def __init__(self, S, BuildMasterOnly =True) :
"""
Construction : with named arguments only.
Possible constructors from source S :
- Parameters(d) : d is a dict to be copied (no deepcopy, just updated).
- Parameters(f) : f is a string containing the path of a file
The type of the file is determined from the extension:
- .py, .txt : the file is executed in the Parameters
- .xml : to be written
MPI : if BuildMasterOnly is true, the contruction is only made on the master,
the result is then bcasted to all the nodes.
Otherwise, it is done on all nodes (not recommended to read files).
"""
if MPI.IS_MASTER_NODE() or not BuildMasterOnly :
if type(S) == type(''):
# detect the type of the file
try :
extension = S.split('.')[-1].lower()
except IndexError:
raise ValueError, "I am lost : I can not determine the extension of the file !"
if extension in ['py','txt'] : execfile(S,{},self)
else : raise ValueError, "Extension of the file not recognized"
else : # S is therefore a dict
try :
self.update(S)
except :
print "Error in Parameter constructor. Is the source an iterable ?"
raise
# end of master only
if BuildMasterOnly : MPI.bcast(self) # bcast it on the nodes
def update_params(self, d):
allparams = [
('QMC_N_cycles_MAX', 'N_Cycles'),
('NCycles', 'N_Cycles'),
('Hloc', 'H_Local'),
('QuantumNumbers', 'Quantum_Numbers'),
('Length_One_QMC_Cycle', 'Length_Cycle'),
('Number_Warming_Iteration', 'N_Warmup_Cycles'),
('Number_Frequencies_Accumulated', 'N_Frequencies_Accumulated'),
('Global_Move', 'Global_Moves'),
('UseSegmentPicture', 'Use_Segment_Picture'),
('Proba_Move_Insert_Remove_Kink', 'Proba_Insert_Remove'),
('Proba_Move_Move_Kink', 'Proba_Move'),
('OperatorsToAverage', 'Measured_Operators'),
('OpCorrToAverage', 'Measured_Time_Correlators'),
('KeepGF_MC_series', 'Keep_Full_MC_Series'),
('DecorrelationAnalysisG_NFreq', 'Decorrelation_Analysis_G_NFreq'),
('RecordStatisticConfigurations', 'Record_Statistics_Configurations')
]
issue_warning = False
for (old, new) in allparams:
if old in d:
val = d.pop(old)
d.update({new:val})
issue_warning = True
msg = """
**********************************************************************************
Warning: some parameters you used have a different name now and will be
deprecated in future versions. Please check the documentation.
**********************************************************************************
"""
if issue_warning: MPI.report(msg)
def update_params(self, d):
allparams = [
('QMC_N_cycles_MAX', 'N_Cycles'),
('NCycles', 'N_Cycles'),
('Hloc', 'H_Local'),
('QuantumNumbers', 'Quantum_Numbers'),
('Length_One_QMC_Cycle', 'Length_Cycle'),
('Number_Warming_Iteration', 'N_Warmup_Cycles'),
('Number_Frequencies_Accumulated', 'N_Frequencies_Accumulated'),
('Global_Move', 'Global_Moves'),
('UseSegmentPicture', 'Use_Segment_Picture'),
('Proba_Move_Insert_Remove_Kink', 'Proba_Insert_Remove'),
('Proba_Move_Move_Kink', 'Proba_Move'),
('OperatorsToAverage', 'Measured_Operators'),
('OpCorrToAverage', 'Measured_Time_Correlators'),
('KeepGF_MC_series', 'Keep_Full_MC_Series'),
('DecorrelationAnalysisG_NFreq', 'Decorrelation_Analysis_G_NFreq'),
('RecordStatisticConfigurations', 'Record_Statistics_Configurations')
]
issue_warning = False
for (old, new) in allparams:
if old in d:
val = d.pop(old)
d.update({new:val})
issue_warning = True
msg = """
**********************************************************************************
Warning: some parameters you used have a different name now and will be
deprecated in future versions. Please check the documentation.
**********************************************************************************
"""
if issue_warning: MPI.report(msg)
def __init__(self, S, BuildMasterOnly=True):
"""
Construction : with named arguments only.
Possible constructors from source S :
- Parameters(d) : d is a dict to be copied (no deepcopy, just updated).
- Parameters(f) : f is a string containing the path of a file
The type of the file is determined from the extension:
- .py, .txt : the file is executed in the Parameters
- .xml : to be written
MPI : if BuildMasterOnly is true, the contruction is only made on the master,
the result is then bcasted to all the nodes.
Otherwise, it is done on all nodes (not recommended to read files).
"""
if MPI.IS_MASTER_NODE() or not BuildMasterOnly:
if type(S) == type(''):
# detect the type of the file
try:
extension = S.split('.')[-1].lower()
except IndexError:
raise ValueError, "I am lost : I can not determine the extension of the file !"
if extension in ['py', 'txt']: execfile(S, {}, self)
else: raise ValueError, "Extension of the file not recognized"
else: # S is therefore a dict
try:
self.update(S)
except:
print "Error in Parameter constructor. Is the source an iterable ?"
raise
# end of master only
if BuildMasterOnly: MPI.bcast(self) # bcast it on the nodes
def HT(Res) :
# First compute the eps_hat array
eps_hat = Epsilon_Hat(self.dos.eps) if Epsilon_Hat else numpy.array( [ x* numpy.identity (Sigma.N1) for x in self.dos.eps] )
assert eps_hat.shape[0] == self.dos.eps.shape[0],"Epsilon_Hat function behaves incorrectly"
assert eps_hat.shape[1] == eps_hat.shape[2],"Epsilon_Hat function behaves incorrectly (result not a square matrix)"
assert Sigma.N1 == eps_hat.shape[1], "Size of Sigma and of epsilon_hat mismatch"
Res.zero()
Sigma_fnt = callable(Sigma)
if Sigma_fnt : assert len(inspect.getargspec(Sigma)[0]) ==1, "Sigma function is not of the correct type. See Documentation"
# Perform the sum over eps[i]
tmp,tmp2 = Res.copy(),Res.copy()
tmp <<= GF_Initializers.A_Omega_Plus_B(1,mu + eta * 1j)
if not(Sigma_fnt) :
tmp -= Sigma
if Field != None : tmp -= Field
# I slice all the arrays on the node. Cf reduce operation below.
for d,e_h,e in itertools.izip (*[MPI.slice_array(A) for A in [self.rho_for_sum,eps_hat,self.dos.eps]]):
tmp2.copyFrom(tmp)
tmp2 -= e_h
if Sigma_fnt : tmp2 -= Sigma(e)
tmp2.invert()
tmp2 *= d
Res += tmp2
# sum the Res GF of all nodes and returns the results on all nodes...
# Cf Boost.mpi.python, collective communicator for documentation.
# The point is that Res is pickable, hence can be transmitted between nodes without further code...
Res <<= MPI.all_reduce(MPI.world,Res,lambda x,y : x+y)
MPI.barrier()
def __should_continue(self,N_Iter_SelfCons_Max) :
""" stop test"""
should_continue = True
if MPI.IS_MASTER_NODE():
if (self.Iteration_Number > N_Iter_SelfCons_Max):
should_continue = False
should_continue = MPI.bcast(should_continue)
return should_continue
def simplepointdensmat(self):
ntoi = self.names_to_ind[self.SO]
bln = self.blocnames[self.SO]
MMat = [numpy.zeros( [self.N_Orbitals[0][ntoi[bl]],self.N_Orbitals[0][ntoi[bl]]], numpy.complex_) for bl in bln]
densmat = [ {} for icrsh in xrange(self.N_corr_shells)]
for icrsh in xrange(self.N_corr_shells):
for bl in self.blocnames[self.corr_shells[icrsh][4]]:
densmat[icrsh][bl] = numpy.zeros([self.corr_shells[icrsh][3],self.corr_shells[icrsh][3]], numpy.complex_)
ikarray=numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
unchangedsize = all( [ self.N_Orbitals[ik][ntoi[bln[ib]]]==len(MMat[ib])
for ib in range(self.NspinblocsGF[self.SO]) ] )
if (not unchangedsize):
MMat = [numpy.zeros( [self.N_Orbitals[ik][ntoi[bl]],self.N_Orbitals[ik][ntoi[bl]]], numpy.complex_) for bl in bln]
for ibl,bl in enumerate(bln):
ind = ntoi[bl]
for inu in range(self.N_Orbitals[ik][ind]):
if ( (self.Hopping[ik][ind][inu,inu]-self.hfield*(1-2*ibl)) < 0.0):
MMat[ibl][inu,inu] = 1.0
else:
MMat[ibl][inu,inu] = 0.0
for icrsh in range(self.N_corr_shells):
for ibn,bn in enumerate(self.blocnames[self.corr_shells[icrsh][4]]):
isp = self.names_to_ind[self.corr_shells[icrsh][4]][bn]
#print ik, bn, isp
densmat[icrsh][bn] += self.BZ_weights[ik] * numpy.dot( numpy.dot(self.Proj_Mat[ik][isp][icrsh],MMat[ibn]) ,
self.Proj_Mat[ik][isp][icrsh].transpose().conjugate() )
# get data from nodes:
for icrsh in range(self.N_corr_shells):
for sig in densmat[icrsh]:
densmat[icrsh][sig] = MPI.all_reduce(MPI.world,densmat[icrsh][sig],lambda x,y : x+y)
MPI.barrier()
if (self.symm_op!=0): densmat = self.Symm_corr.symmetrise(densmat)
# Rotate to local coordinate system:
if (self.use_rotations):
for icrsh in xrange(self.N_corr_shells):
for bn in densmat[icrsh]:
if (self.rotmat_timeinv[icrsh]==1): densmat[icrsh][bn] = densmat[icrsh][bn].conjugate()
densmat[icrsh][bn] = numpy.dot( numpy.dot(self.rotmat[icrsh].conjugate().transpose(),densmat[icrsh][bn]) ,
self.rotmat[icrsh])
return densmat
def fitTails(self):
"""Fits the tails using the constant value for the Re Sigma calculated from F=Sigma*G.
Works only for blocks of size one."""
#if (len(self.GFStruct)==2*self.Norb):
if (self.blocssizeone):
spinblocs = [v for v in self.map]
MPI.report("Fitting tails manually")
known_coeff = numpy.zeros([1, 1, 2], numpy.float_)
msh = [x.imag for x in self.G[self.map[spinblocs[0]][0]].mesh]
fit_start = msh[self.Fitting_Frequency_Start]
fit_stop = msh[self.N_Frequencies_Accumulated]
# Fit the tail of G just to get the density
for n, g in self.G:
g.fitTail([[[0, 0, 1]]], 7, fit_start, 2 * fit_stop)
densmat = self.G.density()
for sig1 in spinblocs:
for i in range(self.Norb):
coeff = 0.0
for sig2 in spinblocs:
for j in range(self.Norb):
if (sig1 == sig2):
coeff += self.U[self.offset + i, self.offset +
j] * densmat[self.map[sig1]
[j]][0, 0].real
else:
coeff += self.Up[self.offset + i, self.offset +
j] * densmat[self.map[sig2]
[j]][0, 0].real
known_coeff[0, 0, 1] = coeff
self.Sigma[self.map[sig1][i]].fitTail(
fixed_coef=known_coeff,
order_max=3,
fit_start=fit_start,
fit_stop=fit_stop)
else:
for n, sig in self.Sigma:
known_coeff = numpy.zeros([sig.N1, sig.N2, 1], numpy.float_)
msh = [x.imag for x in sig.mesh]
fit_start = msh[self.Fitting_Frequency_Start]
fit_stop = msh[self.N_Frequencies_Accumulated]
sig.fitTail(fixed_coef=known_coeff,
order_max=3,
fit_start=fit_start,
fit_stop=fit_stop)
def extract_Gloc(self, mu=None, withSigma = True):
"""
extracts the local downfolded Green function at the chemical potential of the class.
At the end, the local G is rotated from the gloabl coordinate system to the local system.
if withSigma = False: Sigma is not included => non-interacting local GF
"""
if (mu is None): mu = self.Chemical_Potential
Gloc = [ self.Sigmaimp[icrsh].copy() for icrsh in xrange(self.N_corr_shells) ] # this list will be returned
for icrsh in xrange(self.N_corr_shells): Gloc[icrsh].zero() # initialize to zero
ikarray=numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
S = self.latticeGF_Matsubara(ik=ik,mu=mu,withSigma = withSigma)
S *= self.BZ_weights[ik]
for icrsh in xrange(self.N_corr_shells):
tmp = Gloc[icrsh].copy() # init temporary storage
for sig,gf in tmp: tmp[sig] <<= self.downfold(ik,icrsh,sig,S[sig],gf)
Gloc[icrsh] += tmp
#collect data from MPI:
for icrsh in xrange(self.N_corr_shells):
Gloc[icrsh] <<= MPI.all_reduce(MPI.world,Gloc[icrsh],lambda x,y : x+y)
MPI.barrier()
# Gloc[:] is now the sum over k projected to the local orbitals.
# here comes the symmetrisation, if needed:
if (self.symm_op!=0): Gloc = self.Symm_corr.symmetrise(Gloc)
# Gloc is rotated to the local coordinate system:
if (self.use_rotations):
for icrsh in xrange(self.N_corr_shells):
for sig,gf in Gloc[icrsh]: Gloc[icrsh][sig] <<= self.rotloc(icrsh,gf,direction='toLocal')
# transform to CTQMC blocks:
Glocret = [ GF( Name_Block_Generator = [ (a,GFBloc_ImFreq(Indices = al, Mesh = Gloc[0].mesh)) for a,al in self.GFStruct_Solver[i] ],
Copy = False) for i in xrange(self.N_inequiv_corr_shells) ]
for ish in xrange(self.N_inequiv_corr_shells):
for ibl in range(len(self.GFStruct_Solver[ish])):
for i in range(len(self.GFStruct_Solver[ish][ibl][1])):
for j in range(len(self.GFStruct_Solver[ish][ibl][1])):
bl = self.GFStruct_Solver[ish][ibl][0]
ind1 = self.GFStruct_Solver[ish][ibl][1][i]
ind2 = self.GFStruct_Solver[ish][ibl][1][j]
Glocret[ish][bl][ind1,ind2] <<= Gloc[self.invshellmap[ish]][self.mapinv[ish][bl]][ind1,ind2]
# return only the inequivalent shells:
return Glocret
def density_gf(self, Beta=40):
"""Calculates the density without setting up Gloc. It is useful for Hubbard I, and very fast."""
densmat = [{} for icrsh in xrange(self.N_corr_shells)]
for icrsh in xrange(self.N_corr_shells):
for bl in self.blocnames[self.corr_shells[icrsh][4]]:
densmat[icrsh][bl] = numpy.zeros(
[self.corr_shells[icrsh][3], self.corr_shells[icrsh][3]], numpy.complex_
)
ikarray = numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
Gupf = self.latticeGF_Matsubara(ik=ik, mu=self.Chemical_Potential)
Gupf *= self.BZ_weights[ik]
dm = Gupf.density()
MMat = [dm[bl] for bl in self.blocnames[self.SO]]
for icrsh in range(self.N_corr_shells):
for ibn, bn in enumerate(self.blocnames[self.corr_shells[icrsh][4]]):
isp = self.names_to_ind[self.corr_shells[icrsh][4]][bn]
# print ik, bn, isp
densmat[icrsh][bn] += numpy.dot(
numpy.dot(self.Proj_Mat[ik][isp][icrsh], MMat[ibn]),
self.Proj_Mat[ik][isp][icrsh].transpose().conjugate(),
)
# get data from nodes:
for icrsh in range(self.N_corr_shells):
for sig in densmat[icrsh]:
densmat[icrsh][sig] = MPI.all_reduce(MPI.world, densmat[icrsh][sig], lambda x, y: x + y)
MPI.barrier()
if self.symm_op != 0:
densmat = self.Symm_corr.symmetrise(densmat)
# Rotate to local coordinate system:
if self.use_rotations:
for icrsh in xrange(self.N_corr_shells):
for bn in densmat[icrsh]:
if self.rotmat_timeinv[icrsh] == 1:
densmat[icrsh][bn] = densmat[icrsh][bn].conjugate()
densmat[icrsh][bn] = numpy.dot(
numpy.dot(self.rotmat[icrsh].conjugate().transpose(), densmat[icrsh][bn]), self.rotmat[icrsh]
)
return densmat
def __repack(self):
"""Calls the h5repack routine, in order to reduce the file size of the hdf5 archive.
