def __init__(self,Beta,GFstruct,N_Matsubara_Frequencies=1025,**param):
"""
:param Beta: The inverse temperature
:param GFstruct: The structure of the Green's functions. It must be a list of tuples,
each representing a block of the Green's function. The tuples have two
elements, the name of the block and a list of indices. For example:
[ ('up', [1,2,3]), ('down', [1,2,3]) ].
:param N_Matsubara_Frequencies: (Optional, default = 1025) How many Matsubara frequencies
are used for the Green's functions.
:param param: A list of extra parameters, described below.
"""
# For backward compatibility
self.update_params(param)
Parameters.check_no_parameters_not_in_union_of_dicts(param, self.Required, self.Optional)
Solver_Base.__init__(self,GFstruct,param)
self.Beta = float(Beta)
self.Verbosity = 2 if MPI.rank ==0 else 0
# Green function in frequencies
a_list = [a for a,al in self.GFStruct]
glist = [ GFBloc_ImFreq(Indices = al, Beta = self.Beta, NFreqMatsubara=N_Matsubara_Frequencies) for a,al in self.GFStruct]
self.G0 = GF(NameList = a_list, BlockList = glist, Copy=False, Name="G0")
self.G = GF(Name_Block_Generator = self.G0, Copy=True, Name="G")
self.F = GF(Name_Block_Generator = self.G0, Copy=True, Name="F")
self.Sigma = GF(Name_Block_Generator = self.G0, Copy=True, Name="Sigma")
self.Sigma_Old = GF(Name_Block_Generator = self.G0, Copy=True, Name="Sigma_Old")
self.Name = 'Hybridization Expansion'
# first check that all indices of the Green Function do correspond to a C operator.
for a,alpha_list in self.GFStruct :
for alpha in alpha_list :
if (a,alpha) not in Operators.ClistNames() :
raise "Error : Some indices (%s,%s) of the Green function do not correspond to existing operator"%(a,alpha)
def __init__(self,Beta,GFstruct,N_Matsubara_Frequencies=1025,**param):
"""
:param Beta: The inverse temperature
:param GFstruct: The structure of the Green's functions. It must be a list of tuples,
each representing a block of the Green's function. The tuples have two
elements, the name of the block and a list of indices. For example:
[ ('up', [1,2,3]), ('down', [1,2,3]) ].
:param N_Matsubara_Frequencies: (Optional, default = 1025) How many Matsubara frequencies
are used for the Green's functions.
:param param: A list of extra parameters, described below.
"""
# For backward compatibility
self.update_params(param)
Parameters.check_no_parameters_not_in_union_of_dicts(param, self.Required, self.Optional)
Solver_Base.__init__(self,GFstruct,param)
self.Beta = float(Beta)
self.Verbosity = 1 if MPI.rank ==0 else 0
# Green function in frequencies
a_list = [a for a,al in self.GFStruct]
glist = [ GFBloc_ImFreq(Indices = al, Beta = self.Beta, NFreqMatsubara=N_Matsubara_Frequencies) for a,al in self.GFStruct]
self.G0 = GF(NameList = a_list, BlockList = glist, Copy=False, Name="G0")
self.G = GF(Name_Block_Generator = self.G0, Copy=True, Name="G")
self.F = GF(Name_Block_Generator = self.G0, Copy=True, Name="F")
self.Sigma = GF(Name_Block_Generator = self.G0, Copy=True, Name="Sigma")
self.Sigma_Old = GF(Name_Block_Generator = self.G0, Copy=True, Name="Sigma_Old")
self.Name = 'Hybridization Expansion'
