def _data_estimator(data, size=8, n=3, multilevel=False):
from cddm.multitau import log_average, merge_multilevel
from cddm.avg import denoise
if multilevel == False:
x, y = log_average(data, size)
else:
x, y = merge_multilevel(data)
#in case we have nans, remove them before denoising, and return only valid data
mask = np.isnan(y)
mask = np.logical_not(np.all(mask, axis=tuple(range(mask.ndim - 1))))
return x[mask], denoise(y[..., mask], n=n)
err6 = sigma_prime_weighted(w, g, delta, 0, 0) #/n**0.5
#err0 = err0.mean(0)
#err1 = err1.mean(0)
#err4 = err4.mean(0)
ax1 = plt.subplot(121)
ax1.set_xscale("log")
ax1.set_xlabel(r"$\tau$")
ax1.set_title(r"$g(\tau), w(\tau)$ @ $q = {}$".format(K))
ax2 = plt.subplot(122)
ax2.set_title(r"$\sigma (\tau)$ @ $q = {}$".format(K))
for binning in (0, 1):
x, y = merge_multilevel(
multilevel(data_regular[:, 2, i, j, :], binning=binning))
if CROSS:
x = x * PERIOD // 2
g = g1(x, i, j)
#g = y.mean(0)
std = (((y - g)**2).mean(axis=0))**0.5
#ax1.semilogx(x[1:],y[:,1:].mean(0),marker = "o", linestyle = '',fillstyle = "none",label = "$g_R$", color = "k")
if binning == BINNING_DATA:
ax1.semilogx(x[1:],
y[0, 1:],
marker="o",
linestyle='',
fillstyle="none",
label="$R$",
color="k")
ax = axs[j]
ax.semilogx(x[1:], y[1:], "-", label="linear", fillstyle="none")
y_multi = multilevel(y, binning=True)
x_multi = multilevel(x, binning=True)
for i, (x, y) in enumerate(zip(x_multi, y_multi)):
ax.semilogx(x[1:],
y[1:],
marker=MARKERS[i % 6],
linestyle="-",
label="level {}".format(i))
x, y = merge_multilevel(y_multi)
ax.semilogx(x[1:], y[1:], "k", label="log")
ax.set_title(TITLES[j].format(KI))
ax.set_xlabel(r"$\tau$")
ax.set_ylabel(r"$g$")
ax.set_ylim(-0.2, 1)
plt.legend()
plt.tight_layout()
if SAVE_FIGS:
plt.savefig("plots/plot_corr_example.pdf")
plt.show()