Should only be used BEFORE the first invokation of HDF_Archive in the program, otherwise
the hdf5 linking is broken!!!"""
import subprocess
if not (MPI.IS_MASTER_NODE()): return
MPI.report("Repacking the file %s"%self.HDFfile)
retcode = subprocess.call(["h5repack","-i%s"%self.HDFfile, "-otemphgfrt.h5"])
if (retcode!=0):
MPI.report("h5repack failed!")
else:
subprocess.call(["mv","-f","temphgfrt.h5","%s"%self.HDFfile])
def read_input_from_HDF(self, SubGrp, thingstoread, optionalthings=[]):
"""
Reads data from the HDF file
"""
retval = True
# init variables on all nodes:
for it in thingstoread:
exec "self.%s = 0" % it
for it in optionalthings:
exec "self.%s = 0" % it
if MPI.IS_MASTER_NODE():
ar = HDF_Archive(self.HDFfile, "a")
if SubGrp in ar:
# first read the necessary things:
for it in thingstoread:
if it in ar[SubGrp]:
exec "self.%s = ar['%s']['%s']" % (it, SubGrp, it)
else:
MPI.report("Loading %s failed!" % it)
retval = False
if (retval) and (len(optionalthings) > 0):
# if necessary things worked, now read optional things:
retval = {}
for it in optionalthings:
if it in ar[SubGrp]:
exec "self.%s = ar['%s']['%s']" % (it, SubGrp, it)
retval["%s" % it] = True
else:
retval["%s" % it] = False
else:
MPI.report("Loading failed: No %s subgroup in HDF5!" % SubGrp)
retval = False
del ar
# now do the broadcasting:
for it in thingstoread:
exec "self.%s = MPI.bcast(self.%s)" % (it, it)
for it in optionalthings:
exec "self.%s = MPI.bcast(self.%s)" % (it, it)
retval = MPI.bcast(retval)
return retval
def __repack(self):
"""Calls the h5repack routine, in order to reduce the file size of the hdf5 archive.
Should only be used BEFORE the first invokation of HDF_Archive in the program, otherwise
the hdf5 linking is broken!!!"""
import subprocess
if not (MPI.IS_MASTER_NODE()): return
MPI.report("Repacking the file %s" % self.HDFfile)
retcode = subprocess.call(
["h5repack", "-i%s" % self.HDFfile, "-otemphgfrt.h5"])
if (retcode != 0):
MPI.report("h5repack failed!")
else:
subprocess.call(["mv", "-f", "temphgfrt.h5", "%s" % self.HDFfile])
def Self_Consistency(self) :
S.Transform_SymmetryBasis_toRealSpace (IN= S.Sigma, OUT = Sigma) # Embedding
# Computes sum over BZ and returns density
F = lambda mu : SK(mu = mu,Sigma = Sigma, Field = None ,Res = G).total_density()/4
if Density_Required :
self.Chemical_potential = Dichotomy.Dichotomy(Function = F,
xinit = self.Chemical_potential, yvalue =Density_Required,
Precision_on_y = 0.01, Delta_x=0.5, MaxNbreLoop = 100,
xname="Chemical_Potential", yname= "Total Density",
verbosity = 3)[0]
else:
MPI.report("Total density = %.3f"%F(self.Chemical_potential))
S.Transform_RealSpace_to_SymmetryBasis (IN = G, OUT = S.G) # Extraction
S.G0 = inverse(S.Sigma + inverse(S.G)) # Finally get S.G0
def fitTails(self):
"""Fits the tails using the constant value for the Re Sigma calculated from F=Sigma*G.
Works only for blocks of size one."""
#if (len(self.GFStruct)==2*self.Norb):
if (self.blocssizeone):
spinblocs = [v for v in self.map]
MPI.report("Fitting tails manually")
known_coeff = numpy.zeros([1,1,2],numpy.float_)
msh = [x.imag for x in self.G[self.map[spinblocs[0]][0]].mesh ]
fit_start = msh[self.Fitting_Frequency_Start]
fit_stop = msh[self.N_Frequencies_Accumulated]
# Fit the tail of G just to get the density
for n,g in self.G:
g.fitTail([[[0,0,1]]],7,fit_start,2*fit_stop)
densmat = self.G.density()
for sig1 in spinblocs:
for i in range(self.Norb):
coeff = 0.0
for sig2 in spinblocs:
for j in range(self.Norb):
if (sig1==sig2):
coeff += self.U[self.offset+i,self.offset+j] * densmat[self.map[sig1][j]][0,0].real
else:
coeff += self.Up[self.offset+i,self.offset+j] * densmat[self.map[sig2][j]][0,0].real
known_coeff[0,0,1] = coeff
self.Sigma[self.map[sig1][i]].fitTail(fixed_coef = known_coeff, order_max = 3, fit_start = fit_start, fit_stop = fit_stop)
else:
for n,sig in self.Sigma:
known_coeff = numpy.zeros([sig.N1,sig.N2,1],numpy.float_)
msh = [x.imag for x in sig.mesh]
fit_start = msh[self.Fitting_Frequency_Start]
fit_stop = msh[self.N_Frequencies_Accumulated]
sig.fitTail(fixed_coef = known_coeff, order_max = 3, fit_start = fit_start, fit_stop = fit_stop)
def HT(Res):
# First compute the eps_hat array
eps_hat = Epsilon_Hat(
self.dos.eps) if Epsilon_Hat else numpy.array(
[x * numpy.identity(Sigma.N1) for x in self.dos.eps])
assert eps_hat.shape[0] == self.dos.eps.shape[
0], "Epsilon_Hat function behaves incorrectly"
assert eps_hat.shape[1] == eps_hat.shape[
2], "Epsilon_Hat function behaves incorrectly (result not a square matrix)"
assert Sigma.N1 == eps_hat.shape[
1], "Size of Sigma and of epsilon_hat mismatch"
Res.zero()
Sigma_fnt = callable(Sigma)
if Sigma_fnt:
assert len(
inspect.getargspec(Sigma)[0]
) == 1, "Sigma function is not of the correct type. See Documentation"
# Perform the sum over eps[i]
tmp, tmp2 = Res.copy(), Res.copy()
tmp <<= GF_Initializers.A_Omega_Plus_B(1, mu + eta * 1j)
if not (Sigma_fnt):
tmp -= Sigma
if Field != None: tmp -= Field
# I slice all the arrays on the node. Cf reduce operation below.
for d, e_h, e in itertools.izip(*[
MPI.slice_array(A)
for A in [self.rho_for_sum, eps_hat, self.dos.eps]
]):
tmp2.copyFrom(tmp)
tmp2 -= e_h
if Sigma_fnt: tmp2 -= Sigma(e)
tmp2.invert()
tmp2 *= d
Res += tmp2
# sum the Res GF of all nodes and returns the results on all nodes...
# Cf Boost.mpi.python, collective communicator for documentation.
# The point is that Res is pickable, hence can be transmitted between nodes without further code...
Res <<= MPI.all_reduce(MPI.world, Res, lambda x, y: x + y)
MPI.barrier()
def total_density(self, mu):
"""
Calculates the total charge for the energy window for a given mu. Since in general N_Orbitals depends on k,
the calculation is done in the following order:
G_aa'(k,iw) -> n(k) = Tr G_aa'(k,iw) -> sum_k n_k
mu: chemical potential
The calculation is done in the global coordinate system, if distinction is made between local/global!
"""
dens = 0.0
ikarray = numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
S = self.latticeGF_Matsubara(ik=ik, mu=mu)
dens += self.BZ_weights[ik] * S.total_density()
# collect data from MPI:
dens = MPI.all_reduce(MPI.world, dens, lambda x, y: x + y)
MPI.barrier()
return dens
def Self_Consistency(self):
S.Transform_SymmetryBasis_toRealSpace(IN=S.Sigma,
OUT=Sigma) # Embedding
# Computes sum over BZ and returns density
F = lambda mu: SK(mu=mu, Sigma=Sigma, Field=None, Res=G).total_density(
) / 4
if Density_Required:
self.Chemical_potential = Dichotomy.Dichotomy(
Function=F,
xinit=self.Chemical_potential,
yvalue=Density_Required,
Precision_on_y=0.01,
Delta_x=0.5,
MaxNbreLoop=100,
xname="Chemical_Potential",
yname="Total Density",
verbosity=3)[0]
else:
MPI.report("Total density = %.3f" % F(self.Chemical_potential))
S.Transform_RealSpace_to_SymmetryBasis(IN=G, OUT=S.G) # Extraction
S.G0 = inverse(S.Sigma + inverse(S.G)) # Finally get S.G0
def __init__(self, HDFfile, subgroup=None):
"""Initialises the class.
Reads the permutations and rotation matrizes from the file, and constructs the mapping for
the given orbitals. For each orbit a matrix is read!!!
SO: Flag for SO coupled calculations.
SP: Spin polarisation yes/no
"""
assert type(HDFfile) == StringType, "HDFfile must be a filename"
self.HDFfile = HDFfile
thingstoread = [
'Ns', 'Natoms', 'perm', 'orbits', 'SO', 'SP', 'timeinv', 'mat',
'mat_tinv'
]
for it in thingstoread:
exec "self.%s = 0" % it
if (MPI.IS_MASTER_NODE()):
#Read the stuff on master:
ar = HDF_Archive(HDFfile, 'a')
if (subgroup is None):
ar2 = ar
else:
ar2 = ar[subgroup]
for it in thingstoread:
exec "self.%s = ar2['%s']" % (it, it)
del ar2
del ar
#broadcasting
for it in thingstoread:
exec "self.%s = MPI.bcast(self.%s)" % (it, it)
# now define the mapping of orbitals:
# self.map[iorb]=jorb gives the permutation of the orbitals as given in the list, when the
# permutation of the atoms is done:
self.N_orbits = len(self.orbits)
self.map = [[0 for iorb in range(self.N_orbits)]
for iNs in range(self.Ns)]
for iNs in range(self.Ns):
for iorb in range(self.N_orbits):
srch = copy.deepcopy(self.orbits[iorb])
srch[0] = self.perm[iNs][self.orbits[iorb][0] - 1]
self.map[iNs][iorb] = self.orbits.index(srch)
def run(self,N_Loops, Mixing_Coefficient = 0.5, MaxTime = 0 ):
r"""
Run the DMFT Loop with the following algorithm ::
while STOP_CONDITION :
self.Self_Consistency()
for solver in Solver_List : S.solve()
self.PostSolver() # defaults : does nothing
where STOP_CONDITION is determined by the number of iterations.
:param N_Loops: Maximum number of iteration of the loop
:param Mixing_Coefficient:
:param MaxTime: Maximum time of the loop.
"""
# Set up the signal
# MPI.report("DMFTlab Job PID = %s"%os.getpid())
# Set the signal handler and a 5-second alarm
signal.signal(signal.SIGALRM, self.handler)
signal.alarm(MaxTime)
should_continue = True
while (should_continue):
MPI.report("------ Node : %d -------- Iteration Number = %d"%(MPI.rank,self.Iteration_Number))
self.Self_Consistency()
# call all solvers
for n,sol in enumerate(self.SolverList) :
if hasattr(self,"Chemical_potential") : sol.Chemical_potential=self.Chemical_potential
sol.Iteration_Number=self.Iteration_Number
sol.Solve()
sol.Sigma = sol.Sigma * Mixing_Coefficient + sol.Sigma_Old * (1-Mixing_Coefficient)
# post-solver processing
self.PostSolver()
self.Iteration_Number +=1
should_continue = self.__should_continue(N_Loops)
# end of the while loop
MPI.report("----------- END of DMFT_Loop ----------------")
MPI.barrier()
def DOSpartial(self, broadening=0.01):
"""calculates the orbitally-resolved DOS"""
assert hasattr(self, "Sigmaimp"), "Set Sigma First!!"
#thingstoread = ['Dens_Mat_below','N_parproj','Proj_Mat_pc','rotmat_all']
#retval = self.read_input_from_HDF(SubGrp=self.ParProjdata, thingstoread=thingstoread)
retval = self.read_ParProj_input_from_HDF()
if not retval: return retval
if self.symm_op:
self.Symm_par = Symmetry(self.HDFfile, subgroup=self.Symmpardata)
mu = self.Chemical_Potential
GFStruct_proj = [[(al, range(self.shells[i][3]))
for al in self.blocnames[self.SO]]
for i in xrange(self.N_shells)]
Gproj = [
GF(Name_Block_Generator=[
(a, GFBloc_ReFreq(Indices=al, Mesh=self.Sigmaimp[0].mesh))
for a, al in GFStruct_proj[ish]
],
Copy=False) for ish in xrange(self.N_shells)
]
for ish in range(self.N_shells):
Gproj[ish].zero()
Msh = [x for x in self.Sigmaimp[0].mesh]
N_om = len(Msh)
DOS = {}
for bn in self.blocnames[self.SO]:
DOS[bn] = numpy.zeros([N_om], numpy.float_)
DOSproj = [{} for ish in range(self.N_shells)]
DOSproj_orb = [{} for ish in range(self.N_shells)]
for ish in range(self.N_shells):
for bn in self.blocnames[self.SO]:
dl = self.shells[ish][3]
DOSproj[ish][bn] = numpy.zeros([N_om], numpy.float_)
DOSproj_orb[ish][bn] = numpy.zeros([dl, dl, N_om],
numpy.float_)
ikarray = numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
S = self.latticeGF_realfreq(ik=ik, mu=mu, broadening=broadening)
S *= self.BZ_weights[ik]
# non-projected DOS
for iom in range(N_om):
for sig, gf in S:
DOS[sig][iom] += gf._data.array[:, :, iom].imag.trace() / (
-3.1415926535)
#projected DOS:
for ish in xrange(self.N_shells):
tmp = Gproj[ish].copy()
for ir in xrange(self.N_parproj[ish]):
for sig, gf in tmp:
tmp[sig] <<= self.downfold_pc(ik, ir, ish, sig, S[sig],
gf)
Gproj[ish] += tmp
# collect data from MPI:
for sig in DOS:
DOS[sig] = MPI.all_reduce(MPI.world, DOS[sig], lambda x, y: x + y)
for ish in xrange(self.N_shells):
Gproj[ish] <<= MPI.all_reduce(MPI.world, Gproj[ish],
lambda x, y: x + y)
MPI.barrier()
if (self.symm_op != 0): Gproj = self.Symm_par.symmetrise(Gproj)
# rotation to local coord. system:
if (self.use_rotations):
for ish in xrange(self.N_shells):
for sig, gf in Gproj[ish]:
Gproj[ish][sig] <<= self.rotloc_all(ish,
gf,
direction='toLocal')
for ish in range(self.N_shells):
for sig, gf in Gproj[ish]:
for iom in range(N_om):
DOSproj[ish][sig][
iom] += gf._data.array[:, :, iom].imag.trace() / (
-3.1415926535)
DOSproj_orb[ish][
sig][:, :, :] += gf._data.array[:, :, :].imag / (
-3.1415926535)
if (MPI.IS_MASTER_NODE()):
# output to files
for bn in self.blocnames[self.SO]:
f = open('./DOScorr%s.dat' % bn, 'w')
for i in range(N_om):
f.write("%s %s\n" % (Msh[i], DOS[bn][i]))
f.close()
# partial
for ish in range(self.N_shells):
f = open('DOScorr%s_proj%s.dat' % (bn, ish), 'w')
for i in range(N_om):
f.write("%s %s\n" % (Msh[i], DOSproj[ish][bn][i]))
f.close()
for i in range(self.shells[ish][3]):
for j in range(i, self.shells[ish][3]):
Fname = './DOScorr' + bn + '_proj' + str(
ish) + '_' + str(i) + '_' + str(j) + '.dat'
f = open(Fname, 'w')
for iom in range(N_om):
f.write("%s %s\n" %
(Msh[iom], DOSproj_orb[ish][bn][i, j,
iom]))
f.close()
def __call__(self,
Sigma,
mu=0,
eta=0,
Field=None,
Epsilon_Hat=None,
Res=None,
SelectedBlocks=()):
"""
- Computes :
Res <- \[ \sum_k (\omega + \mu - Field - t(k) - Sigma(k,\omega)) \]
if Res is None, it returns a new GF with the results.
otherwise, Res must be a GF, in which the calculation is done, and which is then returned.
(this allows chain calculation : SK(mu = mu,Sigma = Sigma, Res = G).total_density()
which computes the sumK into G, and returns the density of G.
- Sigma can be a X, or a function k-> X or a function k,eps ->X where :
- k is expected to be a 1d-numpy array of size self.dim of float,
containing the k vector in the basis of the RBZ (i.e. -0.5< k_i <0.5)
- eps is t(k)
- X is anything such that X[BlockName] can be added/subtracted to a GFBloc for BlockName in SelectedBlocks.
e.g. X can be a GF (with at least the SelectedBlocks), or a dictionnary BlockName -> array
if the array has the same dimension as the GF blocks (for example to add a static Sigma).
