odeinterfacediffeq.jl-09606e27-ecf5-54fc-bb29-004bd9f985bf
Last snapshots taken from https://github.com/UnofficialJuliaMirror/ODEInterfaceDiffEq.jl-09606e27-ecf5-54fc-bb29-004bd9f985bf on 2019-11-20T10:10:29.545-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.26 , triggered by Travis cron job on branch "master"
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Scientific vocabulary similarity
Low similarity (13.0%) to scientific vocabulary
Repository
Last snapshots taken from https://github.com/UnofficialJuliaMirror/ODEInterfaceDiffEq.jl-09606e27-ecf5-54fc-bb29-004bd9f985bf on 2019-11-20T10:10:29.545-05:00 by @UnofficialJuliaMirrorBot via Travis job 153.26 , triggered by Travis cron job on branch "master"
Basic Info
- Host: GitHub
- Owner: UnofficialJuliaMirrorSnapshots
- License: other
- Language: Julia
- Default Branch: master
- Size: 26.4 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
ODEInterfaceDiffEq
This package contains bindings for ODEInterface.jl to allow it to be used with the JuliaDiffEq common interface. For more information on using the solvers from this package, see the DifferentialEquations.jl documentation.
Installation
A standard installation on MacOSX and Linux should work. On Windows, you need to install mingw32 compilers and add them to the path. MingW32 can be found here. Then add the path to your environment variables. An example path is:
C:\Program Files\mingw-w64\x86_64-6.1.0-posix-seh-rt_v5-rev0\mingw64\bin
Note that it is required that you add ODEInterface.jl as well;
julia
]add ODEInterface
Otherwise you may have issues instantiating the solvers.
Common API Usage
This library adds the common interface to ODEInterface.jl's solvers. See the DifferentialEquations.jl documentation for details on the interface. Following the Lorenz example from the ODE tutorial, we can solve this using dopri5 via the following:
julia
using ODEInterfaceDiffEq
function lorenz(du,u,p,t)
du[1] = 10.0(u[2]-u[1])
du[2] = u[1]*(28.0-u[3]) - u[2]
du[3] = u[1]*u[2] - (8/3)*u[3]
end
u0 = [1.0;0.0;0.0]
tspan = (0.0,100.0)
prob = ODEProblem(lorenz,u0,tspan)
sol = solve(prob,dopri5(),abstol=1e-4)
using Plots; plot(sol,vars=(1,2,3))
The options available in solve are documented at the common solver options page. The available methods are documented at the ODE solvers page.
Owner
- Name: Unofficial Julia Mirror [Snapshots]
- Login: UnofficialJuliaMirrorSnapshots
- Kind: organization
- Website: https://github.com/UnofficialJuliaMirrorSnapshots/RepoSnapshots.jl
- Repositories: 4
- Profile: https://github.com/UnofficialJuliaMirrorSnapshots
Snapshots of all registered Julia packages. Updated weekly by @UnofficialJuliaMirrorBot. See also: @UnofficialJuliaMirror.
Citation (CITATION.bib)
@article{DifferentialEquations.jl-2017,
author = {Rackauckas, Christopher and Nie, Qing},
doi = {10.5334/jors.151},
journal = {The Journal of Open Source Software},
keywords = {Applied Mathematics},
note = {Exported from https://app.dimensions.ai on 2019/05/05},
number = {1},
pages = {},
title = {DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia},
url = {https://app.dimensions.ai/details/publication/pub.1085583166 and http://openresearchsoftware.metajnl.com/articles/10.5334/jors.151/galley/245/download/},
volume = {5},
year = {2017}
}