def coreFunc(a):
"""runs the below for an angle, a
Allows multiprocessing across a range of values for a
"""
i,d,a = a
model = Model(x0, pot=pot, rng=rng, step_size = d,
n_steps = n_steps, rand_steps=False)
c = acorr.Autocorrelations_1d(model)
c.runModel(n_samples=n_samples, n_burn_in=n_burn_in, mixing_angle = a, verbose=False, verb_pos=i)
acs = c.getAcorr(separations, opFn, norm = False)
# get parameters generated
pacc = c.model.p_acc
pacc_err = np.std(c.model.sampler.accept.accept_rates)
ans = errors.uWerr(c.op_samples, acorr=acs) # get errors
op_av, op_diff, _, itau, itau_diff, _, acns = ans # extract data
w = errors.getW(itau, itau_diff, n=n_samples) # get window length
if not np.isnan(w):
acns_err = errors.acorrnErr(acns, w, n_samples) # get error estimates
acs = acns
else:
acns_err = np.full(acs.shape, np.nan)
itau = itau_diff = np.nan
return op_av, op_diff, acs, acns_err, itau, itau_diff, pacc, pacc_err, w
def coreFunc(a):
"""runs the below for an angle, a"""
i, a, n_samples = a
model = Model(
x0,
pot=pot,
spacing=spacing, # set up model
rng=rng,
step_size=step_size,
n_steps=n_steps,
rand_steps=rand_steps)
c = acorr.Autocorrelations_1d(model) # set up autocorrs
c.runModel(
n_samples=n_samples,
n_burn_in=n_burn_in, # run MCMC
mixing_angle=a,
verbose=explicit_prog,
verb_pos=i)
cfn = opFn(c.model.samples) # run func on HMC samples
# get parameters generated
p = c.model.p_acc # get acceptance rates at each M-H step
# Calculating integrated autocorrelations
ans = errors.uWerr(cfn)
xx, f_diff, _, itau, itau_diff, itaus, _ = ans
w = errors.getW(itau, itau_diff, n=cfn.shape[0])
out = itau, itau_diff, f_diff, w
return xx, p, itau, itau_diff, f_diff, w
def preparePlot(op_samples,
ans,
n,
itau_theory=None,
acn_theory=None,
mcore=False):
"""Prepares the plot according to the output of uWerr
Required Inputs
op_samples :: np.ndarray :: function acted upon the HMC samples
ans :: tuple :: output form uWerr
n :: int :: number of samples from MCMC
Optional Input
itau_theory :: float :: theoretical iTau value
acn_theory :: float :: theoretical ac values plotted against ficticous time NOT window length
mcore :: bool :: flag that ans is a nested list of l_ans = [ans, ans, ...]
"""
f_aav, f_diff, f_ddiff, itau, itau_diff, itaus, acn = ans
if not np.isnan(itau):
w = errors.getW(itau, itau_diff, n) # get window
l = w * 2
itaus_diff = errors.itauErrors(itaus, n=n) # calcualte error in itau
acn_diff = errors.acorrnErr(acn, w, n) # note acn not ac is used
itau_label = r"$\tau_{\text{int}}(w_{\text{best}} = " + r"{}) = ".format(int(w)) \
+ r"{:.2f} \pm {:.2f}$".format(itau, itau_diff)
else:
w = np.nan
l = acn.size // 2
itaus_diff = np.zeros(acn.shape)
acn_diff = np.zeros(acn.shape)
itau_label = r"$\tau_{\text{int}}(w_{\text{best}}:\ \text{n/a}) = \text{n/a}$"
# a quick hack
l = min(acn.size, itaus.size, l)
if acn_theory is not None: acn_theory = acn_theory[:l]
g_int = np.cumsum(np.nan_to_num(acn[1:l] /
acn[0])) # recreate the g_int function
g = np.asarray([errors.gW(t, v, 1.5, n) for t, v in enumerate(g_int, 1)
]) # recreate the gW function
x = np.arange(l) # same size for itaus and acn
g = np.insert(g, 0, np.nan) # g starts at x[1] not x[0]
# create items for plotting
gta = (g[:l], None, ''), (itaus[:l], itaus_diff[:l], itau_label,
itau_theory), (acn[:l], acn_diff[:l], '',
acn_theory)
return x, gta, w
def coreFunc(a):
"""runs the below for an angle, a
Allows multiprocessing across a range of values for a
"""
i, a = a
model = Model(x0,
