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h_int = h_int_density(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U=Umat, Uprime=Upmat, H_dump="H.txt")
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# Construct Solver
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S = Solver(beta=beta, gf_struct=gf_struct)
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if previous_present:
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@ -98,19 +112,7 @@ for iteration_number in range(1,loops+1):
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S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
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# Calculate new G0_iw to input into the solver:
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if mpi.is_master_node():
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# We can do a mixing of Delta in order to stabilize the DMFT iterations:
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S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
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# The following lines are uncommented until issue #98 is fixed in TRIQS
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# with HDFArchive(dft_filename+'.h5','a') as ar:
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# if (iteration_number>1 or previous_present):
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# mpi.report("Mixing input Delta with factor %s"%delta_mix)
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# Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['dmft_output']['Delta_iw']
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# S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
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# ar['dmft_output']['Delta_iw'] = delta(S.G0_iw)
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S.G0_iw << inverse(S.G0_iw)
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S.G0_iw << mpi.bcast(S.G0_iw)
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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_int, **p)
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@ -131,7 +133,7 @@ for iteration_number in range(1,loops+1):
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# Write the final Sigma and G to the hdf5 archive:
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if mpi.is_master_node():
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with ar = HDFArchive(dft_filename+'.h5','a') as ar:
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with HDFArchive(dft_filename+'.h5','a') as ar:
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ar['dmft_output']['iterations'] = iteration_number + previous_runs
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ar['dmft_output']['G_tau'] = S.G_tau
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ar['dmft_output']['G_iw'] = S.G_iw
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@ -1,149 +0,0 @@
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import pytriqs.utility.mpi as mpi
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from pytriqs.operators.util import *
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from pytriqs.archive import HDFArchive
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from triqs_cthyb import *
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from pytriqs.gf import *
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from triqs_dft_tools.sumk_dft import *
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from triqs_dft_tools.converters.wien2k_converter import *
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dft_filename='SrVO3'
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U = 9.6
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J = 0.8
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beta = 40
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loops = 10 # Number of DMFT sc-loops
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sigma_mix = 1.0 # Mixing factor of Sigma after solution of the AIM
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delta_mix = 1.0 # Mixing factor of Delta as input for the AIM
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dc_type = 0 # DC type: 0 FLL, 1 Held, 2 AMF
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use_blocks = True # use bloc structure from DFT input
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prec_mu = 0.0001
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h_field = 0.0
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# Solver parameters
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p = {}
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p["max_time"] = -1
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p["random_seed"] = 123 * mpi.rank + 567
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p["length_cycle"] = 200
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p["n_warmup_cycles"] = 100000
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p["n_cycles"] = 1000000
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p["perform_tail_fit"] = True
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p["fit_max_moment"] = 4
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p["fit_min_n"] = 30
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p["fit_max_n"] = 60
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# If conversion step was not done, we could do it here. Uncomment the lines it you want to do this.
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#from triqs_dft_tools.converters.wien2k_converter import *
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#Converter = Wien2kConverter(filename=dft_filename, repacking=True)
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#Converter.convert_dft_input()
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#mpi.barrier()
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previous_runs = 0
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previous_present = False
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','a') as f:
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if 'dmft_output' in f:
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ar = f['dmft_output']
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if 'iterations' in ar:
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previous_present = True
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previous_runs = ar['iterations']
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else:
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f.create_group('dmft_output')
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previous_runs = mpi.bcast(previous_runs)
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previous_present = mpi.bcast(previous_present)
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SK=SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks,h_field=h_field)
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n_orb = SK.corr_shells[0]['dim']
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l = SK.corr_shells[0]['l']
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spin_names = ["up","down"]
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orb_names = [i for i in range(n_orb)]
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# Use GF structure determined by DFT blocks
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gf_struct = [(block, indices) for block, indices in SK.gf_struct_solver[0].iteritems()]
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# Construct Slater U matrix
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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='wien2k'))
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Umat = t2g_submatrix(U_cubic, convention='wien2k')
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# Construct Hamiltonian and solver
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h_int = h_int_slater(spin_names, orb_names, map_operator_structure=SK.sumk_to_solver[0], U_matrix=Umat)
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S = Solver(beta=beta, gf_struct=gf_struct)
