def calculate(binning=1):
out = None
for i in range(NRUN):
print("Run {}/{}".format(i + 1, NRUN))
importlib.reload(dual_video_simulator) #recreates iterator
#reset seed... because we use seed(0) in dual_video_simulator
seed(i)
t1, t2 = dual_video_simulator.t1, dual_video_simulator.t2
video = multiply(dual_video_simulator.video, window_video)
#: if the intesity of light source flickers you can normalize each frame to the intensity of the frame
#video = normalize_video(video)
#: perform rfft2 and crop results, to take only first kimax and first kjmax wavenumbers.
fft = rfft2(video, kimax=51, kjmax=0)
#: you can also normalize each frame with respect to the [0,0] component of the fft
#: this it therefore equivalent to normalize_video
#fft = normalize_fft(fft)
#: now perform auto correlation calculation with default parameters and show live
data, bg, var = iccorr_multi(fft,
t1,
t2,
level_size=16,
binning=binning,
period=PERIOD,
auto_background=True)
#perform normalization and merge data
#5 and 7 are redundand, but we are calulating it for easier indexing
for norm in (1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15):
fast, slow = normalize_multi(data, bg, var, norm=norm, scale=True)
#we merge with binning (averaging) of linear data enabled/disabled
x, y = log_merge(fast, slow, binning=binning)
if out is None:
out = np.empty(shape=(NRUN, 16) + y.shape, dtype=y.dtype)
out[0, norm] = y
else:
out[i, norm] = y
return x, out
from cddm.multitau import iccorr_multi, normalize_multi, log_merge
import matplotlib.pyplot as plt
from conf import PERIOD
import cross_correlate_multi_live
import importlib
importlib.reload(cross_correlate_multi_live) #recreates fft iterator
t1, t2 = cross_correlate_multi_live.t1, cross_correlate_multi_live.t2
fft = cross_correlate_multi_live.fft
#: now perform auto correlation calculation with default parameters and show live
data, bg, var = iccorr_multi(fft,
t1,
t2,
period=PERIOD,
chunk_size=128,
auto_background=True)
i, j = 4, 15
#: plot the results
for norm in (1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15):
fast, slow = normalize_multi(data, bg, var, norm=norm, scale=True)
x, y = log_merge(fast, slow)
plt.semilogx(x, y[i, j], label="norm = {}".format(norm))
plt.xlabel("t")
plt.ylabel("G / Var")
plt.legend()
plt.show()
#:perform the actual multiplication
video = multiply(video, window_video)
#: if the intesity of light source flickers you can normalize each frame to the intensity of the frame
#video = normalize_video(video)
#: perform rfft2 and crop results, to take only first kimax and first kjmax wavenumbers.
fft = rfft2(video, kimax = KIMAX, kjmax = KJMAX)
#: you can also normalize each frame with respect to the [0,0] component of the fft
#: this it therefore equivalent to normalize_video
#fft = normalize_fft(fft)
fft = play_threaded(fft)
if __name__ == "__main__":
import os.path as p
#we will show live calculation with the viewer
viewer = MultitauViewer(scale = True)
#initial mask parameters
viewer.k = 15
viewer.sector = 30
#: now perform auto correlation calculation with default parameters and show live
data, bg, var = iccorr_multi(fft, t1, t2, period = PERIOD, viewer = viewer)
viewer.show()
from cddm.video import mask
from cddm.multitau import iccorr_multi
from cddm.viewer import MultitauViewer
from examples.mask_array import mask as m
import examples.cross_correlate_multi_live as cross_correlate_multi_live
import importlib
importlib.reload(cross_correlate_multi_live) #recreates fft iterator
t1, t2 = cross_correlate_multi_live.t1, cross_correlate_multi_live.t2
fft = cross_correlate_multi_live.fft
fft_masked = mask(fft, mask=m)
data, bg, var = iccorr_multi(fft_masked,
t1,
t2,
period=cross_correlate_multi_live.PERIOD)
#: or this
#data, bg, var = iccorr_multi(fft, t1, t2, period = cross_correlate_multi_live.PERIOD,
# mask = m)
#: inspect the data
viewer = MultitauViewer(scale=True, mask=m)
viewer.set_data(data, bg, var)
viewer.set_mask(k=25, angle=0, sector=180)
viewer.plot()
viewer.show()