pot=pot,
spacing=spacing,
rng=rng,
step_size=step_size,
n_steps=n_steps,
rand_steps=rand_steps)
c = acorr.Autocorrelations_1d(model)
c.runModel(n_samples=n_samples,
n_burn_in=n_burn_in,
mixing_angle=a,
verbose=True,
verb_pos=i)
store.store(c.model.samples, file_name, '_samples')
store.store(c.model.traj, file_name, '_trajs')
acs = c.getAcorr(separations,
opFn,
norm=False,
prll_map=prll_map,
max_sep=50) # non norm for uWerr
# get parameters generated
traj = np.cumsum(c.trajs)
p = c.model.p_acc
xx = c.op_mean
acorr_seps = c.acorr_ficticous_time
acorr_counts = c.acorr_counts
store.store(p, file_name, '_probs')
store.store(acs, file_name, '_acs')
ans = errors.uWerr(c.op_samples, acorr=acs) # get errors
_, _, _, itau, itau_diff, _, acns = ans # extract data
w = errors.getW(itau, itau_diff, n=n_samples) # get window length
acns_err = errors.acorrnErr(acns, w, n_samples) # get error estimates
if rand_steps: # correct for N being different for each correlation
acns_err *= np.sqrt(n_samples) / acorr_counts
return xx, acns, acns_err, p, w, traj, acorr_seps
separations = np.linspace(min_sep, max_sep, (max_sep - min_sep) / res + 1) # multicore result = prll_map(aFn, separations, verbose=True) a, counts = zip(*result) a = np.asarray(a) # the autocorrelation array counts = np.array(counts) # the counts at each separation # mask = 1-np.isnan(a) # a = a[mask] a = a * counts # grab errors print 'getting errors...' ans = uWerr(op_samples, a) _, _, _, itau, itau_diff, _, acns = ans # extract data my_w = getW(itau, itau_diff, n=n_samples) # get window length my_err = acorrnErr(acns, my_w, n_samples) # get autocorr errors my_err *= np.sqrt(n_samples) / np.sqrt(counts) # theory calclations th_x = np.linspace(min_sep, max_sep, 1000) th = np.array([hmc(p, phi, r, i) for i in th_x]) from scipy.optimize import curve_fit fn = lambda x, a, b, c: a * np.cos(b * x)**2 + c expFn = lambda x, a, b, c: a * np.exp(-b * x) + c cos_sq = curve_fit(fn, separations[:50], acns[:50]) exp_fit = curve_fit(expFn, separations[1:80], counts[1:80]) norm_cnt = expFn(0, *exp_fit[0]) / counts[1]
print 'Comparing autocorrelation calculations...'
# assert that the autocorrelation routine is the same
av_xx = comparison_xx.mean()
norm = ((comparison_xx-av_xx)**2).mean()
my_acorr = np.asarray(map(lambda s: myAcorr(comparison_xx, av_xx, s), np.asarray(separations)))
christian_class = Christian_Autocorrelation(comparison_xx)
christian_acorr = christian_class.acf()[:c_len]
christian_acorr = np.asarray(christian_acorr)
diffs = christian_acorr[:my_acorr.size] - my_acorr
print " > av. difference: {}".format(diffs.mean())
print 'Checking integration window calculation:'
christian_tint, christian_dtint, christian_w = christian_class.tauintW(False)
_,_,my_w = windowing(comparison_xx, av_xx, 1.0, comparison_xx.size, fast=True)
print "Christian Window:{}".format(christian_w)
print "My Window:{}".format(my_w)
ans = uWerr(comparison_xx, acorr=my_acorr) # get errors
_, _, _, itau, itau_diff, _, acns = ans # extract data
my_w = getW(itau, itau_diff, n=comparison_xx.size) # get window length
my_err = acorrnErr(acns, my_w, comparison_xx.size) # get autocorr errors
christian_acorr = np.asarray(christian_acorr)
christian_err = acorrnErr(christian_acorr, christian_w, comparison_xx.size)
all_plot = preparePlot(christian_acorr[:2*my_w], acns[:2*my_w], my_err[:2*my_w], christian_err[:2*my_w])
store(all_plot, file_name, '_allPlot')
plot(save = saveOrDisplay(save, file_name+"_compareAc"), **all_plot)