- Field : Any k independant object to be added to the GF
- Epsilon_Hat : a function of eps_k returning a matrix, the dimensions of Sigma
- SelectedBlocks : The calculation is done with the SAME t(k) for all blocks. If this list is not None
only the blocks in this list are calculated.
e.g. G and Sigma have block indices 'up' and 'down'.
if SelectedBlocks ==None : 'up' and 'down' are calculated
if SelectedBlocks == ['up'] : only 'up' is calculated. 'down' is 0.
"""
S = Sigma.View_SelectedBlocks(
SelectedBlocks) if SelectedBlocks else Sigma
Gres = Res if Res else Sigma.copy()
G = Gres.View_SelectedBlocks(
SelectedBlocks) if SelectedBlocks else Gres
# check input
assert self.Orthogonal_Basis, "Local_G : must be orthogonal. non ortho cases not checked."
assert isinstance(G, GF), "G must be a GF"
assert len(list(set([g.N1 for i, g in G]))) == 1
assert self.BZ_weights.shape[0] == self.N_kpts(), "Internal Error"
no = list(set([g.N1 for i, g in G]))[0]
Sigma_Nargs = len(
inspect.getargspec(Sigma)[0]) if callable(Sigma) else 0
assert Sigma_Nargs <= 2, "Sigma function is not of the correct type. See Documentation"
# Initialize
G.zero()
tmp, tmp2 = G.copy(), G.copy()
mupat = mu * numpy.identity(no, numpy.complex_)
tmp <<= iOmega_n
if Field != None: tmp -= Field
if Sigma_Nargs == 0: tmp -= Sigma # substract Sigma once for all
# Loop on k points...
for w, k, eps_k in izip(*[
MPI.slice_array(A)
for A in [self.BZ_weights, self.BZ_Points, self.Hopping]
]):
eps_hat = Epsilon_Hat(eps_k) if Epsilon_Hat else eps_k
tmp2 <<= tmp
tmp2 -= tmp2.NBlocks * [eps_hat - mupat]
if Sigma_Nargs == 1: tmp2 -= Sigma(k)
elif Sigma_Nargs == 2: tmp2 -= Sigma(k, eps_k)
tmp2.invert()
tmp2 *= w
G += tmp2
G <<= MPI.all_reduce(MPI.world, G, lambda x, y: x + y)
MPI.barrier()
return Gres
def __call__ (self, Sigma, mu=0, eta = 0, Field = None, Res = None, SelectedBlocks = () ):
"""
- Computes :
Res <- \[ \sum_k (\omega + \mu - Field - t(k) - Sigma(k,\omega)) \]
if Res is None, it returns a new GF with the results.
otherwise, Res must be a GF, in which the calculation is done, and which is then returned.
(this allows chain calculation : SK(mu = mu,Sigma = Sigma, Res = G).total_density()
which computes the sumK into G, and returns the density of G.
- Sigma can be a X, or a function k-> X or a function k,eps ->X where :
- k is expected to be a 1d-numpy array of size self.dim of float,
containing the k vector in the basis of the RBZ (i.e. -0.5< k_i <0.5)
- eps is t(k)
- X is anything such that X[BlockName] can be added/subtracted to a GFBloc for BlockName in SelectedBlocks.
e.g. X can be a GF (with at least the SelectedBlocks), or a dictionnary BlockName -> array
if the array has the same dimension as the GF blocks (for example to add a static Sigma).
- Field : Any k independant Array_with_GF_Indices to be added to the GF
- SelectedBlocks : The calculation is done with the SAME t(k) for all blocks. If this list is not None
only the blocks in this list are calculated.
e.g. G and Sigma have block indices 'up' and 'down'.
if SelectedBlocks ==None : 'up' and 'down' are calculated
if SelectedBlocks == ['up'] : only 'up' is calculated. 'down' is 0.
"""
if Field : assert isinstance(Field,Array_with_GF_Indices) , " Field must be a Array_with_GF_Indices object. Cf Example"
S = Sigma.View_SelectedBlocks(SelectedBlocks) if SelectedBlocks else Sigma
Gres = Res if Res else Sigma.copy()
G = Gres.View_SelectedBlocks(SelectedBlocks) if SelectedBlocks else Gres
# check input
assert self.Orthogonal_Basis, "Local_G : must be orthogonal. non ortho cases not checked."
assert isinstance(G,GF), "G must be a GF"
assert list(set([ g.N1 for i,g in G])) == [self.Hopping.shape[1]],"G size and hopping size mismatch"
assert self.BZ_weights.shape[0] == self.N_kpts(), "Internal Error"
Sigma_Nargs = len(inspect.getargspec(Sigma)[0]) if callable (Sigma) else 0
assert Sigma_Nargs <=2 , "Sigma function is not of the correct type. See Documentation"
#init
G.zero()
#tmp,tmp2 = GF(G),GF(G)
tmp,tmp2 = G.copy(),G.copy()
mupat = mu * self.Mu_Pattern
tmp <<= GF_Initializers.A_Omega_Plus_B(A=1,B=0)
#tmp.Set_Omega()
##tmp += tmp.Nblocks() * [ mupat ]
if Field : tmp -= Field
if Sigma_Nargs==0: tmp -= Sigma # substract Sigma once for all
# Loop on k points...
for w, k, eps_k in izip(*[MPI.slice_array(A) for A in [self.BZ_weights, self.BZ_Points, self.Hopping]]):
tmp2 <<= tmp
#tmp2.copy_from(tmp)
tmp2 -= tmp2.NBlocks * [eps_k -mupat ]
#tmp2.save("tmp2_w")
#Sigma.save("S_w")
if Sigma_Nargs == 1: tmp2 -= Sigma (k)
elif Sigma_Nargs ==2: tmp2 -= Sigma (k,eps_k)
tmp2.invert()
tmp2 *= w
G += tmp2
#G.save("GG1")
#print mu,mupat,eps_k
#assert 0
#print G['up'][1,1]._data
G <<= MPI.all_reduce(MPI.world,G,lambda x,y : x+y)
MPI.barrier()
return Res
def convert_DMFT_input(self):
"""
Reads the input files, and stores the data in the HDFfile
"""
if not (MPI.IS_MASTER_NODE()): return # do it only on master:
MPI.report("Reading input from %s..."%self.LDA_file)
# Read and write only on Master!!!
# R is a generator : each R.Next() will return the next number in the file
R = Read_Fortran_File(self.LDA_file)
try:
EnergyUnit = R.next() # read the energy convertion factor
Nk = int(R.next()) # read the number of k points
k_dep_projection = 1
SP = int(R.next()) # flag for spin-polarised calculation
SO = int(R.next()) # flag for spin-orbit calculation
charge_below = R.next() # total charge below energy window
Density_Required = R.next() # total density required, for setting the chemical potential
symm_op = 1 # Use symmetry groups for the k-sum
# the information on the non-correlated shells is not important here, maybe skip:
N_shells = int(R.next()) # number of shells (e.g. Fe d, As p, O p) in the unit cell,
# corresponds to index R in formulas
# now read the information about the shells:
shells = [ [ int(R.next()) for i in range(4) ] for icrsh in range(N_shells) ] # reads iatom, sort, l, dim
N_corr_shells = int(R.next()) # number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
# corresponds to index R in formulas
# now read the information about the shells:
corr_shells = [ [ int(R.next()) for i in range(6) ] for icrsh in range(N_corr_shells) ] # reads iatom, sort, l, dim, SO flag, irep
self.inequiv_shells(corr_shells) # determine the number of inequivalent correlated shells, has to be known for further reading...
use_rotations = 1
rotmat = [numpy.identity(corr_shells[icrsh][3],numpy.complex_) for icrsh in xrange(N_corr_shells)]
# read the matrices
rotmat_timeinv = [0 for i in range(N_corr_shells)]
for icrsh in xrange(N_corr_shells):
for i in xrange(corr_shells[icrsh][3]): # read real part:
for j in xrange(corr_shells[icrsh][3]):
rotmat[icrsh][i,j] = R.next()
for i in xrange(corr_shells[icrsh][3]): # read imaginary part:
for j in xrange(corr_shells[icrsh][3]):
rotmat[icrsh][i,j] += 1j * R.next()
if (SP==1): # read time inversion flag:
rotmat_timeinv[icrsh] = int(R.next())
# Read here the infos for the transformation of the basis:
Nreps = [1 for i in range(self.N_inequiv_corr_shells)]
dim_reps = [0 for i in range(self.N_inequiv_corr_shells)]
T = []
for icrsh in range(self.N_inequiv_corr_shells):
Nreps[icrsh] = int(R.next()) # number of representatives ("subsets"), e.g. t2g and eg
dim_reps[icrsh] = [int(R.next()) for i in range(Nreps[icrsh])] # dimensions of the subsets
# The transformation matrix:
# it is of dimension 2l+1, if no SO, and 2*(2l+1) with SO!!
#T = []
#for ish in xrange(self.N_inequiv_corr_shells):
ll = 2*corr_shells[self.invshellmap[icrsh]][2]+1
lmax = ll * (corr_shells[self.invshellmap[icrsh]][4] + 1)
T.append(numpy.zeros([lmax,lmax],numpy.complex_))
# now read it from file:
for i in xrange(lmax):
for j in xrange(lmax):
T[icrsh][i,j] = R.next()
for i in xrange(lmax):
for j in xrange(lmax):
T[icrsh][i,j] += 1j * R.next()
# Spin blocks to be read:
Nspinblocs = SP + 1 - SO # number of spins to read for Norbs and Ham, NOT Projectors
# read the list of N_Orbitals for all k points
N_Orbitals = [ [0 for isp in range(Nspinblocs)] for ik in xrange(Nk)]
for isp in range(Nspinblocs):
for ik in xrange(Nk):
N_Orbitals[ik][isp] = int(R.next())
#print N_Orbitals
# Initialise the projectors:
Proj_Mat = [ [ [numpy.zeros([corr_shells[icrsh][3], N_Orbitals[ik][isp]], numpy.complex_)
for icrsh in range (N_corr_shells)]
for isp in range(Nspinblocs)]
for ik in range(Nk) ]
# Read the projectors from the file:
for ik in xrange(Nk):
for icrsh in range(N_corr_shells):
no = corr_shells[icrsh][3]
# first Real part for BOTH spins, due to conventions in dmftproj:
for isp in range(Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i,j] = R.next()
# now Imag part:
for isp in range(Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i,j] += 1j * R.next()
# now define the arrays for weights and hopping ...
BZ_weights = numpy.ones([Nk],numpy.float_)/ float(Nk) # w(k_index), default normalisation
Hopping = [ [numpy.zeros([N_Orbitals[ik][isp],N_Orbitals[ik][isp]],numpy.complex_)
for isp in range(Nspinblocs)] for ik in xrange(Nk) ]
# weights in the file
for ik in xrange(Nk) : BZ_weights[ik] = R.next()
# if the sum over spins is in the weights, take it out again!!
sm = sum(BZ_weights)
BZ_weights[:] /= sm
# Grab the H
# we use now the convention of a DIAGONAL Hamiltonian!!!!
for isp in range(Nspinblocs):
for ik in xrange(Nk) :
no = N_Orbitals[ik][isp]
for i in xrange(no):
Hopping[ik][isp][i,i] = R.next() * EnergyUnit
#keep some things that we need for reading parproj:
self.N_shells = N_shells
self.shells = shells
self.N_corr_shells = N_corr_shells
self.corr_shells = corr_shells
self.Nspinblocs = Nspinblocs
self.N_Orbitals = N_Orbitals
self.Nk = Nk
self.SO = SO
self.SP = SP
self.EnergyUnit = EnergyUnit
except StopIteration : # a more explicit error if the file is corrupted.
raise "SumK_LDA : reading file HMLT_file failed!"
R.close()
#print Proj_Mat[0]
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile,'a')
if not (self.LDASubGrp in ar): ar.create_group(self.LDASubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
ar[self.LDASubGrp]['EnergyUnit'] = EnergyUnit
ar[self.LDASubGrp]['Nk'] = Nk
ar[self.LDASubGrp]['k_dep_projection'] = k_dep_projection
ar[self.LDASubGrp]['SP'] = SP
ar[self.LDASubGrp]['SO'] = SO
ar[self.LDASubGrp]['charge_below'] = charge_below
ar[self.LDASubGrp]['Density_Required'] = Density_Required
ar[self.LDASubGrp]['symm_op'] = symm_op
ar[self.LDASubGrp]['N_shells'] = N_shells
ar[self.LDASubGrp]['shells'] = shells
ar[self.LDASubGrp]['N_corr_shells'] = N_corr_shells
ar[self.LDASubGrp]['corr_shells'] = corr_shells
ar[self.LDASubGrp]['use_rotations'] = use_rotations
ar[self.LDASubGrp]['rotmat'] = rotmat
ar[self.LDASubGrp]['rotmat_timeinv'] = rotmat_timeinv
ar[self.LDASubGrp]['Nreps'] = Nreps
ar[self.LDASubGrp]['dim_reps'] = dim_reps
ar[self.LDASubGrp]['T'] = T
ar[self.LDASubGrp]['N_Orbitals'] = N_Orbitals
ar[self.LDASubGrp]['Proj_Mat'] = Proj_Mat
ar[self.LDASubGrp]['BZ_weights'] = BZ_weights
ar[self.LDASubGrp]['Hopping'] = Hopping
del ar
# Symmetries are used,
# Now do the symmetries for correlated orbitals:
self.read_Symmetry_input(orbits=corr_shells,symmfile=self.Symm_file,SymmSubGrp=self.SymmSubGrp,SO=SO,SP=SP)
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 DOSpartial(self,broadening=0.01):
"""calculates the orbitally-resolved DOS"""
assert hasattr(self,"Sigmaimp"), "Set Sigma First!!"
#thingstoread = ['Dens_Mat_below','N_parproj','Proj_Mat_pc','rotmat_all']
#retval = self.read_input_from_HDF(SubGrp=self.ParProjdata, thingstoread=thingstoread)
retval = self.read_ParProj_input_from_HDF()
if not retval: return retval
if self.symm_op: self.Symm_par = Symmetry(self.HDFfile,subgroup=self.Symmpardata)
mu = self.Chemical_Potential
GFStruct_proj = [ [ (al, range(self.shells[i][3])) for al in self.blocnames[self.SO] ] for i in xrange(self.N_shells) ]
Gproj = [GF(Name_Block_Generator = [ (a,GFBloc_ReFreq(Indices = al, Mesh = self.Sigmaimp[0].mesh)) for a,al in GFStruct_proj[ish] ], Copy = False )
for ish in xrange(self.N_shells)]
for ish in range(self.N_shells): Gproj[ish].zero()
Msh = [x for x in self.Sigmaimp[0].mesh]
N_om = len(Msh)
DOS = {}
for bn in self.blocnames[self.SO]:
DOS[bn] = numpy.zeros([N_om],numpy.float_)
DOSproj = [ {} for ish in range(self.N_shells) ]
DOSproj_orb = [ {} for ish in range(self.N_shells) ]
for ish in range(self.N_shells):
for bn in self.blocnames[self.SO]:
dl = self.shells[ish][3]
DOSproj[ish][bn] = numpy.zeros([N_om],numpy.float_)
DOSproj_orb[ish][bn] = numpy.zeros([dl,dl,N_om],numpy.float_)
ikarray=numpy.array(range(self.Nk))
for ik in MPI.slice_array(ikarray):
S = self.latticeGF_realfreq(ik=ik,mu=mu,broadening=broadening)
S *= self.BZ_weights[ik]
# non-projected DOS
for iom in range(N_om):
for sig,gf in S: DOS[sig][iom] += gf._data.array[:,:,iom].imag.trace()/(-3.1415926535)
#projected DOS:
for ish in xrange(self.N_shells):
tmp = Gproj[ish].copy()
for ir in xrange(self.N_parproj[ish]):
for sig,gf in tmp: tmp[sig] <<= self.downfold_pc(ik,ir,ish,sig,S[sig],gf)
Gproj[ish] += tmp
# collect data from MPI:
for sig in DOS:
DOS[sig] = MPI.all_reduce(MPI.world,DOS[sig],lambda x,y : x+y)
for ish in xrange(self.N_shells):
Gproj[ish] <<= MPI.all_reduce(MPI.world,Gproj[ish],lambda x,y : x+y)
MPI.barrier()
if (self.symm_op!=0): Gproj = self.Symm_par.symmetrise(Gproj)
# rotation to local coord. system:
if (self.use_rotations):
for ish in xrange(self.N_shells):
for sig,gf in Gproj[ish]: Gproj[ish][sig] <<= self.rotloc_all(ish,gf,direction='toLocal')
for ish in range(self.N_shells):
for sig,gf in Gproj[ish]:
for iom in range(N_om): DOSproj[ish][sig][iom] += gf._data.array[:,:,iom].imag.trace()/(-3.1415926535)
DOSproj_orb[ish][sig][:,:,:] += gf._data.array[:,:,:].imag / (-3.1415926535)
if (MPI.IS_MASTER_NODE()):
# output to files
for bn in self.blocnames[self.SO]:
f=open('./DOScorr%s.dat'%bn, 'w')
for i in range(N_om): f.write("%s %s\n"%(Msh[i],DOS[bn][i]))
f.close()
# partial
for ish in range(self.N_shells):
f=open('DOScorr%s_proj%s.dat'%(bn,ish),'w')
for i in range(N_om): f.write("%s %s\n"%(Msh[i],DOSproj[ish][bn][i]))
f.close()
for i in range(self.shells[ish][3]):
for j in range(i,self.shells[ish][3]):
Fname = './DOScorr'+bn+'_proj'+str(ish)+'_'+str(i)+'_'+str(j)+'.dat'
f=open(Fname,'w')
for iom in range(N_om): f.write("%s %s\n"%(Msh[iom],DOSproj_orb[ish][bn][i,j,iom]))
f.close()
def read_Symmetry_input(self,orbits,symmfile,SymmSubGrp,SO,SP):
"""
Reads input for the symmetrisations from symmfile, which is case.sympar or case.symqmc.
