def read_full_correlator(filename, emass=None, eamp=False, symmetric=False):
logging.info("reading file {}".format(filename))
cor = build_corr.corr_and_vev_from_pickle(filename, None, None)
logging.info("File read.")
if symmetric:
corsym = cor.determine_symmetry()
if corsym is None:
logging.error("called with symmetric but correlator isnt")
raise RuntimeError("called with symmetric but correlator isnt")
logging.info("correlator found to be {}".format(corsym))
cor.make_symmetric()
cor.prune_invalid(delete=False, sigma=2.0)
if emass:
emasses = cor.periodic_effective_mass(1, fast=False, period=emass)
#emasses = cor.periodic_effective_mass(1, fast=False, period=emass)
#emasses = cor.effective_mass(1)
times = emasses.keys()
data = [emasses[t] for t in times]
#errs = cor.periodic_effective_mass_errors(1, fast=False, period=emass)
errs = cor.periodic_effective_mass_errors(1, fast=False, period=emass)
#errs = cor.effective_mass_errors(1)
errors = [errs[t] for t in times]
logging.debug("emasses {}".format(emasses))
logging.debug("errs {}".format(errs))
elif eamp:
eamps = cor.periodic_effective_amp(1, len(cor.times), cor.periodic_effective_mass(1)[32] )
times = eamps.keys()
data = [eamps[t] for t in times]
errors = [0 for t in times]
else:
times = cor.times
data = [cor.average_sub_vev()[t] for t in times]
errors = [cor.jackknifed_errors()[t] for t in times]
d = {"time": times, "correlator": data, "error": errors, "quality": [float('NaN') for t in times]}
df = pd.DataFrame(d)
return df
logging.warn(".boot file exists but is empty!")
except OSError:
logging.info("running fit")
consistant_argument_counts(args)
cors = []
for i in range(len(args.inputfile)):
corrfile = args.inputfile[i]
vev1 = args.vev[i]
vev2 = vev1
if args.vev2:
vev2 = args.vev2[i]
cor = build_corr.corr_and_vev_from_pickle(corrfile, vev1, vev2)
if args.bin:
cor = cor.reduce_to_bins(args.bin)
corsym = cor.determine_symmetry()
if corsym is None:
logging.error("called with symmetric but correlator isnt")
raise RuntimeError("called with symmetric but correlator isnt")
logging.info("correlator found to be {}".format(corsym))
cor.make_symmetric()
cor.prune_invalid(delete=True, sigma=args.prune)
if args.debug_heavycorrection:
import heavy_correction_dicts
beta = [b for b in ["4.17", "4.35", "4.47"] if b in args.inputfile[i]][0]
def determine_fit_range(options):
""" get fit range """
logging.debug("Called with {}".format(options))
files = glob.glob("SymDW_sHtTanh_b2.0_smr3_*x{}x*b{}*0/data/*{}*{}*{}-{}*{}".format(options.period, options.beta, options.heavy, options.flavor, options.smearing1, options.smearing2, options.operator))
if len(files) < 1:
logging.error("no files")
logging.error("looked for SymDW_sHtTanh_b2.0_smr3_*x{}x*b{}*0/data/*{}*{}*{}-{}*{}".format(options.period, options.beta, options.heavy, options.flavor, options.smearing1, options.smearing2, options.operator))
exit(-1)
logging.info(files)
files = files
#fhandles = [open(f) for f in files]
maxt = (options.period/2)-1
rel_err = {t:0 for t in range(1,options.period/2)}
cors = [build_corr.corr_and_vev_from_pickle(f, None, None) for f in files]
p = 1.05
for c in cors:
if options.operator == "PP":
c.make_symmetric()
elif options.operator == "A4P" or options.operator == "PA4":
c.make_symmetric()
maxt = (options.period/2)-4
p = 1.20
else:
c.make_symmetric()
for c in cors:
c.prune_invalid(delete=True, sigma=2.0)
maxt = min(maxt,max(c.times))
for cor in cors:
asv = cor.average_sub_vev()
errs = cor.jackknifed_errors()
masserrs = cor.effective_mass_errors(1)
for t in range(4,maxt):
rel_err[t] = max(errs[t]/asv[t], rel_err[t])
if t > options.period/2 and masserrs[t] > masserrs[t-2]*2:
logging.warning("Emass error doubled in two slices!")
logging.warning("{} {} {}".format(t, masserrs[t] , masserrs[t-2]))
maxt = min(maxt, t-2)
maxt = maxt
mint = -1
for f,cor in zip(files,cors):
emass = cor.periodic_effective_mass(1, fast=False, period=options.period)
errs = cor.periodic_effective_mass_errors(1, fast=False, period=options.period)
prev = emass[3]
for t in range(4, maxt):
if emass[t] > prev:
logging.info("increased at {} for {}".format(t, f))
mint = max(mint,t)
break
logging.debug("{}, {}, {}".format(emass[t]+errs[t]*p > emass[t-1] , emass[t-1]+errs[t-1] > emass[t-2] , emass[t]+errs[t]*p > emass[t-2]))
logging.debug("{} {}, {} {}, {} {}".format(emass[t]+errs[t]*p , emass[t-1] , emass[t-1]+errs[t-1]*p , emass[t-2] , emass[t]+errs[t] , emass[t-2]))
if emass[t]+errs[t]*p > emass[t-1] and emass[t-1]+errs[t-1]*p > emass[t-2] and emass[t]+errs[t]*p > emass[t-2]:
logging.info("const at {} for {}".format(t-1, f))
mint = max(mint,t-1)
break
prev = emass[t]
if mint < 0:
logging.error(files)
logging.error("could not find const")
mint = maxt-4
#exit(-1)
ofilename = options.ofilename
if maxt - mint < 4:
logging.error("found fitrange too small {},{}!!".format(mint,maxt))
if (emass[maxt-4] - emass[maxt])/4.0 < errs[maxt-4]:
mint = maxt-4
logging.error("setting fit range to {},{}!!".format(mint,maxt))
else:
mint = maxt-4
logging.error("setting fit range to {},{}!!".format(mint,maxt))
#exit(-1)
logging.info("fit range deteremined to be {} {}".format(mint,maxt))
logging.info("writing fit range to {}".format(ofilename))
with open(ofilename, 'w') as outfile:
outfile.write("{} {}\n".format(mint, maxt))
