JumpProcesses
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
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Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
Basic Info
- Host: GitHub
- Owner: SciML
- License: other
- Language: Julia
- Default Branch: master
- Homepage: https://docs.sciml.ai/JumpProcesses/stable/
- Size: 1.62 GB
Statistics
- Stars: 144
- Watchers: 9
- Forks: 38
- Open Issues: 79
- Releases: 147
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Metadata Files
README.md
JumpProcesses.jl
JumpProcesses.jl provides methods for simulating jump processes, known as stochastic simulation algorithms (SSAs), Doob's method, Gillespie methods, or Kinetic Monte Carlo methods across different fields of science. It also enables the incorporation of jump processes into hybrid jump-ODE and jump-SDE models, including piecewise deterministic Markov processes (PDMPs) and jump diffusions.
JumpProcesses is a component package in the SciML ecosystem, and one of the core solver libraries included in DifferentialEquations.jl.
For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation which contains unreleased features.
The documentation includes
- a tutorial on simulating basic Poisson processes
- a tutorial and details on using JumpProcesses to simulate jump processes via SSAs (i.e. Gillespie methods),
- a tutorial on simulating jump-diffusion processes,
- a reference on the types of jumps and available simulation methods,
- a reference on jump time stepping methods,
- a FAQ with information on changing parameters between simulations and using callbacks,
- the JumpProcesses.jl API documentation.
Contributions welcomed!
Contact us in sciml-bridged on Slack to discuss where to get started, the Help wanted issue, or just open a PR to address an open issue or add new functionality. Contributions, no matter how small, are always welcome and appreciated,
including documentation editing/writing. See also the contribution section.
Installation
There are two ways to install JumpProcesses.jl. First, users may install the meta
DifferentialEquations.jl package, which installs and wraps OrdinaryDiffEq.jl
for solving ODEs, StochasticDiffEq.jl for solving SDEs, and JumpProcesses.jl,
along with a number of other useful packages for solving models involving ODEs,
SDEs and/or jump process. This single install will provide the user with all of
the facilities for developing and solving Jump problems.
To install the DifferentialEquations.jl package, refer to the following link
for complete installation
details.
If the user wishes to separately install the JumpProcesses.jl library, which is a
lighter dependency than DifferentialEquations.jl, then the following code will
install JumpProcesses.jl using the Julia package manager:
julia
using Pkg
Pkg.add("JumpProcesses")
Examples
Stochastic Chemical Kinetics SIR Model
Here we consider the stochastic chemical kinetics jump process model for the basic SIR model, involving three species, $(S,I,R)$, that can undergo the reactions $S + I \to 2I$ and $I \to R$ (each represented as a jump process)
```julia using JumpProcesses, Plots
here we order S = 1, I = 2, and R = 3
substrate stoichiometry:
substoich = [[1 => 1, 2 => 1], # 1S + 1I [2 => 1]] # 1*I
net change by each jump type
netstoich = [[1 => -1, 2 => 1], # S -> S-1, I -> I+1 [2 => -1, 3 => 1]] # I -> I-1, R -> R+1
rate constants for each jump
p = (0.1 / 1000, 0.01)
p[1] is rate for S+I --> 2I, p[2] for I --> R
pidxs = [1, 2]
maj = MassActionJump(substoich, netstoich; param_idxs = pidxs)
u = [999, 1, 0] #[S(0),I(0),R(0)] tspan = (0.0, 250.0) dprob = DiscreteProblem(u, tspan, p)
use the Direct method to simulate
jprob = JumpProblem(dprob, maj)
solve as a pure jump process, i.e. using SSAStepper
sol = solve(jprob) plot(sol) ```
Instead of MassActionJump, we could have used the less efficient, but more
flexible, ConstantRateJump type
```julia rate1(u, p, t) = p[1] * u[1] * u[2] # p[1]SI function affect1!(integrator) integrator.u[1] -= 1 # S -> S - 1 integrator.u[2] += 1 # I -> I + 1 end jump = ConstantRateJump(rate1, affect1!)
rate2(u, p, t) = p[2] * u[2] # p[2]*I function affect2!(integrator) integrator.u[2] -= 1 # I -> I - 1 integrator.u[3] += 1 # R -> R + 1 end jump2 = ConstantRateJump(rate2, affect2!) jprob = JumpProblem(dprob, jump, jump2) sol = solve(jprob) ```
Jump-ODE Example
Let's solve an ODE for exponential growth, but coupled to a constant rate jump (Poisson) process that halves the solution each time it fires
```julia using DifferentialEquations, Plots
du/dt = u is the ODE part
function f(du, u, p, t) du[1] = u[1] end u = [0.2] tspan = (0.0, 10.0) prob = ODEProblem(f, u, tspan)
jump part
fires with a constant intensity of 2
rate(u, p, t) = 2
halve the solution when firing
affect!(integrator) = (integrator.u[1] = integrator.u[1] / 2) jump = ConstantRateJump(rate, affect!)
use the Direct method to handle simulating the jumps
jump_prob = JumpProblem(prob, Direct(), jump)
now couple to the ODE, solving the ODE with the Tsit5 method
sol = solve(jump_prob, Tsit5()) plot(sol) ```
Contributing and Getting Help
Please refer to the SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages for guidance on PRs, issues, and other matters relating to contributing to SciML.
See the SciML Style Guide for common coding practices and other style decisions.
There are a few community forums for getting help and asking questions:
- The #diffeq-bridged and #sciml-bridged channels in the Julia Slack
- The #diffeq-bridged and #sciml-bridged channels in the Julia Zulip
- The Julia Discourse forums
- See also the SciML Community page
Owner
- Name: SciML Open Source Scientific Machine Learning
- Login: SciML
- Kind: organization
- Email: contact@chrisrackauckas.com
- Website: https://sciml.ai
- Twitter: SciML_Org
- Repositories: 170
- Profile: https://github.com/SciML
Open source software for scientific machine learning
GitHub Events
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- Create event: 20
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- Issues event: 30
- Watch event: 4
- Delete event: 10
- Issue comment event: 108
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- Fork event: 4
Last Year
- Create event: 20
- Release event: 8
- Issues event: 30
- Watch event: 4
- Delete event: 10
- Issue comment event: 108
- Push event: 103
- Pull request event: 56
- Pull request review comment event: 183
- Pull request review event: 162
- Fork event: 4
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Sam Isaacson | i****s | 477 |
| Vasily Ilin | v****7@g****m | 244 |
| Christopher Rackauckas | a****s@c****m | 241 |
| gzagatti | g****i | 122 |
| vyudu | v****n@g****m | 33 |
| Arno Strouwen | a****n@t****e | 18 |
| alanderos91 | a****s@u****u | 15 |
| github-actions[bot] | 4****] | 12 |
| elevien | e****n@g****m | 12 |
| dependabot[bot] | 4****] | 11 |
| Manuel Reinhardt | m****t@g****m | 10 |
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| Torkel | t****n@g****m | 8 |
| Anant Thazhemadam | a****m@g****m | 6 |
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| ScottPJones | s****s@a****u | 1 |
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Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 103
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- Average time to close issues: 6 months
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- Average comments per issue: 5.67
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- Average time to close issues: 21 days
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Total downloads:
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- Total dependent packages: 9
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- Total versions: 48
juliahub.com: JumpProcesses
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
- Homepage: https://docs.sciml.ai/JumpProcesses/stable/
- Documentation: https://docs.juliahub.com/General/JumpProcesses/stable/
- License: MIT
-
Latest release: 9.17.0
published 7 months ago
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