mirror of https://github.com/triqs/dft_tools
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
from itertools import *
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import numpy as np
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import triqs.utility.mpi as mpi
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from h5 import *
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from triqs.gf import *
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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.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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import warnings
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warnings.filterwarnings("ignore", category=FutureWarning)
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filename = 'vasp'
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beta = 5.0
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mesh = MeshImFreq(beta=beta, S='Fermion', n_iw=1000)
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SK = SumkDFT(hdf_file = filename+'.h5', use_dft_blocks = False, mesh=mesh)
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# We analyze the block structure of the Hamiltonian
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Sigma = SK.block_structure.create_gf(mesh=mesh)
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SK.put_Sigma([Sigma])
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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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# Print some information on the master node
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iteration_offset = 0
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Sigma_iw = 0
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block_structure = None
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if mpi.is_master_node():
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ar = HDFArchive(filename+'.h5','a')
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if not 'DMFT_results' in ar: ar.create_group('DMFT_results')
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if not 'Iterations' in ar['DMFT_results']: ar['DMFT_results'].create_group('Iterations')
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if 'DMFT_input' in ar:
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block_structure = ar['DMFT_input']['sumk_block_structure']
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if 'iteration_count' in ar['DMFT_results']:
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iteration_offset = ar['DMFT_results']['iteration_count']+1
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print(('offset',iteration_offset))
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Sigma_iw = ar['DMFT_results']['Iterations']['Sigma_it'+str(iteration_offset-1)]
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SK.dc_imp = ar['DMFT_results']['Iterations']['dc_imp'+str(iteration_offset-1)]
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SK.dc_energ = ar['DMFT_results']['Iterations']['dc_energ'+str(iteration_offset-1)]
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SK.chemical_potential = ar['DMFT_results']['Iterations']['chemical_potential'+str(iteration_offset-1)].real
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block_structure = mpi.bcast(block_structure)
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iteration_offset = mpi.bcast(iteration_offset)
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Sigma_iw = mpi.bcast(Sigma_iw)
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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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if block_structure:
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SK.block_structure = block_structure
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else:
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G = SK.extract_G_loc()
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SK.analyse_block_structure_from_gf(G, threshold = 1e-3)
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SK.put_Sigma(Sigma_imp = [Sigma_iw])
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ikarray = numpy.array(list(range(SK.n_k)))
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gf_csc = Gf(mesh=SK.mesh, target_shape=(SK.proj_mat_csc.shape[2],SK.proj_mat_csc.shape[2]))
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G_latt_orb = BlockGf(name_list=['up','down'], block_list=[gf_csc, gf_csc], make_copies=True)
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for ik in mpi.slice_array(ikarray):
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G_latt_KS = SK.lattice_gf(ik=ik)*SK.bz_weights[ik]
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for bname, gf in G_latt_orb:
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gf += SK.downfold(ik, 0, bname, G_latt_KS[bname], gf, shells='csc', ir=None)
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G_latt_orb << mpi.all_reduce(G_latt_orb)
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mpi.barrier()
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if mpi.is_master_node():
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ar['DMFT_results']['Iterations']['G_latt_orb_it'+str(iteration_offset-1)] = G_latt_orb
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if mpi.is_master_node(): del ar
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