"""
if not (MPI.IS_MASTER_NODE()): return
MPI.report("Reading symmetry input from %s..."%symmfile)
N_orbits = len(orbits)
R=Read_Fortran_File(symmfile)
try:
Ns = int(R.next()) # Number of symmetry operations
Natoms = int(R.next()) # number of atoms involved
perm = [ [int(R.next()) for i in xrange(Natoms)] for j in xrange(Ns) ] # list of permutations of the atoms
if SP:
timeinv = [ int(R.next()) for j in xrange(Ns) ] # timeinversion for SO xoupling
else:
timeinv = [ 0 for j in xrange(Ns) ]
# Now read matrices:
mat = []
for iNs in xrange(Ns):
mat.append( [ numpy.zeros([orbits[orb][3], orbits[orb][3]],numpy.complex_) for orb in xrange(N_orbits) ] )
for orb in range(N_orbits):
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat[iNs][orb][i,j] = R.next() # real part
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat[iNs][orb][i,j] += 1j * R.next() # imaginary part
# determine the inequivalent shells:
#SHOULD BE FINALLY REMOVED, PUT IT FOR ALL ORBITALS!!!!!
#self.inequiv_shells(orbits)
mat_tinv = [numpy.identity(orbits[orb][3],numpy.complex_)
for orb in range(N_orbits)]
if ((SO==0) and (SP==0)):
# here we need an additional time inversion operation, so read it:
for orb in range(N_orbits):
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat_tinv[orb][i,j] = R.next() # real part
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat_tinv[orb][i,j] += 1j * R.next() # imaginary part
except StopIteration : # a more explicit error if the file is corrupted.
raise "Symmetry : reading file failed!"
R.close()
# Save it to the HDF:
ar=HDF_Archive(self.HDFfile,'a')
if not (SymmSubGrp in ar): ar.create_group(SymmSubGrp)
thingstowrite = ['Ns','Natoms','perm','orbits','SO','SP','timeinv','mat','mat_tinv']
for it in thingstowrite: exec "ar['%s']['%s'] = %s"%(SymmSubGrp,it,it)
del ar
N_Legendre_Coeffs=50, # Number of Legendre coefficients
Random_Generator_Name='mt19937', # Name of the random number generator
Use_Segment_Picture=True, # Use the segment picture
Measured_Operators={ # Operators to be averaged
'Nimp': N('up', 1) + N('down', 1)
},
Global_Moves=[ # Global move in the QMC
(0.05, lambda (a, alpha, dag): ({
'up': 'down',
'down': 'up'
}[a], alpha, dag))
],
)
# Initialize the non-interacting Green's function S.G0
for spin, g0 in S.G0:
g0 <<= inverse(iOmega_n - e_f - V**2 * Wilson(D))
# Run the solver. The result will be in S.G
S.Solve()
# Save the results in an hdf5 file (only on the master node)
from pytriqs.Base.Archive import HDF_Archive
import pytriqs.Base.Utility.MPI as MPI
if MPI.IS_MASTER_NODE():
Results = HDF_Archive("solution.h5", 'w')
Results["G"] = S.G
Results["Gl"] = S.G_Legendre
Results["Nimp"] = S.Measured_Operators_Results['Nimp']
def partial_charges(self):
"""Calculates the orbitally-resolved density matrix for all the orbitals considered in the input.
The theta-projectors are used, hence case.parproj data is necessary"""
#thingstoread = ['Dens_Mat_below','N_parproj','Proj_Mat_pc','rotmat_all']
#retval = self.read_input_from_HDF(SubGrp=self.ParProjdata,thingstoread=thingstoread)
retval = self.read_ParProj_input_from_HDF()
if not retval: return retval
if self.symm_op: self.Symm_par = Symmetry(self.HDFfile,subgroup=self.Symmpardata)
# Density matrix in the window
bln = self.blocnames[self.SO]
ntoi = self.names_to_ind[self.SO]
self.Dens_Mat_window = [ [numpy.zeros([self.shells[ish][3],self.shells[ish][3]],numpy.complex_) for ish in range(self.N_shells)]
for isp in range(len(bln)) ] # init the density matrix
mu = self.Chemical_Potential
GFStruct_proj = [ [ (al, range(self.shells[i][3])) for al in bln ] for i in xrange(self.N_shells) ]
if hasattr(self,"Sigmaimp"):
Gproj = [GF(Name_Block_Generator = [ (a,GFBloc_ImFreq(Indices = al, Mesh = self.Sigmaimp[0].mesh)) for a,al in GFStruct_proj[ish] ], Copy = False)
for ish in xrange(self.N_shells)]
else:
Gproj = [GF(Name_Block_Generator = [ (a,GFBloc_ImFreq(Indices = al, Beta = 40)) for a,al in GFStruct_proj[ish] ], Copy = False)
for ish in xrange(self.N_shells)]
for ish in xrange(self.N_shells): Gproj[ish].zero()
ikarray=numpy.array(range(self.Nk))
#print MPI.rank, MPI.slice_array(ikarray)
#print "K-Sum starts on node",MPI.rank," at ",datetime.now()
for ik in MPI.slice_array(ikarray):
#print MPI.rank, ik, datetime.now()
S = self.latticeGF_Matsubara(ik=ik,mu=mu)
S *= self.BZ_weights[ik]
for ish in xrange(self.N_shells):
tmp = Gproj[ish].copy()
for ir in xrange(self.N_parproj[ish]):
for sig,gf in tmp: tmp[sig] <<= self.downfold_pc(ik,ir,ish,sig,S[sig],gf)
Gproj[ish] += tmp
#print "K-Sum done on node",MPI.rank," at ",datetime.now()
#collect data from MPI:
for ish in xrange(self.N_shells):
Gproj[ish] <<= MPI.all_reduce(MPI.world,Gproj[ish],lambda x,y : x+y)
MPI.barrier()
#print "Data collected on node",MPI.rank," at ",datetime.now()
# Symmetrisation:
if (self.symm_op!=0): Gproj = self.Symm_par.symmetrise(Gproj)
#print "Symmetrisation done on node",MPI.rank," at ",datetime.now()
for ish in xrange(self.N_shells):
# Rotation to local:
if (self.use_rotations):
for sig,gf in Gproj[ish]: Gproj[ish][sig] <<= self.rotloc_all(ish,gf,direction='toLocal')
isp = 0
for sig,gf in Gproj[ish]: #dmg.append(Gproj[ish].density()[sig])
self.Dens_Mat_window[isp][ish] = Gproj[ish].density()[sig]
isp+=1
# add Density matrices to get the total:
Dens_Mat = [ [ self.Dens_Mat_below[ntoi[bln[isp]]][ish]+self.Dens_Mat_window[isp][ish] for ish in range(self.N_shells)]
for isp in range(len(bln)) ]
return Dens_Mat
def convert_Parproj_input(self,
ParProjSubGrp='SumK_LDA_ParProj',
SymmParSubGrp='SymmPar'):
"""
Reads the input for the partial charges projectors from case.parproj, and stores it in the SymmParSubGrp
group in the HDF5.
"""
if not (MPI.IS_MASTER_NODE()): return
self.ParProjSubGrp = ParProjSubGrp
self.SymmParSubGrp = SymmParSubGrp
MPI.report("Reading parproj input from %s..." % self.Parproj_file)
Dens_Mat_below = [[
numpy.zeros([self.shells[ish][3], self.shells[ish][3]],
numpy.complex_) for ish in range(self.N_shells)
] for isp in range(self.Nspinblocs)]
R = Read_Fortran_File(self.Parproj_file)
#try:
N_parproj = [int(R.next()) for i in range(self.N_shells)]
# Initialise P, here a double list of matrices:
Proj_Mat_pc = [[[[
numpy.zeros([self.shells[ish][3], self.N_Orbitals[ik][isp]],
numpy.complex_) for ir in range(N_parproj[ish])
] for ish in range(self.N_shells)] for isp in range(self.Nspinblocs)]
for ik in range(self.Nk)]
rotmat_all = [
numpy.identity(self.shells[ish][3], numpy.complex_)
for ish in xrange(self.N_shells)
]
rotmat_all_timeinv = [0 for i in range(self.N_shells)]
for ish in range(self.N_shells):
#print ish
# read first the projectors for this orbital:
for ik in xrange(self.Nk):
for ir in range(N_parproj[ish]):
for isp in range(self.Nspinblocs):
for i in xrange(
self.shells[ish][3]): # read real part:
for j in xrange(self.N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][i, j] = R.next()
for isp in range(self.Nspinblocs):
for i in xrange(
self.shells[ish][3]): # read imaginary part:
for j in xrange(self.N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][
i, j] += 1j * R.next()
# now read the Density Matrix for this orbital below the energy window:
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(self.shells[ish][3]):
Dens_Mat_below[isp][ish][i, j] = R.next()
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(self.shells[ish][3]):
Dens_Mat_below[isp][ish][i, j] += 1j * R.next()
if (self.SP == 0): Dens_Mat_below[isp][ish] /= 2.0
# Global -> local rotation matrix for this shell:
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(self.shells[ish][3]):
rotmat_all[ish][i, j] = R.next()
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(self.shells[ish][3]):
rotmat_all[ish][i, j] += 1j * R.next()
#print Dens_Mat_below[0][ish],Dens_Mat_below[1][ish]
if (self.SP):
rotmat_all_timeinv[ish] = int(R.next())
#except StopIteration : # a more explicit error if the file is corrupted.
# raise "SumK_LDA_Wien2k_input: reading file for Projectors failed!"
R.close()
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile, 'a')
if not (self.ParProjSubGrp in ar): ar.create_group(self.ParProjSubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
thingstowrite = [
'Dens_Mat_below', 'N_parproj', 'Proj_Mat_pc', 'rotmat_all',
'rotmat_all_timeinv'
]
for it in thingstowrite:
exec "ar['%s']['%s'] = %s" % (self.ParProjSubGrp, it, it)
del ar
# Symmetries are used,
# Now do the symmetries for all orbitals:
self.read_Symmetry_input(orbits=self.shells,
symmfile=self.Symmpar_file,
SymmSubGrp=self.SymmParSubGrp,
SO=self.SO,
SP=self.SP)
def partial_charges(self):
"""Calculates the orbitally-resolved density matrix for all the orbitals considered in the input.
The theta-projectors are used, hence case.parproj data is necessary"""
#thingstoread = ['Dens_Mat_below','N_parproj','Proj_Mat_pc','rotmat_all']
#retval = self.read_input_from_HDF(SubGrp=self.ParProjdata,thingstoread=thingstoread)
retval = self.read_ParProj_input_from_HDF()
if not retval: return retval
if self.symm_op:
self.Symm_par = Symmetry(self.HDFfile, subgroup=self.Symmpardata)
# Density matrix in the window
bln = self.blocnames[self.SO]
ntoi = self.names_to_ind[self.SO]
self.Dens_Mat_window = [[
numpy.zeros([self.shells[ish][3], self.shells[ish][3]],
numpy.complex_) for ish in range(self.N_shells)
] for isp in range(len(bln))] # init the density matrix
mu = self.Chemical_Potential
GFStruct_proj = [[(al, range(self.shells[i][3])) for al in bln]
for i in xrange(self.N_shells)]
if hasattr(self, "Sigmaimp"):
Gproj = [
GF(Name_Block_Generator=[
(a, GFBloc_ImFreq(Indices=al, Mesh=self.Sigmaimp[0].mesh))
for a, al in GFStruct_proj[ish]
],
Copy=False) for ish in xrange(self.N_shells)
]
else:
Gproj = [
GF(Name_Block_Generator=[(a, GFBloc_ImFreq(Indices=al,
Beta=40))
for a, al in GFStruct_proj[ish]],
Copy=False) for ish in xrange(self.N_shells)
]
for ish in xrange(self.N_shells):
Gproj[ish].zero()
ikarray = numpy.array(range(self.Nk))
#print MPI.rank, MPI.slice_array(ikarray)
#print "K-Sum starts on node",MPI.rank," at ",datetime.now()
for ik in MPI.slice_array(ikarray):
#print MPI.rank, ik, datetime.now()
S = self.latticeGF_Matsubara(ik=ik, mu=mu)
S *= self.BZ_weights[ik]
for ish in xrange(self.N_shells):
tmp = Gproj[ish].copy()
for ir in xrange(self.N_parproj[ish]):
for sig, gf in tmp:
tmp[sig] <<= self.downfold_pc(ik, ir, ish, sig, S[sig],
gf)
Gproj[ish] += tmp
#print "K-Sum done on node",MPI.rank," at ",datetime.now()
#collect data from MPI:
for ish in xrange(self.N_shells):
Gproj[ish] <<= MPI.all_reduce(MPI.world, Gproj[ish],
lambda x, y: x + y)
MPI.barrier()
#print "Data collected on node",MPI.rank," at ",datetime.now()
# Symmetrisation:
if (self.symm_op != 0): Gproj = self.Symm_par.symmetrise(Gproj)
#print "Symmetrisation done on node",MPI.rank," at ",datetime.now()
for ish in xrange(self.N_shells):
# Rotation to local:
if (self.use_rotations):
for sig, gf in Gproj[ish]:
Gproj[ish][sig] <<= self.rotloc_all(ish,
gf,
direction='toLocal')
isp = 0
for sig, gf in Gproj[ish]: #dmg.append(Gproj[ish].density()[sig])
self.Dens_Mat_window[isp][ish] = Gproj[ish].density()[sig]
isp += 1
# add Density matrices to get the total:
Dens_Mat = [[
self.Dens_Mat_below[ntoi[bln[isp]]][ish] +
self.Dens_Mat_window[isp][ish] for ish in range(self.N_shells)
] for isp in range(len(bln))]
return Dens_Mat
def spaghettis(self,
broadening,
shift=0.0,
plotrange=None,
ishell=None,
invertAkw=False,
Fermisurface=False):
""" Calculates the correlated band structure with a real-frequency self energy.