# first check that all indices of the Green Function do correspond to a C operator.
for a,alpha_list in self.GFStruct :
for alpha in alpha_list :
if (a,alpha) not in Operators.ClistNames() :
raise "Error : Some indices (%s,%s) of the Green function do not correspond to existing operator"%(a,alpha)
def __init__(self,Beta,GFstruct,**param):
self.Beta = float(Beta)
Parameters.check_no_parameters_not_in_union_of_dicts (param,self.Required, self.Optional)
#DataTestTools.EnsureAllParametersMakeSense(self,param)
if 'Nmsb' not in param : param['Nmsb'] = 1025
if 'Nspin' not in param : param['Nspin'] = 2
Solver_Base.__init__(self,GFstruct,param)
# construct Greens functions:
self.a_list = [a for a,al in self.GFStruct]
glist = lambda : [ GFBloc_ImFreq(Indices = al, Beta = self.Beta, NFreqMatsubara = self.Nmsb) for a,al in self.GFStruct]
self.G = GF(NameList = self.a_list, BlockList = glist(),Copy=False)
self.G_Old = self.G.copy()
self.G0 = self.G.copy()
self.Sigma = self.G.copy()
self.Sigma_Old = self.G.copy()
M = [x for x in self.G.mesh]
self.zmsb = numpy.array([x for x in M],numpy.complex_)
# for the tails:
self.tailtempl={}
for sig,g in self.G:
self.tailtempl[sig] = copy.deepcopy(g._tail)
for i in range(11): self.tailtempl[sig][i].array[:] *= 0.0
self.Name=''
# effective atomic levels:
if self.UseSpinOrbit: self.NSpin=2
self.ealmat = numpy.zeros([self.Nlm*self.Nspin,self.Nlm*self.Nspin],numpy.complex_)
def Solve(self):
""" Solve the impurity problem """
# Find if an operator is in oplist
def mysearch(op):
l = [ k for (k,v) in OPdict.items() if (v-op).is_zero()]
assert len(l) <=1
return l[0] if l else None
# Same but raises an error if pb
def myfind(op):
r = mysearch(op)
if r==None : raise "Operator %s can not be found by myfind !"%r
return r
# For backward compatibility
self.update_params(self.__dict__)
# Test all a parameters before solutions
MPI.report(Parameters.check(self.__dict__,self.Required,self.Optional))
# We have to add the Hamiltonian the epsilon part of G0
if type(self.H_Local) != type(Operator()) : raise "H_Local is not an operator"
H = self.H_Local
for a,alpha_list in self.GFStruct :
for mu in alpha_list :
for nu in alpha_list :
H += real(self.G0[a]._tail[2][mu,nu]) * Cdag(a,mu)*C(a,nu)
OPdict = {"Hamiltonian": H}
MPI.report("Hamiltonian with Eps0 term : ",H)
# First separate the quantum Numbers that are operators and those which are symmetries.
QuantumNumberOperators = dict( (n,op) for (n,op) in self.Quantum_Numbers.items() if type(op) == type(Operator()))
QuantumNumberSymmetries = dict( (n,op) for (n,op) in self.Quantum_Numbers.items() if type(op) != type(Operator()))
# Check that the quantum numbers commutes with the Hamiltonian
for name,op in QuantumNumberOperators.items():
assert Commutator(self.H_Local ,op).is_zero(), "One quantum number is not commuting with Hamiltonian"
OPdict[name]=op
# Complete the OPdict with the fundamental operators
OPdict, nf, nb, SymChar, NameOpFundamentalList = Operators.Complete_OperatorsList_with_Fundamentals(OPdict)
# Add the operators to be averaged in OPdict and prepare the list for the C-code
self.Measured_Operators_Results = {}
self.twice_defined_Ops = {}
self.Operators_To_Average_List = []
for name, op in self.Measured_Operators.items():
opn = mysearch(op)
if opn == None :
OPdict[name] = op
self.Measured_Operators_Results[name] = 0.0
self.Operators_To_Average_List.append(name)
else:
MPI.report("Operator %s already defined as %s, using this instead for measuring"%(name,opn))
self.twice_defined_Ops[name] = opn
self.Measured_Operators_Results[opn] = 0.0
if opn not in self.Operators_To_Average_List: self.Operators_To_Average_List.append(opn)
# Time correlation functions are added
self.OpCorr_To_Average_List = []
for name, op in self.Measured_Time_Correlators.items():
opn = mysearch(op[0])
if opn == None :
OPdict[name] = op[0]
self.OpCorr_To_Average_List.append(name)
else:
MPI.report("Operator %s already defined as %s, using this instead for measuring"%(name,opn))
if opn not in self.OpCorr_To_Average_List: self.OpCorr_To_Average_List.append(opn)
# Create storage for data:
Nops = len(self.OpCorr_To_Average_List)
f = lambda L : GFBloc_ImTime(Indices= [0], Beta = self.Beta, NTimeSlices=L )
