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https://github.com/triqs/dft_tools
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edd1ff4529
A first general restructuration of the doc according to the pattern [tour|tutorial|reference]. In the reference part, objects are documented per topic. In each topic, [definition|c++|python|hdf5] (not yet implemented)
46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
from pytriqs.gf.local import *
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from pytriqs.operators import *
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from pytriqs.archive import *
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import pytriqs.utility.mpi as mpi
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# Set up a few parameters
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U = 2.5
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half_bandwidth = 1.0
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chemical_potential = U/2.0
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beta = 100
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n_loops = 5
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# Construct the CTQMC solver
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from pytriqs.applications.impurity_solvers.cthyb_matrix import Solver
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S = Solver(beta = beta, gf_struct = [ ('up',[1]), ('down',[1]) ])
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# Initalize the Green's function to a semi circular
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S.G <<= SemiCircular(half_bandwidth)
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# Now do the DMFT loop
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for IterationNumber in range(n_loops):
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# Compute S.G0 with the self-consistency condition while imposing paramagnetism
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g = 0.5 * ( S.G['up'] + S.G['down'] )
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for name, g0block in S.G0:
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g0block <<= inverse( iOmega_n + chemical_potential - (half_bandwidth/2.0)**2 * g )
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# Run the solver
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S.solve(H_local = U * N('up',1) * N('down',1), # Local Hamiltonian
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quantum_numbers = { 'Nup' : N('up',1), 'Ndown' : N('down',1) }, # Quantum Numbers (operators commuting with H_Local)
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n_cycles = 5000, # Number of QMC cycles
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length_cycle = 200, # Length of a cycle
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n_warmup_cycles = 1000, # How many warmup cycles
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n_legendre = 30, # Use 30 Legendre coefficients to represent G(tau)
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random_name = "mt19937", # Use the Mersenne Twister 19937 random generator
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use_segment_picture = True) # Here we can use the segment picture
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# Some intermediate saves
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if mpi.is_master_node():
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R = HDFArchive("single_site_bethe.h5")
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R["G-%s"%IterationNumber] = S.G
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del R
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# Here we would usually write some convergence test
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# if Converged : break
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