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)
def _get_avg_data(self):
data = normalize(self.data,
self.background,
self.variance,
norm=self.norm,
scale=self.scale,
mask=self.mask)
if self.size is not None:
t, data = log_average(data, self.size)
else:
t = np.arange(data.shape[-1])
return t, np.nanmean(data, axis=-2)
if binning == BINNING_ERROR:
ax2.semilogx(x[1:],
std[1:],
marker=MARKERS.get(norm, "o"),
linestyle='',
fillstyle="none",
label="${}$".format(LABELS.get(norm)))
else:
#ax1.semilogx(x[1:],y[0,1:],linestyle = ':',fillstyle = "none")
ax2.semilogx(x[1:], std[1:], linestyle=':', fillstyle="none")
ax1.plot(x[1:], g1(x[1:], i, j), "k", label="$g$")
#: take first run, norm = 3 data for g estimation
x, g = log_average(data[0, 3, i, j, :])
g = denoise(g)
g = decreasing(g)
g = g.clip(0, 1)
#ax1.plot(x[1:],g[1:], "k:",label = "denoised")
x = np.arange(NFRAMES)
ax1.plot(x[1:], w[1:], "k--", label="$w$")
ax1.plot(x[1:], wp[1:], "k:", label="$w'$")
#ax2.set_ylim(ax1.get_ylim())
x, err2 = merge_multilevel(multilevel(err2, binning=0))
x, err3 = merge_multilevel(multilevel(err3, binning=0))
x, err6 = merge_multilevel(multilevel(err6, binning=0))
#load in numpy array
fft_array, = asarrays(fft, NFRAMES_RANDOM)
if __name__ == "__main__":
import os.path as p
#: now perform auto correlation calculation with default parameters
data = acorr(fft_array, t = video_simulator.t, n = int(NFRAMES/DT_RANDOM))
bg, var = stats(fft_array)
for norm in (1,2,3,5,6,7,9,10,11):
#: perform normalization and merge data
data_lin = normalize(data, bg, var, scale = True, norm = norm)
#: change size, to define time resolution in log space
x,y = log_average(data_lin, size = 16)
#: save the normalized data to numpy files
np.save(p.join(DATA_PATH, "corr_random_t.npy"),x*DT_RANDOM)
np.save(p.join(DATA_PATH, "corr_random_data_norm{}.npy".format(norm)),y)
#: inspect the data
viewer = CorrViewer(scale = True)
viewer.set_data(data,bg, var)
viewer.set_mask(k = 25, angle = 0, sector = 30)
viewer.plot()
viewer.show()