if (Nops>0):
self.Measured_Time_Correlators_Results = GF(Name_Block_Generator = [ ( n,f(self.Measured_Time_Correlators[n][1]) ) for n in self.Measured_Time_Correlators], Copy=False)
else:
self.Measured_Time_Correlators_Results = GF(Name_Block_Generator = [ ( 'OpCorr',f(2) ) ], Copy=False)
# Take care of the global moves
# First, given a function (a,alpha,dagger) -> (a', alpha', dagger')
# I construct a function on fundamental operators
def Map_GM_to_Fund_Ops( GM ) :
def f(fop) :
a,alpha, dagger = fop.name + (fop.dag,)
ap,alphap,daggerp = GM((a,alpha,dagger))
return Cdag(ap,alphap) if daggerp else C(ap,alphap)
return f
# Complete the OpList so that it is closed under the global moves
while 1:
added_something = False
for n,(proba,GM) in enumerate(self.Global_Moves):
# F is a function that map all operators according to the global move
F = Extend_Function_on_Fundamentals(Map_GM_to_Fund_Ops(GM))
# Make sure that OPdict is complete, i.e. all images of OPdict operators are in OPdict
for name,op in OPdict.items() :
op_im = F(op)
if mysearch(op_im)==None :
# find the key and put in in the dictionnary
i=0
while 1:
new_name = name + 'GM' + i*'_' + "%s"%n
if new_name not in OPdict : break
added_something = True
OPdict[new_name] = op_im
# break the while loop
if not added_something: break
# Now I have all operators, I make the transcription of the global moves
self.Global_Moves_Mapping_List = []
for n,(proba,GM) in enumerate(self.Global_Moves):
F = Extend_Function_on_Fundamentals(Map_GM_to_Fund_Ops(GM))
m = {}
for name,op in OPdict.items() :
op_im = F(op)
n1,n2 = myfind(op),myfind(op_im)
m[n1] = n2
name = "%s"%n
self.Global_Moves_Mapping_List.append((proba,m,name))
#MPI.report ("Global_Moves_Mapping_List", self.Global_Moves_Mapping_List)
# Now add the operator for F calculation if needed
if self.Use_F :
Hloc_WithoutQuadratic = self.H_Local.RemoveQuadraticTerms()
for n,op in OPdict.items() :
if op.is_Fundamental():
op2 = Commutator(Hloc_WithoutQuadratic,op)
if not mysearch(op2) : OPdict["%s_Comm_Hloc"%n] = op2
# All operators have real coefficients. Check this and remove the 0j term
# since the C++ expects operators with real numbers
for n,op in OPdict.items(): op.make_coef_real_and_check()
# Transcription of operators for C++
Oplist2 = Operators.Transcribe_OpList_for_C(OPdict)
SymList = [sym for (n,sym) in SymChar.items() if n in QuantumNumberSymmetries]
self.H_diag = C_Module.Hloc(nf,nb,Oplist2,QuantumNumberOperators,SymList,self.Quantum_Numbers_Selection,0)
# Create the C_Cag_Ops array which describes the grouping of (C,Cdagger) operator
# for the MonteCarlo moves : (a, alpha) block structure [ [ (C_name, Cdag_name)]]
self.C_Cdag_Ops = [ [ (myfind(C(a,alpha)), myfind(Cdag(a,alpha))) for alpha in al ] for a,al in self.GFStruct]
# Define G0_inv and correct it to have G0 to have perfect 1/omega behavior
self.G0_inv = inverse(self.G0)
Delta = self.G0_inv.Delta()
for n,g in self.G0_inv:
assert(g.N1==g.N2)
identity=numpy.identity(g.N1)
self.G0[n] <<= GF_Initializers.A_Omega_Plus_B(identity, g._tail[0])
self.G0[n] -= Delta[n]
#self.G0[n] <<= iOmega_n + g._tail[0] - Delta[n]
self.G0_inv <<= self.G0
self.G0.invert()
# Construct the function in tau
f = lambda g,L : GFBloc_ImTime(Indices= g.Indices, Beta = g.Beta, NTimeSlices=L )
self.Delta_tau = GF(Name_Block_Generator = [ (n,f(g,self.N_Time_Slices_Delta) ) for n,g in self.G], Copy=False, Name='D')
self.G_tau = GF(Name_Block_Generator = [ (n,f(g,self.N_Time_Slices_Gtau) ) for n,g in self.G], Copy=False, Name='G')
self.F_tau = GF(Name_Block_Generator = self.G_tau, Copy=True, Name='F')
for (i,gt) in self.Delta_tau : gt.setFromInverseFourierOf(Delta[i])
MPI.report("Inv Fourier done")
if (self.Legendre_Accumulation):
self.G_Legendre = GF(Name_Block_Generator = [ (n,GFBloc_ImLegendre(Indices=g.Indices, Beta=g.Beta, NLegendreCoeffs=self.N_Legendre_Coeffs) ) for n,g in self.G], Copy=False, Name='Gl')
else:
self.G_Legendre = GF(Name_Block_Generator = [ (n,GFBloc_ImLegendre(Indices=[1], Beta=g.Beta, NLegendreCoeffs=1) ) for n,g in self.G], Copy=False, Name='Gl') # G_Legendre must not be empty but is not needed in this case. So I make it as small as possible.