ATTENTION: Many things from the original input file are are overwritten!!!"""
assert hasattr(self, "Sigmaimp"), "Set Sigma First!!"
thingstoread = [
'Nk', 'N_Orbitals', 'Proj_Mat', 'Hopping', 'N_parproj',
'Proj_Mat_pc'
]
retval = self.read_input_from_HDF(SubGrp=self.Bandsdata,
thingstoread=thingstoread)
if not retval: return retval
if Fermisurface: ishell = None
# print hamiltonian for checks:
if ((self.SP == 1) and (self.SO == 0)):
f1 = open('hamup.dat', 'w')
f2 = open('hamdn.dat', 'w')
for ik in xrange(self.Nk):
for i in xrange(self.N_Orbitals[ik][0]):
f1.write('%s %s\n' %
(ik, self.Hopping[ik][0][i, i].real))
for i in xrange(self.N_Orbitals[ik][1]):
f2.write('%s %s\n' %
(ik, self.Hopping[ik][1][i, i].real))
f1.write('\n')
f2.write('\n')
f1.close()
f2.close()
else:
f = open('ham.dat', 'w')
for ik in xrange(self.Nk):
for i in xrange(self.N_Orbitals[ik][0]):
f.write('%s %s\n' %
(ik, self.Hopping[ik][0][i, i].real))
f.write('\n')
f.close()
#=========================================
# calculate A(k,w):
mu = self.Chemical_Potential
bln = self.blocnames[self.SO]
# init DOS:
M = [x for x in self.Sigmaimp[0].mesh]
N_om = len(M)
if plotrange is None:
omminplot = M[0] - 0.001
ommaxplot = M[N_om - 1] + 0.001
else:
omminplot = plotrange[0]
ommaxplot = plotrange[1]
if (ishell is None):
Akw = {}
for ibn in bln:
Akw[ibn] = numpy.zeros([self.Nk, N_om], numpy.float_)
else:
Akw = {}
for ibn in bln:
Akw[ibn] = numpy.zeros([self.shells[ishell][3], self.Nk, N_om],
numpy.float_)
if Fermisurface:
omminplot = -2.0 * broadening
ommaxplot = 2.0 * broadening
Akw = {}
for ibn in bln:
Akw[ibn] = numpy.zeros([self.Nk, 1], numpy.float_)
if not (ishell is None):
GFStruct_proj = [(al, range(self.shells[ishell][3])) for al in bln]
Gproj = GF(Name_Block_Generator=[
(a, GFBloc_ReFreq(Indices=al, Mesh=self.Sigmaimp[0].mesh))
for a, al in GFStruct_proj
],
Copy=False)
Gproj.zero()
for ik in xrange(self.Nk):
S = self.latticeGF_realfreq(ik=ik, mu=mu, broadening=broadening)
if (ishell is None):
# non-projected A(k,w)
for iom in range(N_om):
if (M[iom] > omminplot) and (M[iom] < ommaxplot):
if Fermisurface:
for sig, gf in S:
Akw[sig][
ik,
0] += gf._data.array[:, :, iom].imag.trace(
) / (-3.1415926535) * (M[1] - M[0])
else:
for sig, gf in S:
Akw[sig][
ik,
iom] += gf._data.array[:, :,
iom].imag.trace(
) / (-3.1415926535)
Akw[sig][
ik,
iom] += ik * shift # shift Akw for plotting in xmgrace
else:
# projected A(k,w):
Gproj.zero()
tmp = Gproj.copy()
for ir in xrange(self.N_parproj[ishell]):
for sig, gf in tmp:
tmp[sig] <<= self.downfold_pc(ik, ir, ishell, sig,
S[sig], gf)
Gproj += tmp
# TO BE FIXED:
# rotate to local frame
#if (self.use_rotations):
# for sig,gf in Gproj: Gproj[sig] <<= self.rotloc(0,gf,direction='toLocal')
for iom in range(N_om):
if (M[iom] > omminplot) and (M[iom] < ommaxplot):
for ish in range(self.shells[ishell][3]):
for ibn in bln:
Akw[ibn][ish, ik,
iom] = Gproj[ibn]._data.array[
ish, ish,
iom].imag / (-3.1415926535)
# END k-LOOP
if (MPI.IS_MASTER_NODE()):
if (ishell is None):
for ibn in bln:
# loop over GF blocs:
if (invertAkw):
maxAkw = Akw[ibn].max()
minAkw = Akw[ibn].min()
# open file for storage:
if Fermisurface:
f = open('FS_' + ibn + '.dat', 'w')
else:
f = open('Akw_' + ibn + '.dat', 'w')
for ik in range(self.Nk):
if Fermisurface:
if (invertAkw):
Akw[ibn][ik, 0] = 1.0 / (minAkw - maxAkw) * (
Akw[ibn][ik, iom] - maxAkw)
f.write('%s %s\n' % (ik, Akw[ibn][ik, 0]))
else:
for iom in range(N_om):
if (M[iom] > omminplot) and (M[iom] <
ommaxplot):
if (invertAkw):
Akw[ibn][
ik,
iom] = 1.0 / (minAkw - maxAkw) * (
Akw[ibn][ik, iom] - maxAkw)
if (shift > 0.0001):
f.write('%s %s\n' %
(M[iom], Akw[ibn][ik, iom]))
else:
f.write(
'%s %s %s\n' %
(ik, M[iom], Akw[ibn][ik, iom]))
f.write('\n')
f.close()
else:
for ibn in bln:
for ish in range(self.shells[ishell][3]):
if (invertAkw):
maxAkw = Akw[ibn][ish, :, :].max()
minAkw = Akw[ibn][ish, :, :].min()
f = open('Akw_' + ibn + '_proj' + str(ish) + '.dat',
'w')
for ik in range(self.Nk):
for iom in range(N_om):
if (M[iom] > omminplot) and (M[iom] <
ommaxplot):
if (invertAkw):
Akw[ibn][ish, ik, iom] = 1.0 / (
minAkw - maxAkw
) * (Akw[ibn][ish, ik, iom] - maxAkw)
if (shift > 0.0001):
f.write(
'%s %s\n' %
(M[iom], Akw[ibn][ish, ik, iom]))
else:
f.write('%s %s %s\n' %
(ik, M[iom], Akw[ibn][ish, ik,
iom]))
f.write('\n')
f.close()
def convert_Parproj_input(self,ParProjSubGrp='SumK_LDA_ParProj',SymmParSubGrp='SymmPar'):
"""
Reads the input for the partial charges projectors from case.parproj, and stores it in the SymmParSubGrp
group in the HDF5.
"""
if not (MPI.IS_MASTER_NODE()): return
self.ParProjSubGrp = ParProjSubGrp
self.SymmParSubGrp = SymmParSubGrp
MPI.report("Reading parproj input from %s..."%self.Parproj_file)
Dens_Mat_below = [ [numpy.zeros([self.shells[ish][3],self.shells[ish][3]],numpy.complex_) for ish in range(self.N_shells)]
for isp in range(self.Nspinblocs) ]
R = Read_Fortran_File(self.Parproj_file)
#try:
N_parproj = [int(R.next()) for i in range(self.N_shells)]
# Initialise P, here a double list of matrices:
Proj_Mat_pc = [ [ [ [numpy.zeros([self.shells[ish][3], self.N_Orbitals[ik][isp]], numpy.complex_)
for ir in range(N_parproj[ish])]
for ish in range (self.N_shells) ]
for isp in range(self.Nspinblocs) ]
for ik in range(self.Nk) ]
rotmat_all = [numpy.identity(self.shells[ish][3],numpy.complex_) for ish in xrange(self.N_shells)]
rotmat_all_timeinv = [0 for i in range(self.N_shells)]
for ish in range(self.N_shells):
#print ish
# read first the projectors for this orbital:
for ik in xrange(self.Nk):
for ir in range(N_parproj[ish]):
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(self.N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][i,j] = R.next()
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(self.N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][i,j] += 1j * R.next()
# now read the Density Matrix for this orbital below the energy window:
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(self.shells[ish][3]):
Dens_Mat_below[isp][ish][i,j] = R.next()
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(self.shells[ish][3]):
Dens_Mat_below[isp][ish][i,j] += 1j * R.next()
if (self.SP==0): Dens_Mat_below[isp][ish] /= 2.0
# Global -> local rotation matrix for this shell:
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(self.shells[ish][3]):
rotmat_all[ish][i,j] = R.next()
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(self.shells[ish][3]):
rotmat_all[ish][i,j] += 1j * R.next()
#print Dens_Mat_below[0][ish],Dens_Mat_below[1][ish]
if (self.SP):
rotmat_all_timeinv[ish] = int(R.next())
#except StopIteration : # a more explicit error if the file is corrupted.
# raise "SumK_LDA_Wien2k_input: reading file for Projectors failed!"
R.close()
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile,'a')
if not (self.ParProjSubGrp in ar): ar.create_group(self.ParProjSubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
thingstowrite = ['Dens_Mat_below','N_parproj','Proj_Mat_pc','rotmat_all','rotmat_all_timeinv']
for it in thingstowrite: exec "ar['%s']['%s'] = %s"%(self.ParProjSubGrp,it,it)
del ar
# Symmetries are used,
# Now do the symmetries for all orbitals:
self.read_Symmetry_input(orbits=self.shells,symmfile=self.Symmpar_file,SymmSubGrp=self.SymmParSubGrp,SO=self.SO,SP=self.SP)
def convert_DMFT_input(self):
"""
Reads the input files, and stores the data in the HDFfile
"""
if not (MPI.IS_MASTER_NODE()): return # do it only on master:
MPI.report("Reading input from %s..." % self.LDA_file)
# Read and write only on Master!!!
# R is a generator : each R.Next() will return the next number in the file
R = Read_Fortran_File(self.LDA_file)
try:
EnergyUnit = R.next() # read the energy convertion factor
Nk = int(R.next()) # read the number of k points
k_dep_projection = 1
SP = int(R.next()) # flag for spin-polarised calculation
SO = int(R.next()) # flag for spin-orbit calculation
charge_below = R.next() # total charge below energy window
Density_Required = R.next(
) # total density required, for setting the chemical potential
symm_op = 1 # Use symmetry groups for the k-sum
# the information on the non-correlated shells is not important here, maybe skip:
N_shells = int(R.next(
)) # number of shells (e.g. Fe d, As p, O p) in the unit cell,
# corresponds to index R in formulas
# now read the information about the shells:
shells = [[int(R.next()) for i in range(4)]
for icrsh in range(N_shells)
] # reads iatom, sort, l, dim
N_corr_shells = int(R.next(
)) # number of corr. shells (e.g. Fe d, Ce f) in the unit cell,
# corresponds to index R in formulas
# now read the information about the shells:
corr_shells = [[int(R.next()) for i in range(6)]
for icrsh in range(N_corr_shells)
] # reads iatom, sort, l, dim, SO flag, irep
self.inequiv_shells(
corr_shells
) # determine the number of inequivalent correlated shells, has to be known for further reading...
use_rotations = 1
rotmat = [
numpy.identity(corr_shells[icrsh][3], numpy.complex_)
for icrsh in xrange(N_corr_shells)
]
# read the matrices
rotmat_timeinv = [0 for i in range(N_corr_shells)]
for icrsh in xrange(N_corr_shells):
for i in xrange(corr_shells[icrsh][3]): # read real part:
for j in xrange(corr_shells[icrsh][3]):
rotmat[icrsh][i, j] = R.next()
for i in xrange(corr_shells[icrsh][3]): # read imaginary part:
for j in xrange(corr_shells[icrsh][3]):
rotmat[icrsh][i, j] += 1j * R.next()
if (SP == 1): # read time inversion flag:
rotmat_timeinv[icrsh] = int(R.next())
# Read here the infos for the transformation of the basis:
Nreps = [1 for i in range(self.N_inequiv_corr_shells)]
dim_reps = [0 for i in range(self.N_inequiv_corr_shells)]
T = []
for icrsh in range(self.N_inequiv_corr_shells):
Nreps[icrsh] = int(R.next(
)) # number of representatives ("subsets"), e.g. t2g and eg
dim_reps[icrsh] = [int(R.next()) for i in range(Nreps[icrsh])
] # dimensions of the subsets
# The transformation matrix:
# it is of dimension 2l+1, if no SO, and 2*(2l+1) with SO!!
#T = []
#for ish in xrange(self.N_inequiv_corr_shells):
ll = 2 * corr_shells[self.invshellmap[icrsh]][2] + 1
lmax = ll * (corr_shells[self.invshellmap[icrsh]][4] + 1)
T.append(numpy.zeros([lmax, lmax], numpy.complex_))
# now read it from file:
for i in xrange(lmax):
for j in xrange(lmax):
T[icrsh][i, j] = R.next()
for i in xrange(lmax):
for j in xrange(lmax):
T[icrsh][i, j] += 1j * R.next()
# Spin blocks to be read:
Nspinblocs = SP + 1 - SO # number of spins to read for Norbs and Ham, NOT Projectors
# read the list of N_Orbitals for all k points
N_Orbitals = [[0 for isp in range(Nspinblocs)]
for ik in xrange(Nk)]
for isp in range(Nspinblocs):
for ik in xrange(Nk):
N_Orbitals[ik][isp] = int(R.next())
#print N_Orbitals
# Initialise the projectors:
Proj_Mat = [[[
numpy.zeros([corr_shells[icrsh][3], N_Orbitals[ik][isp]],
numpy.complex_) for icrsh in range(N_corr_shells)
] for isp in range(Nspinblocs)] for ik in range(Nk)]
# Read the projectors from the file:
for ik in xrange(Nk):
for icrsh in range(N_corr_shells):
no = corr_shells[icrsh][3]
# first Real part for BOTH spins, due to conventions in dmftproj:
for isp in range(Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i, j] = R.next()
# now Imag part:
for isp in range(Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i, j] += 1j * R.next()
# now define the arrays for weights and hopping ...
BZ_weights = numpy.ones([Nk], numpy.float_) / float(
Nk) # w(k_index), default normalisation
Hopping = [[
numpy.zeros([N_Orbitals[ik][isp], N_Orbitals[ik][isp]],
numpy.complex_) for isp in range(Nspinblocs)
] for ik in xrange(Nk)]
# weights in the file
for ik in xrange(Nk):
BZ_weights[ik] = R.next()
# if the sum over spins is in the weights, take it out again!!
sm = sum(BZ_weights)
BZ_weights[:] /= sm
# Grab the H
# we use now the convention of a DIAGONAL Hamiltonian!!!!
for isp in range(Nspinblocs):
for ik in xrange(Nk):
no = N_Orbitals[ik][isp]
for i in xrange(no):
Hopping[ik][isp][i, i] = R.next() * EnergyUnit
#keep some things that we need for reading parproj:
self.N_shells = N_shells
self.shells = shells
self.N_corr_shells = N_corr_shells
self.corr_shells = corr_shells
self.Nspinblocs = Nspinblocs
self.N_Orbitals = N_Orbitals
self.Nk = Nk
self.SO = SO
self.SP = SP
self.EnergyUnit = EnergyUnit
except StopIteration: # a more explicit error if the file is corrupted.
raise "SumK_LDA : reading file HMLT_file failed!"
R.close()
#print Proj_Mat[0]
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile, 'a')
if not (self.LDASubGrp in ar): ar.create_group(self.LDASubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
ar[self.LDASubGrp]['EnergyUnit'] = EnergyUnit
ar[self.LDASubGrp]['Nk'] = Nk
ar[self.LDASubGrp]['k_dep_projection'] = k_dep_projection
ar[self.LDASubGrp]['SP'] = SP
ar[self.LDASubGrp]['SO'] = SO
ar[self.LDASubGrp]['charge_below'] = charge_below
ar[self.LDASubGrp]['Density_Required'] = Density_Required
ar[self.LDASubGrp]['symm_op'] = symm_op
ar[self.LDASubGrp]['N_shells'] = N_shells
ar[self.LDASubGrp]['shells'] = shells
ar[self.LDASubGrp]['N_corr_shells'] = N_corr_shells
ar[self.LDASubGrp]['corr_shells'] = corr_shells
ar[self.LDASubGrp]['use_rotations'] = use_rotations
ar[self.LDASubGrp]['rotmat'] = rotmat
ar[self.LDASubGrp]['rotmat_timeinv'] = rotmat_timeinv
ar[self.LDASubGrp]['Nreps'] = Nreps
ar[self.LDASubGrp]['dim_reps'] = dim_reps
ar[self.LDASubGrp]['T'] = T
ar[self.LDASubGrp]['N_Orbitals'] = N_Orbitals
ar[self.LDASubGrp]['Proj_Mat'] = Proj_Mat
ar[self.LDASubGrp]['BZ_weights'] = BZ_weights
ar[self.LDASubGrp]['Hopping'] = Hopping
del ar
# Symmetries are used,
# Now do the symmetries for correlated orbitals:
self.read_Symmetry_input(orbits=corr_shells,
symmfile=self.Symm_file,
SymmSubGrp=self.SymmSubGrp,
SO=SO,
SP=SP)
def convert_bands_input(self,BandsSubGrp = 'SumK_LDA_Bands'):
"""
Converts the input for momentum resolved spectral functions, and stores it in BandsSubGrp in the
HDF5.
