def MCAvg(ff, obs, nonequi, i=-1):
if i == -1:
dat = read_cpmc(ff, [obs])
else:
dat = zip(*read_cpmc(ff, [obs]))[0]
# print dat
mc.equi_prof(dat[nonequi:])
mc.binning_prof(dat[nonequi:], NBmin=10)
def MC_analysis_vec(vdat, nonequi, binsize):
vdat = list(map(list, zip(*vdat)))
# Plot equilibrium and binning profile for the 1st element in the observable vector
# mc.equi_prof (vdat[1][nonequi:])
# mc.binning_prof (vdat[1][nonequi:], NBmin=10)
Os, Oes = [], []
for dat in vdat:
O, Oerr = mc.binning(dat[nonequi:], binsize)
Os.append(O)
Oes.append(Oerr)
return Os, Oes
def MC_analysis(dat, nonequi, binsize):
# mc.equi_prof (dat)
# mc.binning_prof (dat, NBmin=2)
O, Oerr = mc.binning(dat[nonequi:], binsize)
return O, Oerr