# Starting the C++ code
self.Sigma_Old <<= self.Sigma
C_Module.MC_solve(self.__dict__ ) # C++ solver
# Compute G on Matsubara axis possibly fitting the tail
if self.Legendre_Accumulation:
for s,g in self.G:
identity=numpy.zeros([g.N1,g.N2],numpy.float)
for i,m in enumerate (g._IndicesL):
for j,n in enumerate (g._IndicesR):
if m==n: identity[i,j]=1
self.G_Legendre[s].enforce_discontinuity(identity) # set the known tail
g <<= LegendreToMatsubara(self.G_Legendre[s])
else:
if (self.Time_Accumulation):
for name, g in self.G_tau:
identity=numpy.zeros([g.N1,g.N2],numpy.float)
for i,m in enumerate (g._IndicesL):
for j,n in enumerate (g._IndicesR):
if m==n: identity[i,j]=1
g._tail.zero()
g._tail[1] = identity
self.G[name].setFromFourierOf(g)
# This is very sick... but what can we do???
self.Sigma <<= self.G0_inv - inverse(self.G)
self.fitTails()
self.G <<= inverse(self.G0_inv - self.Sigma)
# Now find the self-energy
self.Sigma <<= self.G0_inv - inverse(self.G)
MPI.report("Solver %(Name)s has ended."%self.__dict__)
# for operator averages: if twice defined operator, rename output:
for op1,op2 in self.twice_defined_Ops.items():
self.Measured_Operators_Results[op1] = self.Measured_Operators_Results[op2]
for op1,op2 in self.twice_defined_Ops.items():
if op2 in self.Measured_Operators_Results.keys(): del self.Measured_Operators_Results[op2]
if self.Use_F :
for (n,f) in self.F: f.setFromFourierOf(self.F_tau[n])
self.G2 = self.G0 + self.G0 * self.F
self.Sigma2 = self.F * inverse(self.G2)
def Solve(self):
""" Solve the impurity problem """
# Find if an operator is in oplist
def mysearch(op):
l = [ k for (k,v) in OPdict.items() if (v-op).is_zero()]
assert len(l) <=1
return l[0] if l else None
# Same but raises an error if pb
def myfind(op):
r = mysearch(op)
if r==None : raise "Operator %s can not be found by myfind !"%r
return r
# For backward compatibility
self.update_params(self.__dict__)
# Test all a parameters before solutions
MPI.report(Parameters.check(self.__dict__,self.Required,self.Optional))
# We have to add the Hamiltonian the epsilon part of G0
if type(self.H_Local) != type(Operator()) : raise "H_Local is not an operator"
H = self.H_Local
for a,alpha_list in self.GFStruct :
for mu in alpha_list :
for nu in alpha_list :
H += real(self.G0[a]._tail[2][mu,nu]) * Cdag(a,mu)*C(a,nu)
OPdict = {"Hamiltonian": H}
MPI.report("Hamiltonian with Eps0 term : ",H)
