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License: Other
Utilities for Caltech course BE 150
License: Other
To deal with memory leak issues, the propensity function should not reinstantiate an update array, but should update an existing one. That means we change the call signature for the propensity function to:
@numba.njit
def update_propensity(propensities, population, t, *args):
# Code here
# No return statement; Numpy arrays are mutable.
Should be able to run a single Gillespie simulation where we keep the populations/time points only at transitions. This is useful for finding inter-event times and things like that.
If no move is possible, such as when the sum of all of the propensities is zero, should abort the Gillespie solve. Maybe also have error checking for propensity function to make sure all propensities are positive.
Hi,
I wanna solve my delay differential equations (SIR model with additional compartments) and during my search on google and github I found Caltech webpage related to this issue. and your solver approach for dde is very similar to my target. I tried to use it to solve my model. but I was not successful, can I share it here and ask you to help me to adjust my model and use your dde solver for it ?
Best regards,
Nick
Add a deriv
kwarg for regulatory functions to allow for calculation of derivatives.
This would be used in the book, e.g., in the paradoxical chapter.
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