"""
if not (MPI.IS_MASTER_NODE()): return
self.BandsSubGrp = BandsSubGrp
MPI.report("Reading bands input from %s..."%self.Band_file)
R = Read_Fortran_File(self.Band_file)
try:
Nk = int(R.next())
# read the list of N_Orbitals for all k points
N_Orbitals = [ [0 for isp in range(self.Nspinblocs)] for ik in xrange(Nk)]
for isp in range(self.Nspinblocs):
for ik in xrange(Nk):
N_Orbitals[ik][isp] = int(R.next())
# Initialise the projectors:
Proj_Mat = [ [ [numpy.zeros([self.corr_shells[icrsh][3], N_Orbitals[ik][isp]], numpy.complex_)
for icrsh in range (self.N_corr_shells)]
for isp in range(self.Nspinblocs)]
for ik in range(Nk) ]
# Read the projectors from the file:
for ik in xrange(Nk):
for icrsh in range(self.N_corr_shells):
no = self.corr_shells[icrsh][3]
# first Real part for BOTH spins, due to conventions in dmftproj:
for isp in range(self.Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i,j] = R.next()
# now Imag part:
for isp in range(self.Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i,j] += 1j * R.next()
Hopping = [ [numpy.zeros([N_Orbitals[ik][isp],N_Orbitals[ik][isp]],numpy.complex_)
for isp in range(self.Nspinblocs)] for ik in xrange(Nk) ]
# Grab the H
# we use now the convention of a DIAGONAL Hamiltonian!!!!
for isp in range(self.Nspinblocs):
for ik in xrange(Nk) :
no = N_Orbitals[ik][isp]
for i in xrange(no):
Hopping[ik][isp][i,i] = R.next() * self.EnergyUnit
# now read the partial projectors:
N_parproj = [int(R.next()) for i in range(self.N_shells)]
# Initialise P, here a double list of matrices:
Proj_Mat_pc = [ [ [ [numpy.zeros([self.shells[ish][3], N_Orbitals[ik][isp]], numpy.complex_)
for ir in range(N_parproj[ish])]
for ish in range (self.N_shells) ]
for isp in range(self.Nspinblocs) ]
for ik in range(Nk) ]
for ish in range(self.N_shells):
for ik in xrange(Nk):
for ir in range(N_parproj[ish]):
for isp in range(self.Nspinblocs):
for i in xrange(self.shells[ish][3]): # read real part:
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][i,j] = R.next()
for i in xrange(self.shells[ish][3]): # read imaginary part:
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][i,j] += 1j * R.next()
except StopIteration : # a more explicit error if the file is corrupted.
raise "SumK_LDA : reading file HMLT_file failed!"
R.close()
# reading done!
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile,'a')
if not (self.BandsSubGrp in ar): ar.create_group(self.BandsSubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
thingstowrite = ['Nk','N_Orbitals','Proj_Mat','Hopping','N_parproj','Proj_Mat_pc']
for it in thingstowrite: exec "ar['%s']['%s'] = %s"%(self.BandsSubGrp,it,it)
#ar[self.BandsSubGrp]['Nk'] = Nk
#ar[self.BandsSubGrp]['N_Orbitals'] = N_Orbitals
#ar[self.BandsSubGrp]['Proj_Mat'] = Proj_Mat
#self.Proj_Mat = Proj_Mat
#self.N_Orbitals = N_Orbitals
#self.Nk = Nk
#self.Hopping = Hopping
del ar
def SetDoubleCounting(self, densmat, U_interact, J_Hund, orb=0, useDCformula=0, useval=None):
"""Sets the double counting term for inequiv orbital orb
useDCformula=0: LDA+U FLL double counting, useDCformula=1: Held's formula.
useDCformula=2: AMF
Be sure that you use the correct interaction Hamiltonian!"""
# if (not hasattr(self,"dc_imp")): self.__initDC()
dm = [{} for i in xrange(self.N_corr_shells)]
for i in xrange(self.N_corr_shells):
l = self.corr_shells[i][3] # *(1+self.corr_shells[i][4])
for j in xrange(len(self.GFStruct_corr[i])):
dm[i]["%s" % self.GFStruct_corr[i][j][0]] = numpy.zeros([l, l], numpy.float_)
for icrsh in xrange(self.N_corr_shells):
iorb = self.shellmap[icrsh] # iorb is the index of the inequivalent shell corresponding to icrsh
if iorb == orb:
# do this orbital
l = self.corr_shells[icrsh][3] # *(1+self.corr_shells[icrsh][4])
for j in xrange(len(self.GFStruct_corr[icrsh])):
self.dc_imp[icrsh]["%s" % self.GFStruct_corr[icrsh][j][0]] = numpy.identity(l, numpy.float_)
# transform the CTQMC blocks to the full matrix:
for ibl in range(len(self.GFStruct_Solver[iorb])):
for i in range(len(self.GFStruct_Solver[iorb][ibl][1])):
for j in range(len(self.GFStruct_Solver[iorb][ibl][1])):
bl = self.GFStruct_Solver[iorb][ibl][0]
ind1 = self.GFStruct_Solver[iorb][ibl][1][i]
ind2 = self.GFStruct_Solver[iorb][ibl][1][j]
dm[icrsh][self.mapinv[iorb][bl]][ind1, ind2] = densmat[bl][
i, j
].real # only real part relevant for trace
M = self.corr_shells[icrsh][3]
Ncr = {}
Ncrtot = 0.0
a_list = [a for a, al in self.GFStruct_corr[icrsh]]
for bl in a_list:
Ncr[bl] = dm[icrsh][bl].trace()
Ncrtot += Ncr[bl]
# average the densities if there is no SP:
if self.SP == 0:
for bl in a_list:
Ncr[bl] = Ncrtot / len(a_list)
# correction for SO: we have only one block in this case, but in DC we need N/2
elif self.SP == 1 and self.SO == 1:
for bl in a_list:
Ncr[bl] = Ncrtot / 2.0
if useval is None:
if useDCformula == 0:
self.DCenerg[icrsh] = U_interact / 2.0 * Ncrtot * (Ncrtot - 1.0)
for bl in a_list:
Uav = U_interact * (Ncrtot - 0.5) - J_Hund * (Ncr[bl] - 0.5)
self.dc_imp[icrsh][bl] *= Uav
self.DCenerg[icrsh] -= J_Hund / 2.0 * (Ncr[bl]) * (Ncr[bl] - 1.0)
MPI.report("DC for shell %(icrsh)i and block %(bl)s = %(Uav)f" % locals())
elif useDCformula == 1:
self.DCenerg[icrsh] = (
(U_interact + J_Hund * (2.0 - (M - 1)) / (2 * M - 1)) / 2.0 * Ncrtot * (Ncrtot - 1.0)
)
for bl in a_list:
# Held's formula, with U_interact the interorbital onsite interaction
Uav = (U_interact + J_Hund * (2.0 - (M - 1)) / (2 * M - 1)) * (Ncrtot - 0.5)
self.dc_imp[icrsh][bl] *= Uav
MPI.report("DC for shell %(icrsh)i and block %(bl)s = %(Uav)f" % locals())
elif useDCformula == 2:
self.DCenerg[icrsh] = 0.5 * U_interact * Ncrtot * Ncrtot
for bl in a_list:
# AMF
Uav = U_interact * (Ncrtot - Ncr[bl] / M) - J_Hund * (Ncr[bl] - Ncr[bl] / M)
self.dc_imp[icrsh][bl] *= Uav
self.DCenerg[icrsh] -= (U_interact + (M - 1) * J_Hund) / M * 0.5 * Ncr[bl] * Ncr[bl]
MPI.report("DC for shell %(icrsh)i and block %(bl)s = %(Uav)f" % locals())
# output:
MPI.report("DC energy for shell %s = %s" % (icrsh, self.DCenerg[icrsh]))
else:
a_list = [a for a, al in self.GFStruct_corr[icrsh]]
for bl in a_list:
self.dc_imp[icrsh][bl] *= useval
self.DCenerg[icrsh] = useval * Ncrtot
# output:
MPI.report("DC for shell %(icrsh)i = %(useval)f" % locals())
MPI.report("DC energy = %s" % self.DCenerg[icrsh])
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")
def analyse_BS(self, threshold=0.00001, includeshells=None):
""" Determines the Greens function block structure from the simple point integration"""
dm = self.simplepointdensmat()
densmat = [dm[self.invshellmap[ish]] for ish in xrange(self.N_inequiv_corr_shells)]
if includeshells is None:
includeshells = range(self.N_inequiv_corr_shells)
for ish in includeshells:
# self.GFStruct_Solver.append([])
self.GFStruct_Solver[ish] = []
a_list = [a for a, al in self.GFStruct_corr[self.invshellmap[ish]]]
for a in a_list:
dm = densmat[ish][a]
dmbool = abs(dm) > threshold # gives an index list of entries larger that threshold
offdiag = []
for i in xrange(len(dmbool)):
for j in xrange(i, len(dmbool)):
if (dmbool[i, j]) & (i != j):
offdiag.append([i, j])
NBlocs = len(dmbool)
blocs = [[i] for i in range(NBlocs)]
for i in range(len(offdiag)):
if offdiag[i][0] != offdiag[i][1]:
for j in range(len(blocs[offdiag[i][1]])):
blocs[offdiag[i][0]].append(blocs[offdiag[i][1]][j])
del blocs[offdiag[i][1]]
for j in range(i + 1, len(offdiag)):
if offdiag[j][0] == offdiag[i][1]:
offdiag[j][0] = offdiag[i][0]
if offdiag[j][1] == offdiag[i][1]:
offdiag[j][1] = offdiag[i][0]
if offdiag[j][0] > offdiag[i][1]:
offdiag[j][0] -= 1
if offdiag[j][1] > offdiag[i][1]:
offdiag[j][1] -= 1
offdiag[j].sort()
NBlocs -= 1
for i in range(NBlocs):
blocs[i].sort()
self.GFStruct_Solver[ish].append(("%s%s" % (a, i), blocs[i]))
# map is the mapping of the blocs from the SK blocs to the CTQMC blocs:
self.map[ish][a] = range(len(dmbool))
for ibl in range(NBlocs):
for j in range(len(blocs[ibl])):
self.map[ish][a][blocs[ibl][j]] = "%s%s" % (a, ibl)
self.mapinv[ish]["%s%s" % (a, ibl)] = a
# now calculate degeneracies of orbitals:
dm = {}
for bl in self.GFStruct_Solver[ish]:
bln = bl[0]
ind = bl[1]
# get dm for the blocks:
dm[bln] = numpy.zeros([len(ind), len(ind)], numpy.complex_)
for i in range(len(ind)):
for j in range(len(ind)):
dm[bln][i, j] = densmat[ish][self.mapinv[ish][bln]][ind[i], ind[j]]
for bl in self.GFStruct_Solver[ish]:
for bl2 in self.GFStruct_Solver[ish]:
if dm[bl[0]].shape == dm[bl2[0]].shape:
if ((abs(dm[bl[0]] - dm[bl2[0]]) < threshold).all()) and (bl[0] != bl2[0]):
# check if it was already there:
ind1 = -1
ind2 = -2
for n, ind in enumerate(self.deg_shells[ish]):
if bl[0] in ind:
ind1 = n
if bl2[0] in ind:
ind2 = n
if (ind1 < 0) and (ind2 >= 0):
self.deg_shells[ish][ind2].append(bl[0])
elif (ind1 >= 0) and (ind2 < 0):
self.deg_shells[ish][ind1].append(bl2[0])
elif (ind1 < 0) and (ind2 < 0):
self.deg_shells[ish].append([bl[0], bl2[0]])
if MPI.IS_MASTER_NODE():
ar = HDF_Archive(self.HDFfile, "a")
ar[self.LDAdata]["GFStruct_Solver"] = self.GFStruct_Solver
ar[self.LDAdata]["map"] = self.map
ar[self.LDAdata]["mapinv"] = self.mapinv
try:
ar[self.LDAdata]["deg_shells"] = self.deg_shells
except:
MPI.report("deg_shells not stored, degeneracies not found")
del ar
return densmat
def __call__ (self, Sigma, mu=0, eta = 0, Field = None, Epsilon_Hat=None, Res = None, SelectedBlocks = ()):
"""
- Computes :
Res <- \[ \sum_k (\omega + \mu - Field - t(k) - Sigma(k,\omega)) \]
if Res is None, it returns a new GF with the results.
otherwise, Res must be a GF, in which the calculation is done, and which is then returned.
(this allows chain calculation : SK(mu = mu,Sigma = Sigma, Res = G).total_density()
which computes the sumK into G, and returns the density of G.
- Sigma can be a X, or a function k-> X or a function k,eps ->X where :
- k is expected to be a 1d-numpy array of size self.dim of float,
containing the k vector in the basis of the RBZ (i.e. -0.5< k_i <0.5)
- eps is t(k)
- X is anything such that X[BlockName] can be added/subtracted to a GFBloc for BlockName in SelectedBlocks.
e.g. X can be a GF (with at least the SelectedBlocks), or a dictionnary BlockName -> array
if the array has the same dimension as the GF blocks (for example to add a static Sigma).
- Field : Any k independant object to be added to the GF
- Epsilon_Hat : a function of eps_k returning a matrix, the dimensions of Sigma
- SelectedBlocks : The calculation is done with the SAME t(k) for all blocks. If this list is not None
only the blocks in this list are calculated.
e.g. G and Sigma have block indices 'up' and 'down'.
if SelectedBlocks ==None : 'up' and 'down' are calculated
if SelectedBlocks == ['up'] : only 'up' is calculated. 'down' is 0.
"""
S = Sigma.View_SelectedBlocks(SelectedBlocks) if SelectedBlocks else Sigma
Gres = Res if Res else Sigma.copy()
G = Gres.View_SelectedBlocks(SelectedBlocks) if SelectedBlocks else Gres
# check input
assert self.Orthogonal_Basis, "Local_G : must be orthogonal. non ortho cases not checked."
assert isinstance(G,GF), "G must be a GF"
assert len(list(set([g.N1 for i,g in G]))) == 1
assert self.BZ_weights.shape[0] == self.N_kpts(), "Internal Error"
no = list(set([g.N1 for i,g in G]))[0]
Sigma_Nargs = len(inspect.getargspec(Sigma)[0]) if callable (Sigma) else 0
assert Sigma_Nargs <=2 , "Sigma function is not of the correct type. See Documentation"
# Initialize
G.zero()
tmp,tmp2 = G.copy(),G.copy()
mupat = mu * numpy.identity(no, numpy.complex_)
tmp <<= iOmega_n
if Field != None : tmp -= Field
if Sigma_Nargs==0: tmp -= Sigma # substract Sigma once for all
# Loop on k points...
for w, k, eps_k in izip(*[MPI.slice_array(A) for A in [self.BZ_weights, self.BZ_Points, self.Hopping]]):
eps_hat = Epsilon_Hat(eps_k) if Epsilon_Hat else eps_k
tmp2 <<= tmp
tmp2 -= tmp2.NBlocks * [eps_hat - mupat]
if Sigma_Nargs == 1: tmp2 -= Sigma (k)
elif Sigma_Nargs ==2: tmp2 -= Sigma (k,eps_k)
tmp2.invert()
tmp2 *= w
G += tmp2
G <<= MPI.all_reduce(MPI.world,G,lambda x,y : x+y)
MPI.barrier()
return Gres
def __init__(self,
Beta,
Norb,
U_interact=None,
J_Hund=None,
GFStruct=False,
map=False,
use_spinflip=False,
useMatrix=True,
l=2,
T=None,
dimreps=None,
irep=None,
deg_orbs=[],
Sl_Int=None):
self.offset = 0
self.use_spinflip = use_spinflip
self.Norb = Norb
if (useMatrix):
if not (Sl_Int is None):
Umat = Umatrix(l=l)
assert len(Sl_Int) == (l + 1), "Sl_Int has the wrong length"
if (type(Sl_Int) == ListType):
Rcl = numpy.array(Sl_Int)
else:
Rcl = Sl_Int
Umat(T=T, Rcl=Rcl)
else:
if ((U_interact == None) and (J_Hund == None)):
MPI.report("Give U,J or Slater integrals!!!")
assert 0
Umat = Umatrix(U_interact=U_interact, J_Hund=J_Hund, l=l)
Umat(T=T)
Umat.ReduceMatrix()
if (Umat.N == Umat.Nmat):
# Transformation T is of size 2l+1
self.U = Umat.U
self.Up = Umat.Up
else:
# Transformation is of size 2(2l+1)
self.U = Umat.U
# now we have the reduced matrices U and Up, we need it for tail fitting anyways
if (use_spinflip):
#Take the 4index Umatrix
# check for imaginary matrix elements:
if (abs(Umat.Ufull.imag) > 0.0001).any():
MPI.report(
"WARNING: complex interaction matrix!! Ignoring imaginary part for the moment!"