# First separate the quantum Numbers that are operators and those which are symmetries.
QuantumNumberOperators = dict( (n,op) for (n,op) in self.Quantum_Numbers.items() if type(op) == type(Operator()))
QuantumNumberSymmetries = dict( (n,op) for (n,op) in self.Quantum_Numbers.items() if type(op) != type(Operator()))
# Check that the quantum numbers commutes with the Hamiltonian
for name,op in QuantumNumberOperators.items():
assert Commutator(self.H_Local ,op).is_zero(), "One quantum number is not commuting with Hamiltonian"
OPdict[name]=op
# Complete the OPdict with the fundamental operators
OPdict, nf, nb, SymChar, NameOpFundamentalList = Operators.Complete_OperatorsList_with_Fundamentals(OPdict)
# Add the operators to be averaged in OPdict and prepare the list for the C-code
self.Measured_Operators_Results = {}
self.twice_defined_Ops = {}
self.Operators_To_Average_List = []
for name, op in self.Measured_Operators.items():
opn = mysearch(op)
if opn == None :
OPdict[name] = op
self.Measured_Operators_Results[name] = 0.0
self.Operators_To_Average_List.append(name)
else:
MPI.report("Operator %s already defined as %s, using this instead for measuring"%(name,opn))
self.twice_defined_Ops[name] = opn
self.Measured_Operators_Results[opn] = 0.0
if opn not in self.Operators_To_Average_List: self.Operators_To_Average_List.append(opn)
# Time correlation functions are added
self.OpCorr_To_Average_List = []
for name, op in self.Measured_Time_Correlators.items():
opn = mysearch(op[0])
if opn == None :
OPdict[name] = op[0]
self.OpCorr_To_Average_List.append(name)
else:
MPI.report("Operator %s already defined as %s, using this instead for measuring"%(name,opn))
if opn not in self.OpCorr_To_Average_List: self.OpCorr_To_Average_List.append(opn)
# Create storage for data:
Nops = len(self.OpCorr_To_Average_List)
f = lambda L : GFBloc_ImTime(Indices= [0], Beta = self.Beta, NTimeSlices=L )
if (Nops>0):
self.Measured_Time_Correlators_Results = GF(Name_Block_Generator = [ ( n,f(self.Measured_Time_Correlators[n][1]) ) for n in self.Measured_Time_Correlators], Copy=False)
else:
self.Measured_Time_Correlators_Results = GF(Name_Block_Generator = [ ( 'OpCorr',f(2) ) ], Copy=False)
# Take care of the global moves
# First, given a function (a,alpha,dagger) -> (a', alpha', dagger')
# I construct a function on fundamental operators
def Map_GM_to_Fund_Ops( GM ) :
def f(fop) :
a,alpha, dagger = fop.name + (fop.dag,)
ap,alphap,daggerp = GM((a,alpha,dagger))
return Cdag(ap,alphap) if daggerp else C(ap,alphap)
return f
# Complete the OpList so that it is closed under the global moves
while 1:
added_something = False
for n,(proba,GM) in enumerate(self.Global_Moves):
# F is a function that map all operators according to the global move
F = Extend_Function_on_Fundamentals(Map_GM_to_Fund_Ops(GM))
# Make sure that OPdict is complete, i.e. all images of OPdict operators are in OPdict
for name,op in OPdict.items() :
op_im = F(op)
if mysearch(op_im)==None :
# find the key and put in in the dictionnary
i=0
while 1:
new_name = name + 'GM' + i*'_' + "%s"%n
if new_name not in OPdict : break
added_something = True
OPdict[new_name] = op_im
# break the while loop
if not added_something: break
# Now I have all operators, I make the transcription of the global moves
self.Global_Moves_Mapping_List = []
for n,(proba,GM) in enumerate(self.Global_Moves):
F = Extend_Function_on_Fundamentals(Map_GM_to_Fund_Ops(GM))