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if previous_present:
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chemical_potential = 0
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dc_imp = 0
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dc_energ = 0
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','r') as ar:
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S.Sigma_iw << ar['dmft_output']['Sigma_iw']
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chemical_potential,dc_imp,dc_energ = SK.load(['chemical_potential','dc_imp','dc_energ'])
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S.Sigma_iw << mpi.bcast(S.Sigma_iw)
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chemical_potential = mpi.bcast(chemical_potential)
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dc_imp = mpi.bcast(dc_imp)
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dc_energ = mpi.bcast(dc_energ)
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SK.set_mu(chemical_potential)
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SK.set_dc(dc_imp,dc_energ)
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for iteration_number in range(1,loops+1):
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if mpi.is_master_node(): print "Iteration = ", iteration_number
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SK.symm_deg_gf(S.Sigma_iw,orb=0) # symmetrise Sigma
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SK.set_Sigma([ S.Sigma_iw ]) # set Sigma into the SumK class
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chemical_potential = SK.calc_mu( precision = prec_mu ) # find the chemical potential for given density
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S.G_iw << SK.extract_G_loc()[0] # calc the local Green function
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mpi.report("Total charge of Gloc : %.6f"%S.G_iw.total_density())
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# Init the DC term and the real part of Sigma, if no previous runs found:
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if (iteration_number==1 and previous_present==False):
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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)
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S.Sigma_iw << SK.dc_imp[0]['up'][0,0]
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# Calculate new G0_iw to input into the solver:
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if mpi.is_master_node():
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# We can do a mixing of Delta in order to stabilize the DMFT iterations:
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S.G0_iw << S.Sigma_iw + inverse(S.G_iw)
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# The following lines are uncommented until issue #98 is fixed in TRIQS
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# with HDFArchive(dft_filename+'.h5','a') as ar:
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# if (iteration_number>1 or previous_present):
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# mpi.report("Mixing input Delta with factor %s"%delta_mix)
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# Delta = (delta_mix * delta(S.G0_iw)) + (1.0-delta_mix) * ar['dmft_output']['Delta_iw']
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# S.G0_iw << S.G0_iw + delta(S.G0_iw) - Delta
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# ar['dmft_output']['Delta_iw'] = delta(S.G0_iw)
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S.G0_iw << inverse(S.G0_iw)
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S.G0_iw << mpi.bcast(S.G0_iw)
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# Solve the impurity problem:
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S.solve(h_int=h_int, **p)
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# Solved. Now do post-processing:
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mpi.report("Total charge of impurity problem : %.6f"%S.G_iw.total_density())
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# Now mix Sigma and G with factor sigma_mix, if wanted:
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if (iteration_number>1 or previous_present):
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','r') as ar:
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mpi.report("Mixing Sigma and G with factor %s"%sigma_mix)
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S.Sigma_iw << sigma_mix * S.Sigma_iw + (1.0-sigma_mix) * ar['dmft_output']['Sigma_iw']
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S.G_iw << sigma_mix * S.G_iw + (1.0-sigma_mix) * ar['dmft_output']['G_iw']
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S.G_iw << mpi.bcast(S.G_iw)
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S.Sigma_iw << mpi.bcast(S.Sigma_iw)
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# Write the final Sigma and G to the hdf5 archive:
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if mpi.is_master_node():
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with HDFArchive(dft_filename+'.h5','a') as ar:
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ar['dmft_output']['iterations'] = iteration_number + previous_runs
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ar['dmft_output']['G_tau'] = S.G_tau
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ar['dmft_output']['G_iw'] = S.G_iw
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ar['dmft_output']['Sigma_iw'] = S.Sigma_iw
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ar['dmft_output']['G0-%s'%(iteration_number)] = S.G0_iw
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ar['dmft_output']['G-%s'%(iteration_number)] = S.G_iw
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ar['dmft_output']['Sigma-%s'%(iteration_number)] = S.Sigma_iw
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# Set the new double counting:
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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)
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# Save stuff into the dft_output group of hdf5 archive in case of rerun:
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SK.save(['chemical_potential','dc_imp','dc_energ'])
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@ -6,13 +6,9 @@ Some additional parameter are introduced to make the calculation
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more efficient. This is a more advanced example, which is
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also suited for parallel execution.
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For the convenience of the user, we provide also two
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working python scripts in this documentation. One for a calculation
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using Kanamori definitions (:download:`dft_dmft_cthyb.py
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<images_scripts/dft_dmft_cthyb.py>`) and one with a
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rotational-invariant Slater interaction Hamiltonian (:download:`dft_dmft_cthyb_slater.py
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<images_scripts/dft_dmft_cthyb_slater.py>`). The user has to adapt these
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scripts to his own needs. How to execute your script is described :ref:`here<runpy>`.
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For the convenience of the user, we provide also a full
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python script (:download:`dft_dmft_cthyb.py <images_scripts/dft_dmft_cthyb.py>`).
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The user has to adapt it to his own needs. How to execute your script is described :ref:`here<runpy>`.
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The conversion will now be discussed in detail for the Wien2k and VASP packages.
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For more details we refer to the :ref:`documentation <conversion>`.
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