Oh, yesterday a new version was tagged which made all of the ODE solvers
in-place, but that introduced a small bug in the initial Δt determination.
The fix is already available on master, which you can get with
Pkg.checkout("DifferentialEquations"). Or you can just set the initial
stepsize to some value like:
sol = solve(prob,timespan,Δt = 0.125)
[since that's adaptive it's only the initial Δt, you can play around with
refining a bit with adaptive=false to turn off adaptive timestepping].
On Saturday, August 6, 2016 at 4:32:02 AM UTC-7, Henri Girard wrote:
>
> I compiled version 0.5 julia git :
> Here is the result
>
> julia> using DifferentialEquations
> INFO: Precompiling module DifferentialEquations...
> WARNING: Method definition cgrad(Any, Any) in module PlotUtils at
> /home/pi/.julia/v0.5/PlotUtils/src/color_gradients.jl:82 overwritten at
> /home/pi/.julia/v0.5/PlotUtils/src/color_gradients.jl:99.
> WARNING: Method definition #cgrad(Array{Any, 1}, PlotUtils.#cgrad, Any,
> Any) in module PlotUtils overwritten.
>
> julia> using DifferentialEquations
>
> julia> alpha = 0.5 #Setting alpha to 1/2
> 0.5
>
> julia> f(y,t) = alpha*y
> f (generic function with 1 method)
>
> julia> u0 = 1.5
> 1.5
>
> julia> prob = ODEProblem(f,u0)
>
> DifferentialEquations.ODEProblem(f,1.5,DifferentialEquations.#232,false,1,(1,),false)
>
> julia> timespan = [0,1]
> 2-element Array{Int64,1}:
> 0
> 1
>
> julia> sol = solve(prob,timespan)
>
> ERROR: MethodError: objects of type Float64 are not callable
> in ode_determine_initΔt(::Float64, ::Float64, ::Rational{Int64},
> ::Rational{Int64}, ::Int64, ::#f, ::Int64) at
> /home/pi/.julia/v0.5/DifferentialEquations/src/ode/ode_solve.jl:308
> in #solve#453(::Array{Any,1}, ::Function,
> ::DifferentialEquations.ODEProblem, ::Array{Int64,1}) at
> /home/pi/.julia/v0.5/DifferentialEquations/src/ode/ode_solve.jl:131
> in solve(::DifferentialEquations.ODEProblem, ::Array{Int64,1}) at
> /home/pi/.julia/v0.5/DifferentialEquations/src/ode/ode_solve.jl:81
>
> julia> plot(sol)
> ERROR: UndefVarError: sol not defined
>
>
> Le lundi 1 août 2016 16:37:09 UTC+2, Chris Rackauckas a écrit :
>>
>> I am pleased to announce the first release of DifferentialEquations.jl
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl>.
>>
>> An accompanying blog post explains the motivation and philosophy of the
>> package in more detail.
>> <http://www.stochasticlifestyle.com/introducing-differentialequations-jl/>
>>
>> DifferentialEquations.jl is a library of methods for solving various
>> differential equations. DifferentialEquations.jl makes it easy to access
>> many different methods for solving equations and plot the results. This
>> v0.1 release marks that the core of DifferentialEquations.jl is complete,
>> which includes:
>>
>>
>> - The standard ODE, SDE, and PDE (Heat and Poisson) solvers
>>
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/#implemented-solvers>
>> .
>> - Plot recipes for all the basic types.
>> - Tests for convergence of every algorithm.
>> - Extensive documentation
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/>and
>> tutorials
>>
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/tree/master/examples>
>> .
>>
>> DifferentialEquations.jl also has many special features not seen in other
>> differential equation libraries, which includes (but is not limited to):
>>
>>
>> - A simple interface via multiple-dispatch (see the blog post
>> <http://www.stochasticlifestyle.com/introducing-differentialequations-jl/>
>> ).
>> - Implementations of Feagin's Order 10, 12, and 14 Runge-Kutta methods
>>
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/blob/master/examples/Feagin's%20Order%2010%2C%2012%2C%20and%2014%20methods.ipynb>
>> .
>> - Compatibility with Julia-defined number types
>>
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/blob/master/examples/Solving%20Equations%20in%20With%20Julia-Defined%20Types.ipynb>.
>> This
>> has been tested to work with Bigs, DecFP, and ArbFloats, and is actively
>> being tested with ArbReals and DoubleDouble.
>> - Wrappers to ODE.jl and ODEInterface.jl
>>
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/blob/master/examples/Calling%20External%20Solvers%20-%20ODEjl%20and%20ODEInterface.ipynb>,
>>
>> giving you instant access to tons of different solver methods just by
>> changing the `alg` keyword.
>> - State-of-the-art stochastic differential equation solvers
>>
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/solvers/sde_solve/>.
>> Implemented
>> are results from recent papers, and many other algorithms (including fast
>> adaptivity and highly-stable explicit methods) are waiting on a private
>> branch until papers are published.
>> - Finite element solvers for some common stochastic PDEs
>>
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/tutorials/femstochastic_example/>
>> including
>> the Reaction-Diffusion equation used to describe Turing Morphogenesis
>>
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/blob/master/examples/Solving%20the%20Gierer-Meinhardt%20Equations.ipynb>
>> .
>> - An algorithm design and testing suite
>>
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/man/convergence/>
>> .
>>
>> For more information, check out the documentation
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/>or
>> the IJulia tutorial notebooks
>> <https://github.com/ChrisRackauckas/DifferentialEquations.jl/tree/master/examples>.
>> If
>> you're interested in contributing, please see the Contributor's Guide
>> <http://chrisrackauckas.github.io/DifferentialEquations.jl/latest/internals/contributors_guide/>
>>
>> and/or chat with me on the Gitter channel
>> <https://gitter.im/ChrisRackauckas/DifferentialEquations.jl>.
>>
>
