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g2(tau) = <I(t)*I(t+tau)> / (<I(t)> * <I(t+tau)>),

where I(t) is an image intensity at time t, and tau is a delay between two measurements.
Typical experimental condition can be described as follows:

• Run duration is about one hour at frequency up to 120 Hz that gives up to 10^5^10^5-10^6^ 10^6 images.
• Currently typical imaging devise is a Princeton camera with 1300x1340 pixels.
• Need to calculate g2(tau) for each pixel, averaged over all possible image times t with time difference tau between images.
• A set of tau should have about 30-100 points in log scale uniformly covering the run duration.
• Use for example xcsi0112-r0015: 500 images with 8 sec delay between images.
  Desired time for evaluation of the auto-correlation function should be comparable with run duration ~<1 <1 hour. Currently this algorithm takes a few hours that can not be used for fast feedback in real time experiment.

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Basic idea is (1) to split image vs time for small parts in image, (2) to process each part on separate computer node, (3) to merge results at the end of processing. It is clear that significant speedup (about T/N~nodes~N_nodes_) is achieved at the 2nd stage. These three stages are performed in separate C++ applications. Wrapping python script allows to submit job by a single command. It takes care about file and sub-process management in this job, as described below.

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img-xcs-r0015-tau.txt          - string of {{tau}} values for which the auto-correlation function is evaluated
img-xcs-r0015-b0000-result.bin - auto-correlators for the part of the image for all {{tau}} values 
img-xcs-r0015-b0001-result.bin
img-xcs-r0015-b0002-result.bin
img-xcs-r0015-b0003-result.bin
img-xcs-r0015-b0004-result.bin
img-xcs-r0015-b0005-result.bin
img-xcs-r0015-b0006-result.bin
img-xcs-r0015-b0007-result.bin

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• File with split-image data for selected events img-xcs-r0015-b000N.bin:
  Currently this file contains <uint16_t> amplitude for each pixel in binary format for:

  Code Block
  <data-for-img-partN-of-img1> <data-for-img-partN-of-img2> ... <data-for-img-partN-of-imgLast>

• File with metadata parameters img-xcs-r0015-med.txt:

  Code Block
  IMAGE_ROWS 1300 IMAGE_COLS 1340 IMAGE_SIZE 1742000 NUMBER_OF_FILES 8 BLOCK_SIZE 217750 REST_SIZE 0 NUMBER_OF_IMGS 500 FILE_TYPE bin DATA_TYPE uint16_t TIME_SEC_AVE 8.088413 TIME_SEC_RMS 0.063639 TIME_INDEX_MAX 499

• File with image time records img-xcs-r0015-time.txt:

  Code Block
  1 0.000000 0.000000 20120616-080236.671607864 5366 0 2 8.026429 8.026429 20120616-080244.698036743 8255 1 3 16.144788 8.118359 20120616-080252.816395836 11177 2 4 24.154835 8.010048 20120616-080300.826443448 14060 3 ...

  where each record has:

  Code Block
  <image-in-file#> <t(sec)-from-the-1st-event> <dt(sec)> <time-stamp> <fiducials> <event#-since-configure>

• File with image time records and evaluated time index img-xcs-r0015-time-ind.txt:

  Code Block
  1 0.000000 0.000000 20120616-080236.671607864 5366 0 0 2 8.026429 8.026429 20120616-080244.698036743 8255 1 1 3 16.144788 8.118359 20120616-080252.816395836 11177 2 2 4 24.154835 8.010048 20120616-080300.826443448 14060 3 3 5 32.281937 8.127102 20120616-080308.953545010 16985 4 4 ...

  where each record has:

  Code Block
  <image-in-file#> <t(sec)-from-the-1st-event> <dt(sec)> <time-stamp> <fiducials> <event#-since-configure> <time-index-starting-from-0>

• File with split-image correlators for each value of tau img-xcs-r0015-b000N-result.bin:
  Currently it saves <float> correlator for each pixel in binary format for:

  Code Block
  <corr-for-img-partN-of-tau1> <corr-for-img-partN-of-tau2> ... <corr-for-img-partN-of-tauLast>

• my-tau.txt:

  Code Block
  1 3 5 7 9 10 12 14 16 18 20 24 28 30 32 36 40 ... 160 180 200 240 280 300 320 360 400

  contains the tau values presented in terms of number of ordered images in the file.