def measure(self,scale,dmmanagers,threadz=False,**kwargs):
'''
measure an operator(overload).
scale:
the EScale instance.
tdmanager:
the timeline manager, and instance TDManager.
threadz:
multi-threading over z.
**kwargs:
* FDM: wmin(-1),wmax(1),smearing_method(gaussian), b(0.5), tol(0 if zero temperature else 1e-12), nw(50)
* RDM: smearing_method(log_gaussian), b(0.4)
'''
AL=[]
ntask=(scale.nz-1)/SIZE+1
for i in xrange(scale.nz):
if threadz:
if i/ntask==RANK:
print 'Measuring Sigma on core %s with Hamiltonian size %s.'%(RANK,dmmanagers[i].H.N)
w,A=self.get_expect(scale,dmmanagers[i],**kwargs)
AL.append(A)
else:
w,A=self.get_expect(scale,dmmanagers[i],**kwargs)
AL.append(A)
if threadz:
AL=COMM.gather(AL,root=0)
if RANK==0:
AL=concatenate(AL,axis=0)
w,A=w,mean(AL,axis=0)
A=COMM.bcast(A,root=0)
self.data=w,A
else:
self.data=w,mean(AL,axis=0)
def measure(self, scale, dmmanagers, threadz=False, **kwargs):
'''
measure an operator(overload).
scale:
the EScale instance.
tdmanager:
the timeline manager, and instance TDManager.
threadz:
multi-threading over z.
**kwargs:
* FDM: wmin(-1),wmax(1),smearing_method(gaussian), b(0.5), tol(0 if zero temperature else 1e-12), nw(50)
* RDM: smearing_method(log_gaussian), b(0.4)
'''
AL = []
ntask = (scale.nz - 1) / SIZE + 1
for i in xrange(scale.nz):
if threadz:
if i / ntask == RANK:
print 'Measuring Sigma on core %s with Hamiltonian size %s.' % (
RANK, dmmanagers[i].H.N)
w, A = self.get_expect(scale, dmmanagers[i], **kwargs)
AL.append(A)
else:
w, A = self.get_expect(scale, dmmanagers[i], **kwargs)
AL.append(A)
if threadz:
AL = COMM.gather(AL, root=0)
if RANK == 0:
AL = concatenate(AL, axis=0)
w, A = w, mean(AL, axis=0)
A = COMM.bcast(A, root=0)
self.data = w, A
else:
self.data = w, mean(AL, axis=0)
def measure(self, scale, dmmanagers, threadz=False, **kwargs):
'''
measure an operator(overload).
scale:
the EScale instance.
tdmanager:
the timeline manager, and instance TDManager.
threadz:
multi-threading over z.
**kwargs:
* FDM: wmin(-1),wmax(1),smearing_method(gaussian), b(0.5), tol(0 if zero temperature else 1e-12), nw(50)
* RDM: smearing_method(log_gaussian), b(0.4)
'''
AL = []
BL = []
ntask = (scale.nz - 1) / SIZE + 1
for i in xrange(scale.nz):
if threadz:
if i / ntask == RANK:
print 'Measuring Sigma on core %s with Hamiltonian size %s.' % (
RANK, dmmanagers[i].H.N)
w, A, B = self.get_expect(scale, dmmanagers[i], **kwargs)
AL.append(A)
BL.append(B)
else:
w, A, B = self.get_expect(scale, dmmanagers[i], **kwargs)
AL.append(A)
BL.append(B)
if threadz:
AL = COMM.gather(AL, root=0)
BL = COMM.gather(BL, root=0)
if RANK == 0:
AL = concatenate(AL, axis=0)
BL = concatenate(BL, axis=0)
w, A, B = w, mean(AL, axis=0), mean(BL, axis=0)
A, B = COMM.bcast(A, root=0), COMM.bcast(B, root=0)
else:
A, B = mean(AL, axis=0), mean(BL, axis=0)
print 'Fixing Occational Negative Eigenvalues of A.'
aevals, aevecs = eigh(A)
amin = aevals.min()
if amin < 0:
print 'Fixing Negative spectrum up to %s' % amin
aevals[aevals < 0] = 0
Alist = eigen_combine(aevals, aevecs)
self.data = w, A, B
def measure(self,scale,dmmanagers,threadz=False,**kwargs):
'''
measure an operator(overload).
scale:
the EScale instance.
tdmanager:
the timeline manager, and instance TDManager.
threadz:
multi-threading over z.
**kwargs:
* FDM: wmin(-1),wmax(1),smearing_method(gaussian), b(0.5), tol(0 if zero temperature else 1e-12), nw(50)
* RDM: smearing_method(log_gaussian), b(0.4)
'''
AL=[];BL=[]
ntask=(scale.nz-1)/SIZE+1
for i in xrange(scale.nz):
if threadz:
if i/ntask==RANK:
print 'Measuring Sigma on core %s with Hamiltonian size %s.'%(RANK,dmmanagers[i].H.N)
w,A,B=self.get_expect(scale,dmmanagers[i],**kwargs)
AL.append(A)
BL.append(B)
else:
w,A,B=self.get_expect(scale,dmmanagers[i],**kwargs)
AL.append(A)
BL.append(B)
if threadz:
AL=COMM.gather(AL,root=0)
BL=COMM.gather(BL,root=0)
if RANK==0:
AL=concatenate(AL,axis=0)
BL=concatenate(BL,axis=0)
w,A,B=w,mean(AL,axis=0),mean(BL,axis=0)
A,B=COMM.bcast(A,root=0),COMM.bcast(B,root=0)
else:
A,B=mean(AL,axis=0),mean(BL,axis=0)
print 'Fixing Occational Negative Eigenvalues of A.'
aevals,aevecs=eigh(A)
amin=aevals.min()
if amin<0:
print 'Fixing Negative spectrum up to %s'%amin
aevals[aevals<0]=0
Alist=eigen_combine(aevals,aevecs)
self.data=w,A,B