m = {}
for name,op in OPdict.items() :
op_im = F(op)
n1,n2 = myfind(op),myfind(op_im)
m[n1] = n2
name = "%s"%n
self.Global_Moves_Mapping_List.append((proba,m,name))
#MPI.report ("Global_Moves_Mapping_List", self.Global_Moves_Mapping_List)
# Now add the operator for F calculation if needed
if self.Use_F :
Hloc_WithoutQuadratic = self.H_Local.RemoveQuadraticTerms()
for n,op in OPdict.items() :
if op.is_Fundamental():
op2 = Commutator(Hloc_WithoutQuadratic,op)
if not mysearch(op2) : OPdict["%s_Comm_Hloc"%n] = op2
# All operators have real coefficients. Check this and remove the 0j term
# since the C++ expects operators with real numbers
for n,op in OPdict.items(): op.make_coef_real_and_check()
# Transcription of operators for C++
Oplist2 = Operators.Transcribe_OpList_for_C(OPdict)
SymList = [sym for (n,sym) in SymChar.items() if n in QuantumNumberSymmetries]
self.H_diag = C_Module.Hloc(nf,nb,Oplist2,QuantumNumberOperators,SymList,self.Quantum_Numbers_Selection,0)
# Create the C_Cag_Ops array which describes the grouping of (C,Cdagger) operator
# for the MonteCarlo moves : (a, alpha) block structure [ [ (C_name, Cdag_name)]]
self.C_Cdag_Ops = [ [ (myfind(C(a,alpha)), myfind(Cdag(a,alpha))) for alpha in al ] for a,al in self.GFStruct]
# Define G0_inv and correct it to have G0 to have perfect 1/omega behavior
self.G0_inv = inverse(self.G0)
Delta = self.G0_inv.Delta()
for n,g in self.G0_inv:
assert(g.N1==g.N2)
identity=numpy.identity(g.N1)
self.G0[n] <<= GF_Initializers.A_Omega_Plus_B(identity, g._tail[0])
self.G0[n] -= Delta[n]
#self.G0[n] <<= iOmega_n + g._tail[0] - Delta[n]
self.G0_inv <<= self.G0
self.G0.invert()
# Construct the function in tau
f = lambda g,L : GFBloc_ImTime(Indices= g.Indices, Beta = g.Beta, NTimeSlices=L )
self.Delta_tau = GF(Name_Block_Generator = [ (n,f(g,self.N_Time_Slices_Delta) ) for n,g in self.G], Copy=False, Name='D')
self.G_tau = GF(Name_Block_Generator = [ (n,f(g,self.N_Time_Slices_Gtau) ) for n,g in self.G], Copy=False, Name='G')
self.F_tau = GF(Name_Block_Generator = self.G_tau, Copy=True, Name='F')
for (i,gt) in self.Delta_tau : gt.setFromInverseFourierOf(Delta[i])
MPI.report("Inv Fourier done")
if (self.Legendre_Accumulation):
self.G_Legendre = GF(Name_Block_Generator = [ (n,GFBloc_ImLegendre(Indices=g.Indices, Beta=g.Beta, NLegendreCoeffs=self.N_Legendre_Coeffs) ) for n,g in self.G], Copy=False, Name='Gl')
else:
self.G_Legendre = GF(Name_Block_Generator = [ (n,GFBloc_ImLegendre(Indices=[1], Beta=g.Beta, NLegendreCoeffs=1) ) for n,g in self.G], Copy=False, Name='Gl') # G_Legendre must not be empty but is not needed in this case. So I make it as small as possible.
# Starting the C++ code
self.Sigma_Old <<= self.Sigma
C_Module.MC_solve(self.__dict__ ) # C++ solver
# Compute G on Matsubara axis possibly fitting the tail
if self.Legendre_Accumulation:
for s,g in self.G:
identity=numpy.zeros([g.N1,g.N2],numpy.float)
for i,m in enumerate (g._IndicesL):
for j,n in enumerate (g._IndicesR):
if m==n: identity[i,j]=1
self.G_Legendre[s].enforce_discontinuity(identity) # set the known tail
g <<= LegendreToMatsubara(self.G_Legendre[s])
else:
if (self.Time_Accumulation):
for name, g in self.G_tau:
identity=numpy.zeros([g.N1,g.N2],numpy.float)
for i,m in enumerate (g._IndicesL):
for j,n in enumerate (g._IndicesR):
if m==n: identity[i,j]=1
g._tail.zero()
g._tail[1] = identity
self.G[name].setFromFourierOf(g)
