It is inconvenient that model parameters of ODE-systems have to be re-specified under the current implementation (i.e. when calling the solver). :warning: Note that the tools should be at most tools that pymob offers. They should not be interconnected with the core-pymob functionality
Desired features:
[x] inspect model function call to get all arguments that are not t and X. The remaining parameters should be model parameters. These can be then be matched to order the values of an incoming parameter dictionary to provide *args for the solve_ivp call 🙂
[x] ⚠️ Fix seeding problem. pathos uses pickling to serialize objects --> this results in a copied seed queue in parallel model evaluations, meaning that seeds are reused for stochastic simulations, ideally, seeds are passed when simulations are launched.
[x] This could be attacked by using an Evaluator class, which is instantiated each time the simulation is evaluated
With this also the deprecation warning for using the results property (which is convenient) inisde the simulation class can be avoided.
It is inconvenient that model parameters of ODE-systems have to be re-specified under the current implementation (i.e. when calling the solver). :warning: Note that the tools should be at most tools that
pymob
offers. They should not be interconnected with the core-pymob functionalityDesired features:
t
andX
. The remaining parameters should be model parameters. These can be then be matched to order the values of an incoming parameter dictionary to provide*args
for thesolve_ivp
call 🙂pathos
uses pickling to serialize objects --> this results in a copied seed queue in parallel model evaluations, meaning that seeds are reused for stochastic simulations, ideally, seeds are passed when simulations are launched.