#: 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)
if __name__ == "__main__":
import os.path as p
#: now perform auto correlation calculation with default parameters using iterative algorithm
data, bg, var = iacorr_multi(fft, count=NFRAMES)
#: inspect the data
viewer = MultitauViewer(scale=True)
viewer.set_data(data, bg, var)
viewer.set_mask(k=25, angle=0, sector=30)
viewer.plot()
viewer.show()
#perform normalization and merge data
fast, slow = normalize_multi(data, bg, var, scale=True)
#: save the normalized raw data to numpy files
np.save(p.join(DATA_PATH, "auto_correlate_multi_raw_fast.npy"), fast)
np.save(p.join(DATA_PATH, "auto_correlate_multi_raw_slow.npy"), slow)
x, y = log_merge(fast, slow)
#: save the normalized merged data to numpy files
#: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()