def cosh_effective_mass_errors(self, dt, fast=True, period=None):
if fast: logging.info("cosh emass computed fast method")
period = self.period
T = self.period_check(period)
jkasv = self.jackknife_average_sub_vev()
jkemass = {}
for cfg in self.configs:
asvc = jkasv[cfg]
emass = {}
for t in self.times[dt:-dt]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
guess = (1.0 / float(dt))*math.acosh((asvc[t+dt] + asvc[t-dt])/(2.0*asvc[t]))
if fast:
emass[t] = guess
else:
emass[t] = newton.newton_cosh_for_m(t,t+dt,asvc, guess,T)
except ValueError:
#logging.debug("invalid argument to log, setting to zero")
emass[t] = 0.0
except ZeroDivisionError:
logging.debug("div by zero, setting to zero")
emass[t] = 0.0
except KeyError:
logging.error("index out of range")
jkemass[cfg] = emass
jkemassobj = configtimeobj.Cfgtimeobj.fromDataDict(jkemass)
effmass_dt = self.cosh_effective_mass(dt, fast=fast, period=period)
return {t: jackknife.errorbars(effmass_dt[t], jkemassobj.get(time=t))
for t in self.times[dt:-dt]}
def cosh_effective_mass(self, dt, fast=True, period=None):
if fast: logging.info("cosh emass computed fast method")
T = self.period_check(period)
asv = self.average_sub_vev()
emass = {}
for t in self.times[dt:-dt]:
if t in self.emass_skip_times:
emass[t] = 0.0
continue
try:
guess = (1.0 / float(dt))*math.acosh((asv[t+dt] + asv[t-dt])/(2.0*asv[t]))
if fast:
emass[t] = guess
else:
emass[t] = newton.newton_cosh_for_m(t,t+dt,asv, guess, T)
except ValueError:
logging.debug("invalid argument to acosh, setting to zero")
emass[t] = float('NaN')
except KeyError:
logging.error("index out of range")
except ZeroDivisionError:
logging.error("Div by zero either dt:{} or average value sub vev {}".format(dt,asv[t]))
emass[t] = float('NaN')
return emass
def plot(cors, funct, time_starts, time_ends, averages, stds, chi, options):
import plot_helpers
import newton
dt = 1
fig = plt.figure()
corplots = {}
corplots[0] = plt.subplot(221)
corplots[1] = plt.subplot(222)
emassplots = {}
emassplots[0] = plt.subplot(223)
emassplots[1] = plt.subplot(224)
for i in [0,1]:
corplots[i].errorbar(cors[i].times,
cors[i].average_sub_vev().values(),
yerr=cors[i].jackknifed_errors().values(), fmt='o')
emass = cors[i].periodic_effective_mass(dt, fast=False, period=options.period)
emass_errs = cors[i].periodic_effective_mass_errors(dt, fast=False).values()
emassplots[i].errorbar(emass.keys(), emass.values(),
yerr=emass_errs, fmt='o')
fn = funct.individual(Nt=options.period)
for i in [0, 1]:
#X = np.array(cors[i].times)
X = np.linspace(time_starts[i], time_ends[i])
X = np.arange(time_starts[i], time_ends[i]+1, 1)
Xe = np.arange(time_starts[i], time_ends[i], 1)
Y = fn.formula((averages[0], averages[i+1]), X)
Ydt = fn.formula((averages[0], averages[i+1]), X+dt)
corplots[i].plot(X, Y)
guess = (1.0 / float(dt)) * np.log(Y/Ydt)
fitemass = {}
Yd = dict(enumerate(Y, start=time_starts[i]))
guess = dict(enumerate(guess, start=time_starts[i]))
for t in range(time_starts[i], time_ends[i]):
fitemass[t] = newton.newton_cosh_for_m(t, t+dt, Yd, guess[t], options.period)
emassplots[i].plot(fitemass.keys(),fitemass.values())
if options.output_stub:
filestub = options.output_stub
logging.info("Saving plot to {}".format(filestub+".png"))
fig.set_size_inches(18.5, 10.5)
plt.savefig(filestub+".png")
else:
plt.show()
