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