def reduce_k(self,Cfunc,klist):
'''
Get the new C+\dag C type operator(like non-interacting Hamiltonian) for specific k.
Parameters:
:Cfunc: function, Cfunc(k) gives the expectation matrix at k.
:klist: ndarray(Nk,vdim), the k-space.
Return:
The reduced operator.
'''
nband=Cfunc(zeros(2)).shape[-1]
ncell=prod(self.size)
offsets=self.offsets.reshape([-1,self.lattice.dimension])
slist=offsets[:,0,newaxis]*self.lattice.a[0]+offsets[:,1,newaxis]*self.lattice.a[1]
#get the equivalent k-points.
b=toreciprocal(self.lattice.a*self.L[:,newaxis])
Q=(b[0]+b[1])/2.
equivk=meshgrid_v([arange(l) for l in self.L],vecs=b).reshape([-1,b.shape[-1]])
if self.form=='x':
equivk=concatenate([equivk,equivk+Q])
#calculate C matrix.
C=ndarray([len(klist),ncell,ncell,nband,nband],dtype=complex128)
for ik,k in enumerate(klist):
print '@%s'%ik
cs=[Cfunc(k+q) for q in equivk]
for s1 in xrange(ncell):
for s2 in xrange(ncell):
C[ik,s1,s2]=sum([ci*exp(1j*(k+q).dot(slist[s2]-slist[s1])) for ci,q in zip(cs,equivk)],axis=0)/prod(self.L)/(1. if self.form=='#' else 2.)
return swapaxes(C,2,3).reshape([len(klist),ncell*nband,ncell*nband])
def get_kspace(self):
'''
Get the new k space.
'''
a=self.lattice.a
L=self.L
if self.form=='x':
a=array([L[0]*a[0]+L[1]*a[1],L[0]*a[0]-L[1]*a[1]])
else:
a=array([L[0]*a[0],L[1]*a[1]])
b=toreciprocal(a)
ks=KSpace(b,N=self.lattice.N/L)
ks.special_points['X']=array([b[0]/2.,b[1]/2.,-b[0]/2.,-b[1]/2.])
ks.special_points['M']=array([(b[0]+b[1])/2.,(b[0]-b[1])/2.,(-b[0]-b[1])/2.,(-b[0]+b[1])/2.])
return ks