)
MPI.report(
"If you want to change this, look into Wien2k/Solver_MultiBand.py"
)
self.U4ind = Umat.Ufull.real
# this will be changed for arbitrary irep:
# use only one subgroup of orbitals?
if not (irep is None):
#print irep, dimreps
assert not (dimreps is None
), "Dimensions of the representatives are missing!"
assert Norb == dimreps[
irep - 1], "Dimensions of dimrep and Norb do not fit!"
for ii in range(irep - 1):
self.offset += dimreps[ii]
else:
if ((U_interact == None) and (J_Hund == None)):
MPI.report("For Kanamori representation, give U and J!!")
assert 0
self.U = numpy.zeros([Norb, Norb], numpy.float_)
self.Up = numpy.zeros([Norb, Norb], numpy.float_)
for i in range(Norb):
for j in range(Norb):
if (i == j):
self.Up[i, i] = U_interact + 2.0 * J_Hund
else:
self.Up[i, j] = U_interact
self.U[i, j] = U_interact - J_Hund
if (GFStruct):
assert map, "give also the mapping!"
self.map = map
else:
# standard GFStruct and map
GFStruct = [('%s' % (ud), [n for n in range(Norb)])
for ud in ['up', 'down']]
self.map = {
'up': ['up' for v in range(self.Norb)],
'down': ['down' for v in range(self.Norb)]
}
#print GFStruct,self.map
if (use_spinflip == False):
Hamiltonian = self.__setHamiltonian_density()
else:
if (useMatrix):
Hamiltonian = self.__setfullHamiltonian_Slater()
else:
Hamiltonian = self.__setfullHamiltonian_Kanamori(J_Hund=J_Hund)
Quantum_Numbers = self.__setQuantumNumbers(GFStruct)
# Determine if there are only blocs of size 1:
self.blocssizeone = True
for ib in GFStruct:
if (len(ib[1]) > 1): self.blocssizeone = False
# now initialize the solver with the stuff given above:
Solver.__init__(self,
Beta=Beta,
GFstruct=GFStruct,
H_Local=Hamiltonian,
Quantum_Numbers=Quantum_Numbers)
#self.SetGlobalMoves(deg_orbs)
self.N_Cycles = 10000
self.Nmax_Matrix = 100
self.N_Time_Slices_Delta = 10000
#if ((len(GFStruct)==2*Norb) and (use_spinflip==False)):
if ((self.blocssizeone) and (use_spinflip == False)):
self.Use_Segment_Picture = True
else:
self.Use_Segment_Picture = False
def __init__(self, HDFfile, mu = 0.0, hfield = 0.0, UseLDABlocs = False, LDAdata = 'SumK_LDA', Symmcorrdata = 'SymmCorr',
ParProjdata = 'SumK_LDA_ParProj', Symmpardata = 'SymmPar', Bandsdata = 'SumK_LDA_Bands'):
"""
Initialises the class from data previously stored into an HDF5
"""
if not (type(HDFfile)==StringType):
MPI.report("Give a string for the HDF5 filename to read the input!")
else:
self.HDFfile = HDFfile
self.LDAdata = LDAdata
self.ParProjdata = ParProjdata
self.Bandsdata = Bandsdata
self.Symmpardata = Symmpardata
self.Symmcorrdata = Symmcorrdata
self.blocnames= [ ['up','down'], ['ud'] ]
self.NspinblocsGF = [2,1]
self.Gupf = None
self.hfield = hfield
# read input from HDF:
thingstoread = ['EnergyUnit','Nk','k_dep_projection','SP','SO','charge_below','Density_Required',
'symm_op','N_shells','shells','N_corr_shells','corr_shells','use_rotations','rotmat','rotmat_timeinv','Nreps',
'dim_reps','T','N_Orbitals','Proj_Mat','BZ_weights','Hopping']
optionalthings = ['GFStruct_Solver','mapinv','map','Chemical_Potential','dc_imp','DCenerg','deg_shells']
#ar=HDF_Archive(self.HDFfile,'a')
#del ar
self.retval = self.read_input_from_HDF(SubGrp=self.LDAdata,thingstoread=thingstoread,optionalthings=optionalthings)
#ar=HDF_Archive(self.HDFfile,'a')
#del ar
if (self.SO) and (abs(self.hfield)>0.000001):
self.hfield=0.0
MPI.report("For SO, the external magnetic field is not implemented, setting it to 0!!")
self.inequiv_shells(self.corr_shells) # determine the number of inequivalent correlated shells
# field to convert blocnames to indices
self.names_to_ind = [{}, {}]
for ibl in range(2):
for inm in range(self.NspinblocsGF[ibl]):
self.names_to_ind[ibl][self.blocnames[ibl][inm]] = inm * self.SP #(self.Nspinblocs-1)
# GF structure used for the local things in the k sums
self.GFStruct_corr = [ [ (al, range( self.corr_shells[i][3])) for al in self.blocnames[self.corr_shells[i][4]] ]
for i in xrange(self.N_corr_shells) ]
if not (self.retval['GFStruct_Solver']):
# No GFStruct was stored in HDF, so first set a standard one:
self.GFStruct_Solver = [ [ (al, range( self.corr_shells[self.invshellmap[i]][3]) )
for al in self.blocnames[self.corr_shells[self.invshellmap[i]][4]] ]
for i in xrange(self.N_inequiv_corr_shells) ]
self.map = [ {} for i in xrange(self.N_inequiv_corr_shells) ]
self.mapinv = [ {} for i in xrange(self.N_inequiv_corr_shells) ]
for i in xrange(self.N_inequiv_corr_shells):
for al in self.blocnames[self.corr_shells[self.invshellmap[i]][4]]:
self.map[i][al] = [al for j in range( self.corr_shells[self.invshellmap[i]][3] ) ]
self.mapinv[i][al] = al
if not (self.retval['dc_imp']):
# init the double counting:
self.__initDC()
if not (self.retval['Chemical_Potential']):
self.Chemical_Potential = mu
if not (self.retval['deg_shells']):
self.deg_shells = [ [] for i in range(self.N_inequiv_corr_shells)]
if self.symm_op:
#MPI.report("Do the init for symm:")
self.Symm_corr = Symmetry(HDFfile,subgroup=self.Symmcorrdata)
# determine the smallest blocs, if wanted:
if (UseLDABlocs): dm=self.analyse_BS()
# now save things again to HDF5:
if (MPI.IS_MASTER_NODE()):
ar=HDF_Archive(self.HDFfile,'a')
ar[self.LDAdata]['hfield'] = self.hfield
del ar
self.save()
def __init__(
self,
HDFfile,
mu=0.0,
hfield=0.0,
UseLDABlocs=False,
LDAdata="SumK_LDA",
Symmcorrdata="SymmCorr",
ParProjdata="SumK_LDA_ParProj",
Symmpardata="SymmPar",
Bandsdata="SumK_LDA_Bands",
):
"""
Initialises the class from data previously stored into an HDF5
"""
if not (type(HDFfile) == StringType):
MPI.report("Give a string for the HDF5 filename to read the input!")
else:
self.HDFfile = HDFfile
self.LDAdata = LDAdata
self.ParProjdata = ParProjdata
self.Bandsdata = Bandsdata
self.Symmpardata = Symmpardata
self.Symmcorrdata = Symmcorrdata
self.blocnames = [["up", "down"], ["ud"]]
self.NspinblocsGF = [2, 1]
self.Gupf = None
self.hfield = hfield
# read input from HDF:
thingstoread = [
"EnergyUnit",
"Nk",
"k_dep_projection",
"SP",
"SO",
"charge_below",
"Density_Required",
"symm_op",
"N_shells",
"shells",
"N_corr_shells",
"corr_shells",
"use_rotations",
"rotmat",
"rotmat_timeinv",
"Nreps",
"dim_reps",
"T",
"N_Orbitals",
"Proj_Mat",
"BZ_weights",
"Hopping",
]
optionalthings = [
"GFStruct_Solver",
"mapinv",
"map",
"Chemical_Potential",
"dc_imp",
"DCenerg",
"deg_shells",
]
# ar=HDF_Archive(self.HDFfile,'a')
# del ar
self.retval = self.read_input_from_HDF(
SubGrp=self.LDAdata, thingstoread=thingstoread, optionalthings=optionalthings
)
# ar=HDF_Archive(self.HDFfile,'a')
# del ar
if (self.SO) and (abs(self.hfield) > 0.000001):
self.hfield = 0.0
MPI.report("For SO, the external magnetic field is not implemented, setting it to 0!!")
self.inequiv_shells(self.corr_shells) # determine the number of inequivalent correlated shells
# field to convert blocnames to indices
self.names_to_ind = [{}, {}]
for ibl in range(2):
for inm in range(self.NspinblocsGF[ibl]):
self.names_to_ind[ibl][self.blocnames[ibl][inm]] = inm * self.SP # (self.Nspinblocs-1)
# GF structure used for the local things in the k sums
self.GFStruct_corr = [
[(al, range(self.corr_shells[i][3])) for al in self.blocnames[self.corr_shells[i][4]]]
for i in xrange(self.N_corr_shells)
]
if not (self.retval["GFStruct_Solver"]):
# No GFStruct was stored in HDF, so first set a standard one:
self.GFStruct_Solver = [
[
(al, range(self.corr_shells[self.invshellmap[i]][3]))
for al in self.blocnames[self.corr_shells[self.invshellmap[i]][4]]
]
for i in xrange(self.N_inequiv_corr_shells)
]
self.map = [{} for i in xrange(self.N_inequiv_corr_shells)]
self.mapinv = [{} for i in xrange(self.N_inequiv_corr_shells)]
for i in xrange(self.N_inequiv_corr_shells):
for al in self.blocnames[self.corr_shells[self.invshellmap[i]][4]]:
self.map[i][al] = [al for j in range(self.corr_shells[self.invshellmap[i]][3])]
self.mapinv[i][al] = al
if not (self.retval["dc_imp"]):
# init the double counting:
self.__initDC()
if not (self.retval["Chemical_Potential"]):
self.Chemical_Potential = mu
if not (self.retval["deg_shells"]):
self.deg_shells = [[] for i in range(self.N_inequiv_corr_shells)]
if self.symm_op:
# MPI.report("Do the init for symm:")
self.Symm_corr = Symmetry(HDFfile, subgroup=self.Symmcorrdata)
# determine the smallest blocs, if wanted:
if UseLDABlocs:
dm = self.analyse_BS()
# now save things again to HDF5:
if MPI.IS_MASTER_NODE():
ar = HDF_Archive(self.HDFfile, "a")
ar[self.LDAdata]["hfield"] = self.hfield
del ar
self.save()
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 calc_DensityCorrection(self, Filename="densmat.dat"):
""" Calculates the density correction in order to feed it back to the DFT calculations."""
assert type(Filename) == StringType, "Filename has to be a string!"
ntoi = self.names_to_ind[self.SO]
bln = self.blocnames[self.SO]
# Set up deltaN:
deltaN = {}
for ib in bln:
deltaN[ib] = [
numpy.zeros([self.N_Orbitals[ik][ntoi[ib]], self.N_Orbitals[ik][ntoi[ib]]], numpy.complex_)
for ik in range(self.Nk)
]
ikarray = numpy.array(range(self.Nk))
dens = {}
for ib in bln:
dens[ib] = 0.0
for ik in MPI.slice_array(ikarray):
S = self.latticeGF_Matsubara(ik=ik, mu=self.Chemical_Potential)
for sig, g in S:
deltaN[sig][ik] = S[sig].density()
dens[sig] += self.BZ_weights[ik] * S[sig].total_density()
# put MPI Barrier:
for sig in deltaN:
for ik in range(self.Nk):
deltaN[sig][ik] = MPI.all_reduce(MPI.world, deltaN[sig][ik], lambda x, y: x + y)
dens[sig] = MPI.all_reduce(MPI.world, dens[sig], lambda x, y: x + y)
MPI.barrier()
# now save to file:
if MPI.IS_MASTER_NODE():
if self.SP == 0:
f = open(Filename, "w")
else:
f = open(Filename + "up", "w")
f1 = open(Filename + "dn", "w")
# write chemical potential (in Rydberg):
f.write("%.14f\n" % (self.Chemical_Potential / self.EnergyUnit))
if self.SP != 0:
f1.write("%.14f\n" % (self.Chemical_Potential / self.EnergyUnit))
# write beta in ryderg-1
f.write("%.14f\n" % (S.Beta * self.EnergyUnit))
if self.SP != 0:
f1.write("%.14f\n" % (S.Beta * self.EnergyUnit))
if self.SP == 0:
for ik in range(self.Nk):
f.write("%s\n" % self.N_Orbitals[ik][0])
for inu in range(self.N_Orbitals[ik][0]):
for imu in range(self.N_Orbitals[ik][0]):
valre = (deltaN["up"][ik][inu, imu].real + deltaN["down"][ik][inu, imu].real) / 2.0
valim = (deltaN["up"][ik][inu, imu].imag + deltaN["down"][ik][inu, imu].imag) / 2.0
f.write("%.14f %.14f " % (valre, valim))
f.write("\n")
f.write("\n")
f.close()
elif (self.SP == 1) and (self.SO == 0):
for ik in range(self.Nk):
f.write("%s\n" % self.N_Orbitals[ik][0])
for inu in range(self.N_Orbitals[ik][0]):
for imu in range(self.N_Orbitals[ik][0]):
f.write(
"%.14f %.14f " % (deltaN["up"][ik][inu, imu].real, deltaN["up"][ik][inu, imu].imag)
)
f.write("\n")
f.write("\n")
f.close()
for ik in range(self.Nk):
f1.write("%s\n" % self.N_Orbitals[ik][1])
for inu in range(self.N_Orbitals[ik][1]):
for imu in range(self.N_Orbitals[ik][1]):
f1.write(
"%.14f %.14f " % (deltaN["down"][ik][inu, imu].real, deltaN["down"][ik][inu, imu].imag)
)
f1.write("\n")
f1.write("\n")
f1.close()
else:
for ik in range(self.Nk):
f.write("%s\n" % self.N_Orbitals[ik][0])
for inu in range(self.N_Orbitals[ik][0]):
for imu in range(self.N_Orbitals[ik][0]):
f.write(
"%.14f %.14f " % (deltaN["ud"][ik][inu, imu].real, deltaN["ud"][ik][inu, imu].imag)
)
f.write("\n")
f.write("\n")
f.close()
for ik in range(self.Nk):
f1.write("%s\n" % self.N_Orbitals[ik][0])
for inu in range(self.N_Orbitals[ik][0]):
for imu in range(self.N_Orbitals[ik][0]):
f1.write(
"%.14f %.14f " % (deltaN["ud"][ik][inu, imu].real, deltaN["ud"][ik][inu, imu].imag)
)
f1.write("\n")
f1.write("\n")
f1.close()
return deltaN, dens
def __init__(self, Beta, Norb, U_interact=None, J_Hund=None, GFStruct=False, map=False, use_spinflip=False,
useMatrix = True, l=2, T=None, dimreps=None, irep=None, deg_orbs = [], Sl_Int = None):
self.offset = 0
self.use_spinflip = use_spinflip
self.Norb = Norb
if (useMatrix):
if not (Sl_Int is None):
Umat = Umatrix(l=l)
assert len(Sl_Int)==(l+1),"Sl_Int has the wrong length"
if (type(Sl_Int)==ListType):
Rcl = numpy.array(Sl_Int)
else:
Rcl = Sl_Int
Umat(T=T,Rcl=Rcl)
else:
if ((U_interact==None)and(J_Hund==None)):
MPI.report("Give U,J or Slater integrals!!!")
assert 0
Umat = Umatrix(U_interact=U_interact, J_Hund=J_Hund, l=l)
Umat(T=T)
Umat.ReduceMatrix()
if (Umat.N==Umat.Nmat):
# Transformation T is of size 2l+1
self.U = Umat.U
self.Up = Umat.Up
else:
# Transformation is of size 2(2l+1)
self.U = Umat.U
# now we have the reduced matrices U and Up, we need it for tail fitting anyways
if (use_spinflip):
#Take the 4index Umatrix
# check for imaginary matrix elements:
if (abs(Umat.Ufull.imag)>0.0001).any():
MPI.report("WARNING: complex interaction matrix!! Ignoring imaginary part for the moment!")
