Skip to content

High level functions for analyzing the output of simulations

License

Notifications You must be signed in to change notification settings

SciML/EasyModelAnalysis.jl

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

EasyModelAnalysis.jl

Join the chat at https://julialang.zulipchat.com #sciml-bridged Global Docs

codecov Build Status

ColPrac: Contributor's Guide on Collaborative Practices for Community Packages SciML Code Style

EasyModelAnalysis does exactly what it says: it makes model analysis easy. Want to know the first time the number of infected individuals is about 1000? What is the probability that more than 50 people will be infected given probability distributions for the parameters? What variables are the most sensitive? Please find the parameters that best fit the model to the data. All of these, and more, given as simple one-liner queries over SciML-defined differential equation models.

Tutorials and Documentation

For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation which contains the unreleased features.

Quick Demonstration

using EasyModelAnalysis

@parameters t σ ρ β
@variables x(t) y(t) z(t)
D = Differential(t)

eqs = [D(D(x)) ~ σ * (y - x),
    D(y) ~ x *- z) - y,
    D(z) ~ x * y - β * z]

@named sys = ODESystem(eqs)
sys = structural_simplify(sys)

u0 = [D(x) => 2.0,
    x => 1.0,
    y => 0.0,
    z => 0.0]

p ==> 28.0,
    ρ => 10.0,
    β => 8 / 3]

tspan = (0.0, 100.0)
prob = ODEProblem(sys, u0, tspan, p, jac = true)

phaseplot_extrema(prob, x, (x, y))
plot_extrema(prob, x)

About

High level functions for analyzing the output of simulations

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Sponsor this project

 

Packages

No packages published

Languages