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  1. The problem: it seems like all data taking cases, no matter when T_0 was sent, the first acq. cycle runtime timestamp is always ZEROs.
  2. try to do some data taking to test if this is always true

A)  /scratch2/data/testbeam201907-20190720/Run_20190715_183421.csv → have weird starting 64bit frame Run Time

closed /scratch2/data/testbeam201907-20190720/Run_20190715_183421.dat

frameRuntime_ns sampleRuntime_full_ns sampleRuntime_lsb_ns diff_64_ns

0 2448455840 104347135 104347135 -2344108705

1 2448455840 104509260 104509260 -2343946580

2 2448455840 104566885 104566885 -2343888955

3 2448455840 105064960 105064960 -2343390880

4 2448455840 105356010 105356010 -2343099830

... ... ... ... ...

3017 8266914355 8355926810 8355926810 89012455

3018 8266914355 8355961985 8355961985 89047630

3019 8266914355 8356007885 8356007885 89093530

3020 8266914355 8356016660 8356016660 89102305

3021 8266914355 8356088935 8356088935 89174580

  •  To study the above and check more data!!! Mengqing Wu

Problem 2: some trigger timestamp suddenly get smaller than before

  1. The problem: as title said, for some cycles, it happens
  2. to check more details, especially my guess is: this may come from the fact that the MSB of runtime can increment by 1 during one cycle, then of course
    the distored trigger timestamp t_d = LSB + the old MSB
    while the true trigger timestamp t_tr = LSB + (the old MSB+1)
  3. strategy to check: first check the above trick works or not? If not, check more details in terms of times.


A) check the data of /scratch/data/testbeam201907/Run_20190721_234548.dat

lycoris-dev@aida2020-kpix1:~/workspace/LycorisSyncDev$ python3 explorer.py -t /scratch/data/testbeam201907/eudaq2/data/run000400_combined_again.csv -o debug.csv

tlu file: /scratch/data/testbeam201907/eudaq2/data/run000400_combined_again.csv, kpix file: Run_20190721_234548.csv

Kpix recorded 180811 Triggers, while tlu recorded 192395 Triggers

TLU first trigger starts at TID = 178

Any repeated TLU trigger? 0

trigN frameRuntime_ns sampleRuntime_full_ns sampleRuntime_lsb_ns

177 178 699633630 791028235 791028235

178 179 940608580 1023528160 1023528160

[ 791028100 1023528050 1023697975 ... 799562273700 799562840150

799563233750]

time diff < 0:

(array([ 25231, 25232, 25233, 25234, 25235, 25236, 25237, 25238,

25239, 25240, 25241, 25242, 25243, 25244, 25245, 25246,

25247, 67621, 175484, 175485, 175486, 175487, 175488, 175489,

175490, 175491]),)

trigN frameRuntime_ns sampleRuntime_full_ns sampleRuntime_lsb_ns

25229 25230 106490475080 106575331635 20675985715

25230 25231 106490475080 106575883460 20676537540

25231 25232 106490475080 106576176435 20676830515

25232 25233 106490475080 106576517310 20677171390

25233 25234 106490475080 106577267360 20677921440

25234 25235 106490475080 106577758635 20678412715

25235 25236 106490475080 106578020360 20678674440

25236 25237 106490475080 106578244985 20678899065

25237 25238 106490475080 106578447160 20679101240

25238 25239 106490475080 106579151335 20679805415

25239 25240 106490475080 106579399385 20680053465

25240 25241 106490475080 106579605460 20680259540

25241 25242 106490475080 106579915060 20680569140

25242 25243 106490475080 106580330135 20680984215

25243 25244 106490475080 106580477610 20681131690

25244 25245 106570131455 106654616260 20755270340

25245 25246 106570131455 106655363385 20756017465

25246 25247 106570131455 106655755035 20756409115

25247 25248 106570131455 106655964035 20756618115

25248 25249 106570131455 106656478735 20757132815

[-21474836370 -21474836370 -21474836345 -21474836370 -21474836370

-21474836345 -21474836370 -21474836370 -21474836345 -21474836370

-21474836370 -21474836370 -21474836370 -21474836345 -21474836370

-21474836370 -21474836345 -21474836370 -21474836370 -21474836345

-21474836370 -21474836370 -21474836370 -21474836370 -21474836370

-21474836370]

write to debug.csv

nbins = 9 ; xmin, xmax = 110 135