MPI.report("If you want to change this, look into Wien2k/Solver_MultiBand.py")
self.U4ind = Umat.Ufull.real
# this will be changed for arbitrary irep:
# use only one subgroup of orbitals?
if not (irep is None):
#print irep, dimreps
assert not (dimreps is None), "Dimensions of the representatives are missing!"
assert Norb==dimreps[irep-1],"Dimensions of dimrep and Norb do not fit!"
for ii in range(irep-1):
self.offset += dimreps[ii]
else:
if ((U_interact==None)and(J_Hund==None)):
MPI.report("For Kanamori representation, give U and J!!")
assert 0
self.U = numpy.zeros([Norb,Norb],numpy.float_)
self.Up = numpy.zeros([Norb,Norb],numpy.float_)
for i in range(Norb):
for j in range(Norb):
if (i==j):
self.Up[i,i] = U_interact + 2.0*J_Hund
else:
self.Up[i,j] = U_interact
self.U[i,j] = U_interact - J_Hund
if (GFStruct):
assert map, "give also the mapping!"
self.map = map
else:
# standard GFStruct and map
GFStruct = [ ('%s'%(ud),[n for n in range(Norb)]) for ud in ['up','down'] ]
self.map = {'up' : ['up' for v in range(self.Norb)], 'down' : ['down' for v in range(self.Norb)]}
#print GFStruct,self.map
if (use_spinflip==False):
Hamiltonian = self.__setHamiltonian_density()
else:
if (useMatrix):
Hamiltonian = self.__setfullHamiltonian_Slater()
else:
Hamiltonian = self.__setfullHamiltonian_Kanamori(J_Hund = J_Hund)
Quantum_Numbers = self.__setQuantumNumbers(GFStruct)
# Determine if there are only blocs of size 1:
self.blocssizeone = True
for ib in GFStruct:
if (len(ib[1])>1): self.blocssizeone = False
# now initialize the solver with the stuff given above:
Solver.__init__(self,
Beta = Beta,
GFstruct = GFStruct,
H_Local = Hamiltonian,
Quantum_Numbers = Quantum_Numbers )
#self.SetGlobalMoves(deg_orbs)
self.N_Cycles = 10000
self.Nmax_Matrix = 100
self.N_Time_Slices_Delta= 10000
#if ((len(GFStruct)==2*Norb) and (use_spinflip==False)):
if ((self.blocssizeone) and (use_spinflip==False)):
self.Use_Segment_Picture = True
else:
self.Use_Segment_Picture = False
def run(self):
"""
"""
MPI.barrier()
if MPI.size == 1: # single machine. Avoid the fork
while not (self.Finished()):
n = self.Next()
if n != None:
self.Treate(self.The_Function(n), 0)
return
# Code for multiprocessor machines
RequestList, pid = [], 0 # the pid of the child on the master
node_running, node_stopped = MPI.size * [False], MPI.size * [False]
if MPI.rank == 0:
while not (self.Finished()) or pid or [n for n in node_running if n] != []:
# Treat the request which have self.Finished
def keep_request(r):
# if not(MPI.test(r)) : return True
# if r.message !=None : self.Treate(*r.message)
# node_running[r.status.source] = False
T = r.test()
if T is None:
return True
value = T[0]
if value != None:
self.Treate(*value)
node_running[T[1].source] = False
return False
RequestList = filter(keep_request, RequestList)
# send new calculation to the nodes or "stop" them
for node in [n for n in range(1, MPI.size) if not (node_running[n] or node_stopped[n])]:
# open('tmp','a').write("master : comm to node %d %s\n"%(node,self.Finished()))
MPI.send(self.Finished(), node)
if not (self.Finished()):
MPI.send(self.Next(), node) # send the data for the computation
node_running[node] = True
RequestList.append(MPI.irecv(node)) # Post the receive
else:
node_stopped[node] = True
# Look if the child process on the master has self.Finished.
if not (pid) or os.waitpid(pid, os.WNOHANG):
if pid:
RR = cPickle.load(open("res_master", "r"))
if RR != None:
self.Treate(*RR)
if not (self.Finished()):
pid = os.fork()
currently_calculated_by_master = self.Next()
if pid == 0: # we are on the child
if currently_calculated_by_master:
res = self.The_Function(currently_calculated_by_master)
else:
res = None
cPickle.dump((res, MPI.rank), open("res_master", "w"))
os._exit(0) # Cf python doc. Used for child only.
else:
pid = 0
if pid:
time.sleep(self.SleepTime) # so that most of the time is for the actual calculation on the master
else: # not master
while not (MPI.recv(0)): # master will first send a Finished flag
omega = MPI.recv(0)
if omega == None:
res = None
else:
res = self.The_Function(omega)
MPI.send((res, MPI.rank), 0)
MPI.barrier()
def convert_bands_input(self, BandsSubGrp='SumK_LDA_Bands'):
"""
Converts the input for momentum resolved spectral functions, and stores it in BandsSubGrp in the
HDF5.
"""
if not (MPI.IS_MASTER_NODE()): return
self.BandsSubGrp = BandsSubGrp
MPI.report("Reading bands input from %s..." % self.Band_file)
R = Read_Fortran_File(self.Band_file)
try:
Nk = int(R.next())
# read the list of N_Orbitals for all k points
N_Orbitals = [[0 for isp in range(self.Nspinblocs)]
for ik in xrange(Nk)]
for isp in range(self.Nspinblocs):
for ik in xrange(Nk):
N_Orbitals[ik][isp] = int(R.next())
# Initialise the projectors:
Proj_Mat = [[[
numpy.zeros([self.corr_shells[icrsh][3], N_Orbitals[ik][isp]],
numpy.complex_)
for icrsh in range(self.N_corr_shells)
] for isp in range(self.Nspinblocs)] for ik in range(Nk)]
# Read the projectors from the file:
for ik in xrange(Nk):
for icrsh in range(self.N_corr_shells):
no = self.corr_shells[icrsh][3]
# first Real part for BOTH spins, due to conventions in dmftproj:
for isp in range(self.Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i, j] = R.next()
# now Imag part:
for isp in range(self.Nspinblocs):
for i in xrange(no):
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat[ik][isp][icrsh][i, j] += 1j * R.next()
Hopping = [[
numpy.zeros([N_Orbitals[ik][isp], N_Orbitals[ik][isp]],
numpy.complex_) for isp in range(self.Nspinblocs)
] for ik in xrange(Nk)]
# Grab the H
# we use now the convention of a DIAGONAL Hamiltonian!!!!
for isp in range(self.Nspinblocs):
for ik in xrange(Nk):
no = N_Orbitals[ik][isp]
for i in xrange(no):
Hopping[ik][isp][i, i] = R.next() * self.EnergyUnit
# now read the partial projectors:
N_parproj = [int(R.next()) for i in range(self.N_shells)]
# Initialise P, here a double list of matrices:
Proj_Mat_pc = [[[[
numpy.zeros([self.shells[ish][3], N_Orbitals[ik][isp]],
numpy.complex_) for ir in range(N_parproj[ish])
] for ish in range(self.N_shells)]
for isp in range(self.Nspinblocs)]
for ik in range(Nk)]
for ish in range(self.N_shells):
for ik in xrange(Nk):
for ir in range(N_parproj[ish]):
for isp in range(self.Nspinblocs):
for i in xrange(
self.shells[ish][3]): # read real part:
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][
i, j] = R.next()
for i in xrange(self.shells[ish]
[3]): # read imaginary part:
for j in xrange(N_Orbitals[ik][isp]):
Proj_Mat_pc[ik][isp][ish][ir][
i, j] += 1j * R.next()
except StopIteration: # a more explicit error if the file is corrupted.
raise "SumK_LDA : reading file HMLT_file failed!"
R.close()
# reading done!
#-----------------------------------------
# Store the input into HDF5:
ar = HDF_Archive(self.HDFfile, 'a')
if not (self.BandsSubGrp in ar): ar.create_group(self.BandsSubGrp)
# The subgroup containing the data. If it does not exist, it is created.
# If it exists, the data is overwritten!!!
thingstowrite = [
'Nk', 'N_Orbitals', 'Proj_Mat', 'Hopping', 'N_parproj',
'Proj_Mat_pc'
]
for it in thingstowrite:
exec "ar['%s']['%s'] = %s" % (self.BandsSubGrp, it, it)
#ar[self.BandsSubGrp]['Nk'] = Nk
#ar[self.BandsSubGrp]['N_Orbitals'] = N_Orbitals
#ar[self.BandsSubGrp]['Proj_Mat'] = Proj_Mat
#self.Proj_Mat = Proj_Mat
#self.N_Orbitals = N_Orbitals
#self.Nk = Nk
#self.Hopping = Hopping
del ar
def check_inputDOS(self, ommin, ommax, N_om, Beta=10, broadening=0.01):
delta_om = (ommax - ommin) / (N_om - 1)
Mesh = numpy.zeros([N_om], numpy.float_)
DOS = {}
for bn in self.blocnames[self.SO]:
DOS[bn] = numpy.zeros([N_om], numpy.float_)
DOSproj = [{} for icrsh in range(self.N_inequiv_corr_shells)]
DOSproj_orb = [{} for icrsh in range(self.N_inequiv_corr_shells)]
for icrsh in range(self.N_inequiv_corr_shells):
for bn in self.blocnames[self.corr_shells[self.invshellmap[icrsh]]
[4]]:
dl = self.corr_shells[self.invshellmap[icrsh]][3]
DOSproj[icrsh][bn] = numpy.zeros([N_om], numpy.float_)
DOSproj_orb[icrsh][bn] = numpy.zeros([dl, dl, N_om],
numpy.float_)
for i in range(N_om):
Mesh[i] = ommin + delta_om * i
# init:
Gloc = []
for icrsh in range(self.N_corr_shells):
b_list = [a for a, al in self.GFStruct_corr[icrsh]]
glist = lambda: [
GFBloc_ReFreq(Indices=al, Beta=Beta, MeshArray=Mesh)
for a, al in self.GFStruct_corr[icrsh]
]
Gloc.append(GF(NameList=b_list, BlockList=glist(), Copy=False))
for icrsh in xrange(self.N_corr_shells):
Gloc[icrsh].zero() # initialize to zero
for ik in xrange(self.Nk):
Gupf = self.latticeGF_realfreq(ik=ik,
mu=self.Chemical_Potential,
broadening=broadening,
Beta=Beta,
Mesh=Mesh,
withSigma=False)
Gupf *= self.BZ_weights[ik]
# non-projected DOS
for iom in range(N_om):
for sig, gf in Gupf:
asd = gf._data.array[:, :,
iom].imag.trace() / (-3.1415926535)
DOS[sig][iom] += asd
for icrsh in xrange(self.N_corr_shells):
tmp = Gloc[icrsh].copy()
for sig, gf in tmp:
tmp[sig] <<= self.downfold(ik, icrsh, sig, Gupf[sig],
gf) # downfolding G
Gloc[icrsh] += tmp
if (self.symm_op != 0): Gloc = self.Symm_corr.symmetrise(Gloc)
if (self.use_rotations):
for icrsh in xrange(self.N_corr_shells):
for sig, gf in Gloc[icrsh]:
Gloc[icrsh][sig] <<= self.rotloc(icrsh,
gf,
direction='toLocal')
# Gloc can now also be used to look at orbitally resolved quantities
for ish in range(self.N_inequiv_corr_shells):
for sig, gf in Gloc[self.invshellmap[ish]]: # loop over spins
for iom in range(N_om):
DOSproj[ish][sig][
iom] += gf._data.array[:, :, iom].imag.trace() / (
-3.1415926535)
DOSproj_orb[ish][
sig][:, :, :] += gf._data.array[:, :, :].imag / (
-3.1415926535)
# output:
if (MPI.IS_MASTER_NODE()):
for bn in self.blocnames[self.SO]:
f = open('DOS%s.dat' % bn, 'w')
for i in range(N_om):
f.write("%s %s\n" % (Mesh[i], DOS[bn][i]))
f.close()
for ish in range(self.N_inequiv_corr_shells):
f = open('DOS%s_proj%s.dat' % (bn, ish), 'w')
for i in range(N_om):
f.write("%s %s\n" % (Mesh[i], DOSproj[ish][bn][i]))
f.close()
for i in range(self.corr_shells[self.invshellmap[ish]][3]):
for j in range(
i, self.corr_shells[self.invshellmap[ish]][3]):
Fname = 'DOS' + bn + '_proj' + str(
ish) + '_' + str(i) + '_' + str(j) + '.dat'
f = open(Fname, 'w')
for iom in range(N_om):
f.write("%s %s\n" %
(Mesh[iom], DOSproj_orb[ish][bn][i, j,
iom]))
f.close()
def run(self):
"""
"""
MPI.barrier()
if MPI.size==1 : # single machine. Avoid the fork
while not(self.Finished()):
n = self.Next()
if n!=None :
self.Treate(self.The_Function(n),0)
return
# Code for multiprocessor machines
RequestList,pid = [],0 # the pid of the child on the master
node_running,node_stopped= MPI.size*[False],MPI.size*[False]
if MPI.rank==0 :
while not(self.Finished()) or pid or [n for n in node_running if n] != [] :
# Treat the request which have self.Finished
def keep_request(r) :
#if not(MPI.test(r)) : return True
#if r.message !=None : self.Treate(*r.message)
#node_running[r.status.source] = False
T = r.test()
if T is None : return True
value = T[0]
if value !=None : self.Treate(*value)
node_running[T[1].source] = False
return False
RequestList = filter(keep_request,RequestList)
# send new calculation to the nodes or "stop" them
for node in [ n for n in range(1,MPI.size) if not(node_running[n] or node_stopped[n]) ] :
#open('tmp','a').write("master : comm to node %d %s\n"%(node,self.Finished()))
MPI.send(self.Finished(),node)
if not(self.Finished()) :
MPI.send(self.Next(),node) # send the data for the computation
node_running[node] = True
RequestList.append(MPI.irecv(node)) #Post the receive
else :
node_stopped[node] = True
# Look if the child process on the master has self.Finished.
if not(pid) or os.waitpid(pid,os.WNOHANG) :
if pid :
RR = cPickle.load(open("res_master",'r'))
if RR != None : self.Treate(*RR)
if not(self.Finished()) :
pid=os.fork();
currently_calculated_by_master = self.Next()
if pid==0 : # we are on the child
if currently_calculated_by_master :
res = self.The_Function(currently_calculated_by_master)
else:
res = None
cPickle.dump((res,MPI.rank),open('res_master','w'))
os._exit(0) # Cf python doc. Used for child only.
else : pid=0
if (pid): time.sleep(self.SleepTime) # so that most of the time is for the actual calculation on the master
else : # not master
while not(MPI.recv(0)) : # master will first send a Finished flag
omega = MPI.recv(0)
if omega ==None :
res = None
else :
res = self.The_Function(omega)
MPI.send((res,MPI.rank),0)
MPI.barrier()
def read_Symmetry_input(self, orbits, symmfile, SymmSubGrp, SO, SP):
"""
Reads input for the symmetrisations from symmfile, which is case.sympar or case.symqmc.
"""
if not (MPI.IS_MASTER_NODE()): return
MPI.report("Reading symmetry input from %s..." % symmfile)
N_orbits = len(orbits)
R = Read_Fortran_File(symmfile)
try:
Ns = int(R.next()) # Number of symmetry operations
Natoms = int(R.next()) # number of atoms involved
perm = [[int(R.next()) for i in xrange(Natoms)]
for j in xrange(Ns)] # list of permutations of the atoms
if SP:
timeinv = [int(R.next()) for j in xrange(Ns)
] # timeinversion for SO xoupling
else:
timeinv = [0 for j in xrange(Ns)]
# Now read matrices:
mat = []
for iNs in xrange(Ns):
mat.append([
numpy.zeros([orbits[orb][3], orbits[orb][3]],
numpy.complex_) for orb in xrange(N_orbits)
])
for orb in range(N_orbits):
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat[iNs][orb][i, j] = R.next() # real part
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat[iNs][orb][i,
j] += 1j * R.next() # imaginary part
# determine the inequivalent shells:
#SHOULD BE FINALLY REMOVED, PUT IT FOR ALL ORBITALS!!!!!
#self.inequiv_shells(orbits)
mat_tinv = [
numpy.identity(orbits[orb][3], numpy.complex_)
for orb in range(N_orbits)
]
if ((SO == 0) and (SP == 0)):
# here we need an additional time inversion operation, so read it:
for orb in range(N_orbits):
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat_tinv[orb][i, j] = R.next() # real part
for i in xrange(orbits[orb][3]):
for j in xrange(orbits[orb][3]):
mat_tinv[orb][i,
j] += 1j * R.next() # imaginary part
except StopIteration: # a more explicit error if the file is corrupted.
raise "Symmetry : reading file failed!"
R.close()
# Save it to the HDF:
ar = HDF_Archive(self.HDFfile, 'a')
if not (SymmSubGrp in ar): ar.create_group(SymmSubGrp)
thingstowrite = [
'Ns', 'Natoms', 'perm', 'orbits', 'SO', 'SP', 'timeinv', 'mat',
'mat_tinv'
]
for it in thingstowrite:
exec "ar['%s']['%s'] = %s" % (SymmSubGrp, it, it)
del ar