# This is very sick... but what can we do???
self.Sigma <<= self.G0_inv - inverse(self.G)
self.fitTails()
self.G <<= inverse(self.G0_inv - self.Sigma)
# Now find the self-energy
self.Sigma <<= self.G0_inv - inverse(self.G)
MPI.report("Solver %(Name)s has ended."%self.__dict__)
# for operator averages: if twice defined operator, rename output:
for op1,op2 in self.twice_defined_Ops.items():
self.Measured_Operators_Results[op1] = self.Measured_Operators_Results[op2]
for op1,op2 in self.twice_defined_Ops.items():
if op2 in self.Measured_Operators_Results.keys(): del self.Measured_Operators_Results[op2]
if self.Use_F :
for (n,f) in self.F: f.setFromFourierOf(self.F_tau[n])
self.G2 = self.G0 + self.G0 * self.F
self.Sigma2 = self.F * inverse(self.G2)
def Solve(self,Iteration_Number=1,Test_Convergence=0.0001):
"""Calculation of the impurity Greens function using Hubbard-I"""
# Test all a parameters before solutions
print Parameters.check(self.__dict__,self.Required,self.Optional)
#Solver_Base.Solve(self,is_last_iteration,Iteration_Number,Test_Convergence)
if self.Converged :
MPI.report("Solver %(Name)s has already converted: SKIPPING"%self.__dict__)
return
self.__save_eal('eal.dat',Iteration_Number)
MPI.report( "Starting Fortran solver %(Name)s"%self.__dict__)
self.Sigma_Old <<= self.Sigma
self.G_Old <<= self.G
# call the fortran solver:
temp = 1.0/self.Beta
gf,tail,self.atocc,self.atmag = gf_hi_fullu(e0f=self.ealmat, ur=self.ur, umn=self.umn, ujmn=self.ujmn,
zmsb=self.zmsb, nmom=self.Nmoments, ns=self.Nspin, temp=temp, verbosity = self.Verbosity)
#self.sig = sigma_atomic_fullu(gf=self.gf,e0f=self.eal,zmsb=self.zmsb,ns=self.Nspin,nlm=self.Nlm)
if (self.Verbosity==0):
# No fortran output, so give basic results here
MPI.report("Atomic occupancy in Hubbard I Solver : %s"%self.atocc)
MPI.report("Atomic magn. mom. in Hubbard I Solver : %s"%self.atmag)
# transfer the data to the GF class:
if (self.UseSpinOrbit):
nlmtot = self.Nlm*2 # only one block in this case!
else:
nlmtot = self.Nlm
M={}
isp=-1
for a,al in self.GFStruct:
isp+=1
#M[a] = gf[isp*self.Nlm:(isp+1)*self.Nlm,isp*self.Nlm:(isp+1)*self.Nlm,:]
M[a] = gf[isp*nlmtot:(isp+1)*nlmtot,isp*nlmtot:(isp+1)*nlmtot,:]
for i in range(min(self.Nmoments,10)):
self.tailtempl[a][i+1].array[:] = tail[i][isp*nlmtot:(isp+1)*nlmtot,isp*nlmtot:(isp+1)*nlmtot]
glist = lambda : [ GFBloc_ImFreq(Indices = al, Beta = self.Beta, NFreqMatsubara = self.Nmsb, Data=M[a], Tail=self.tailtempl[a])
for a,al in self.GFStruct]
self.G = GF(NameList = self.a_list, BlockList = glist(),Copy=False)
# Self energy:
self.G0 <<= GF_Initializers.A_Omega_Plus_B(A=1,B=0.0)
M = [ self.ealmat[isp*nlmtot:(isp+1)*nlmtot,isp*nlmtot:(isp+1)*nlmtot] for isp in range((2*self.Nlm)/nlmtot) ]
self.G0 -= M
self.Sigma <<= self.G0 - inverse(self.G)
# invert G0
self.G0.invert()
def test_distance(G1,G2, dist) :
def f(G1,G2) :
print abs(G1._data.array - G2._data.array)
dS = max(abs(G1._data.array - G2._data.array).flatten())
aS = max(abs(G1._data.array).flatten())
return dS <= aS*dist
return reduce(lambda x,y : x and y, [f(g1,g2) for (i1,g1),(i2,g2) in izip(G1,G2)])
MPI.report("\nChecking Sigma for convergence...\nUsing tolerance %s"%Test_Convergence)
self.Converged = test_distance(self.Sigma,self.Sigma_Old,Test_Convergence)
if self.Converged :
MPI.report("Solver HAS CONVERGED")
else :
MPI.report("Solver has not yet converged")