def jack_Cv_averages(self, EJack):
"""
returns the M(=self.nsubsets) Cv Jackknife averages (from the combined subsets)
"""
CvJack = np.zeros((self.nsubsets,self.T.size))
if self.rect is 1:
for i in xrange(self.nsubsets):
CvJack[i][:] = compute_Z(np.array(EJack[i][:]), self.T, (self.K - self.n), P=self.P, ndof=self.ndof, imp=self.imp, live=self.live)[1]
else:
for i in xrange(self.nsubsets):
CvJack[i][:] = compute_cv_c(np.array(EJack[i][:]), float(P), (self.K - self.n), float(self.Tmin), float(self.Tmax), self.nT, float(self.ndof), self.imp, self.live)
#print 'CvJack ',CvJack
return np.array(CvJack)
def Cv_singles(self,Esplit):
"""
returns the single Cvs
"""
CvSingle = np.zeros((self.nsubsets,self.T.size))
if self.rect is 1:
for i in xrange(self.nsubsets):
CvSingle[i][:] = compute_Z(np.array(Esplit[i][:]), self.T, (self.n), P=self.P, ndof=self.ndof, imp=self.imp, live=self.live)[1]
else:
for i in xrange(self.nsubsets):
CvSingle[i][:] = compute_cv_c(np.array(Esplit[i][:]), float(P), (self.n), float(self.Tmin), float(self.Tmax), self.nT, float(self.ndof), self.imp, self.live)
#print 'CvSingle ',CvSingle
return np.array(CvSingle)
def Cv_singles(self, Esplit):
"""
returns the single Cvs
"""
CvSingle = np.zeros((self.nsubsets, self.T.size))
if self.rect is 1:
for i in xrange(self.nsubsets):
CvSingle[i][:] = compute_Z(np.array(Esplit[i][:]),
self.T, (self.n),
P=self.P,
ndof=self.ndof,
imp=self.imp,
live=self.live)[1]
else:
for i in xrange(self.nsubsets):
CvSingle[i][:] = compute_cv_c(np.array(Esplit[i][:]), float(P),
(self.n), float(self.Tmin),
float(self.Tmax), self.nT,
float(self.ndof), self.imp,
self.live)
#print 'CvSingle ',CvSingle
return np.array(CvSingle)
def jack_Cv_averages(self, EJack):
"""
returns the M(=self.nsubsets) Cv Jackknife averages (from the combined subsets)
"""
CvJack = np.zeros((self.nsubsets, self.T.size))
if self.rect is 1:
for i in xrange(self.nsubsets):
CvJack[i][:] = compute_Z(np.array(EJack[i][:]),
self.T, (self.K - self.n),
P=self.P,
ndof=self.ndof,
imp=self.imp,
live=self.live)[1]
else:
for i in xrange(self.nsubsets):
CvJack[i][:] = compute_cv_c(np.array(EJack[i][:]),
float(P), (self.K - self.n),
float(self.Tmin),
float(self.Tmax), self.nT,
float(self.ndof), self.imp,
self.live)
#print 'CvJack ',CvJack
return np.array(CvJack)
#make nd-arrays C contiguous
energies = np.array(energies, order='C')
energies_Cv = np.array(energies_Cv, order='C')
##########################################################################################################
print "parallel nprocessors", P
Tmin = args.Tmin
Tmax = args.Tmax
nT = args.nT
dT = (Tmax-Tmin) / nT
T = np.array([Tmin + dT*i for i in range(nT)])
if args.rect is 1:
print "rectangular"
Cv = compute_Z(energies_Cv, T, args.K*len(args.fname), P=P, ndof=args.ndof, imp=args.imp, live=args.live)[1]
else:
print "trapezoidal"
Cv = compute_cv_c(energies_Cv, float(P), float(args.K*len(args.fname)), float(Tmin), float(Tmax), nT, float(args.ndof), args.imp, args.live)
Cv_stdev, Cv_singles, CvMom1 = run_jackknife(energies, args.N, (args.K*len(args.fname)), Tmin, Tmax, nT, P, args.ndof, args.B, args.rect, args.imp, args.live)
with open('cv_std_K{K}_Nsub{N}_d{ndof}_B{B}.dat'.format(K = args.K,N=args.N,ndof=args.ndof,B=args.B), "w") as fout:
fout.write("#T Cv stdev\n")
for vals in zip(T, Cv, Cv_stdev):
fout.write("%g %g %g\n" % vals)
#CvMom1 is also the jackknife estimate
with open('cv_jack_est_K{K}_Nsub{N}_d{ndof}_B{B}.dat'.format(K = args.K,N=args.N,ndof=args.ndof,B=args.B), "w") as fout:
fout.write("#T CvMom1 stdev\n")
for vals in zip(T, CvMom1, Cv_stdev):
##########################################################################################################
print "parallel nprocessors", P
Tmin = args.Tmin
Tmax = args.Tmax
nT = args.nT
dT = (Tmax - Tmin) / nT
T = np.array([Tmin + dT * i for i in range(nT)])
if args.rect is 1:
print "rectangular"
Cv = compute_Z(energies_Cv,
T,
args.K * len(args.fname),
P=P,
ndof=args.ndof,
imp=args.imp,
live=args.live)[1]
else:
print "trapezoidal"
Cv = compute_cv_c(energies_Cv, float(P),
float(args.K * len(args.fname)), float(Tmin),
float(Tmax), nT, float(args.ndof), args.imp,
args.live)
Cv_stdev, Cv_singles, CvMom1 = run_jackknife(energies, args.N,
(args.K * len(args.fname)),
Tmin, Tmax, nT, P, args.ndof,
args.B, args.rect, args.imp,
args.live)
