Comments (4)
Hi @sinkovit ,
Sorry for the long time it took me to answer this one.
I originally looked into making this possible, however it would require to change quite a few functions for generating functions.
I have conducted a small experiment using IGoR's new random seed generator using the following piece of code in the custom code section of the main:
else{
//Write your custom procedure here
size_t n_seeds = 99999999;
ofstream file ("/tmp/random_seeds.csv");
for(size_t i=0;i!=n_seeds;++i){
file<<draw_random_64bits_seed()<<endl;
}
}
Importing the corresponding file with pandas in python and making a histogram out of it with 5000 bins I get the following figure:
I think this shows that the seed generator is indeed uniformly random (1,84x10^19 is 2^64, the maximum value of a 64 bits integer) and getting the same seed twice is very unlikely. I have actually checked and all the 99999999 seeds were unique.
Given this information I think this is a won't fix (at least not in the near future)
Quentin
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Hi Bob,
I have closed the issue but great to hear that you manage to produce so many reads!
Out of curiosity: has this process taken a lot of computation time? Did you feel it was a bottleneck in your analysis? Do you think parallelizing random sequence generation is worth doing?
It would most likely be limited by i/o time then.
Best,
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