Use CBRNG for Schedules #2243
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thorstenhater
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Jan 19, 2024
The core issue here is to add units to the user facing API. I decided on using the LLNL/units library, which offers conversion and checking at runtime. Runtime is a requirement -- as much as I love static guarantees --, but keeping the interface uniform between Python and C++ is a must. While setting this up, I noticed the severe lack of IDE/LSP support for Arbor, so I added typing stubs using https://github.com/sizmailov/pybind11-stubgen. The conjunction of typing and units exposed misuse of pybind11 in several places, so next I had to massage the ordering of bindings, adjust the specification of default arguments, and add the odd missing binding. The schedule/event generator interface was tightened up, hiding the `*_impl` structs and exposing only the type erased `schedule` object. That in turn required de-generification of the Poisson schedule. Now, Mersenne twister is the only choice and I will remove that later on for the CBRNG we are already using elsewhere. Currently, units are used for: - [X] simulation - [X] schedule/generator - [x] paintables - [X] placeables - [X] iclamp - [X] threshold - [X] connections - [X] gap junctions Adding units to mechanism interfaces is _interesting_ but requires more work and thought, so I'll defer that to a later point in time. We'd need to adjust modcc to expose and **check** units and devise a scheme to handle missing units. Generic TODOs; some might spin off into separate issues. - [x] ~~rename py::iclamp OR cpp::i_clamp for consistency~~ covered by #2239 - [x] use scale/base for iexpr paintables for consistency with scaled_mech - [x] ~~Use CBRNG for Poisson schedule~~ covered by #2243 - [ ] Automate stub generation. A wishlist item, requires installing extra software. - [x] Properly integrate units w/ spack. NB. Units doesn't have a spackage. Closes #1983 Closes #2032 --------- Co-authored-by: boeschf <48126478+boeschf@users.noreply.github.com>
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Use the CBRNG instead of
std::randomfor schedules and other random draws.Mainly, that'll affect the Poisson schedule.
As we have taken R123 on as a dependency anyhow, we can leverage it in all relevant
places. By doing this, we reduce the memory footprint of the Poisson schedule's state
and simplify it to a single number. It might be slightly faster, too.
Also, add the PRNG state to SERDES, something we cannot do today.
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