mirror of
https://github.com/triqs/dft_tools
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148 lines
6.3 KiB
Python
148 lines
6.3 KiB
Python
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################################################################################
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#
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# TRIQS: a Toolbox for Research in Interacting Quantum Systems
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#
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# Copyright (C) 2011 by M. Ferrero, O. Parcollet
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#
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# TRIQS is free software: you can redistribute it and/or modify it under the
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# terms of the GNU General Public License as published by the Free Software
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# Foundation, either version 3 of the License, or (at your option) any later
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# version.
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#
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# TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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# FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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# details.
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#
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# You should have received a copy of the GNU General Public License along with
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# TRIQS. If not, see <http://www.gnu.org/licenses/>.
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#
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################################################################################
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from pytriqs.gf.local import *
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import pytriqs.utility.mpi as mpi
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from itertools import *
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import inspect
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import copy,numpy
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class SumkDiscrete:
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"""
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INTERNAL USE
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The function to compute \[ G \leftarrow \sum_k (\omega + \mu - eps_k - Sigma(k,\omega))^{-1} \]
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for GF functions with blocks of the size of the matrix eps_k with a discrete sum.
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The class contains the discretized hoppings and points in the arrays
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Hopping, BZ_Points,BZ_weights,Mu_Pattern,Overlap (IF non orthogonal)
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It can also generate a grid (ComputeGrid) for a regular grid or a Gauss-Legendre sum.
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"""
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def __init__ (self, dim, gf_struct, orthogonal_basis = True ):
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"""
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Just constructs the arrays, but without initializing them
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- dim is the dimension
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- gf_struct : Indices of the Green function
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- orthogonal_basis : True by default
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"""
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self.__GFBLOC_Structure = copy.deepcopy(gf_struct)
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self.orthogonal_basis,self.dim = orthogonal_basis,dim
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#-------------------------------------------------------------
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def resize_arrays (self, nk):
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"""
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Just constructs the arrays, but without initializing them
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- nk : total number of k points
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"""
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# constructs the arrays.
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no = len(self.__GFBLOC_Structure)
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self.Hopping = numpy.zeros([nk,no,no],numpy.complex_) # t(k_index,a,b)
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self.BZ_Points = numpy.zeros([nk,self.dim],numpy.float_) # k(k_index,:)
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self.BZ_weights = numpy.ones([nk],numpy.float_)/ float(nk) # w(k_kindex) , default normalisation
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self.Mu_Pattern = numpy.identity(no,numpy.complex_) if self.orthogonal_basis else numpy.zeros([no,no,nk],numpy.complex_)
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self.Overlap = numpy.array(self.Mu_Pattern, copy=True)
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#-------------------------------------------------------------
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def __get_GFBloc_Structure(self) :
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"""Returns the ONLY block indices accepted for the G and Sigma argument of the
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SumK function"""
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return self.__GFBLOC_Structure
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GFBlocIndices = property(__get_GFBloc_Structure)
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#-------------------------------------------------------------
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def __call__ (self, Sigma, mu=0, eta=0, field=None, epsilon_hat=None, result=None, selected_blocks=()):
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"""
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- Computes :
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result <- \[ \sum_k (\omega + \mu - field - t(k) - Sigma(k,\omega)) \]
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if result is None, it returns a new GF with the results.
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otherwise, result must be a GF, in which the calculation is done, and which is then returned.
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(this allows chain calculation : SK(mu = mu,Sigma = Sigma, result = G).total_density()
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which computes the sumK into G, and returns the density of G.
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- Sigma can be a X, or a function k-> X or a function k,eps ->X where :
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- k is expected to be a 1d-numpy array of size self.dim of float,
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containing the k vector in the basis of the RBZ (i.e. -0.5< k_i <0.5)
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- eps is t(k)
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- X is anything such that X[BlockName] can be added/subtracted to a GFBloc for BlockName in selected_blocks.
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e.g. X can be a BlockGf(with at least the selected_blocks), or a dictionnary Blockname -> array
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if the array has the same dimension as the GF blocks (for example to add a static Sigma).
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- field : Any k independant object to be added to the GF
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- epsilon_hat : a function of eps_k returning a matrix, the dimensions of Sigma
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- selected_blocks : The calculation is done with the SAME t(k) for all blocks. If this list is not None
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only the blocks in this list are calculated.
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e.g. G and Sigma have block indices 'up' and 'down'.
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if selected_blocks ==None : 'up' and 'down' are calculated
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if selected_blocks == ['up'] : only 'up' is calculated. 'down' is 0.
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"""
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S = Sigma.view_selected_blocks(selected_blocks) if selected_blocks else Sigma
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Gres = result if result else Sigma.copy()
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G = Gres.view_selected_blocks(selected_blocks) if selected_blocks else Gres
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# check input
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assert self.orthogonal_basis, "Local_G : must be orthogonal. non ortho cases not checked."
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assert isinstance(G,BlockGf), "G must be a BlockGf"
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assert len(list(set([g.N1 for i,g in G]))) == 1
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assert self.BZ_weights.shape[0] == self.n_kpts(), "Internal Error"
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no = list(set([g.N1 for i,g in G]))[0]
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Sigma_Nargs = len(inspect.getargspec(Sigma)[0]) if callable (Sigma) else 0
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assert Sigma_Nargs <=2 , "Sigma function is not of the correct type. See Documentation"
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# Initialize
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G.zero()
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tmp,tmp2 = G.copy(),G.copy()
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mupat = mu * numpy.identity(no, numpy.complex_)
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tmp <<= iOmega_n
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if field != None : tmp -= field
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if Sigma_Nargs==0: tmp -= Sigma # substract Sigma once for all
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# Loop on k points...
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for w, k, eps_k in izip(*[mpi.slice_array(A) for A in [self.BZ_weights, self.BZ_Points, self.Hopping]]):
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eps_hat = epsilon_hat(eps_k) if epsilon_hat else eps_k
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tmp2 <<= tmp
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tmp2 -= tmp2.n_blocks * [eps_hat - mupat]
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if Sigma_Nargs == 1: tmp2 -= Sigma (k)
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elif Sigma_Nargs ==2: tmp2 -= Sigma (k,eps_k)
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tmp2.invert()
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tmp2 *= w
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G += tmp2
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G <<= mpi.all_reduce(mpi.world,G,lambda x,y : x+y)
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mpi.barrier()
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return Gres
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#-------------------------------------------------------------
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def n_kpts(self) :
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""" Returns the number of k points"""
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return self.BZ_Points.shape[0]
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