DiffEqNoiseProcess.jl
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SciML
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
Addresses https://github.com/SciML/DiffEqNoiseProcess.jl/issues/197 ```julia using DiffEqNoiseProcess, JuliaFormatter, StochasticDiffEq, Plots, Random, Distributions # taken from the first examples about SDE in the doc (https://docs.sciml.ai/DiffEqDocs/stable/tutorials/sde_example/#Example-1:-Scalar-SDEs) function lorenz(du, u, p, t) du[1] = 10.0(u[2]...
**Describe the bug 🐞** To make sure that I understood the doc correctly, I decided to make a minimal example only based on code found in the documentation. I wanted...
```jl Correlated Wiener Process: Test Failed at C:\Users\arno\Desktop\SciML\DiffEqNoiseProcess.jl\test\correlated.jl:36 Expression: ≈(Γ, cov(sol, dims = 2) / dt, rtol = 0.01) Evaluated: [1.0 0.3; 0.3 1.0] ≈ [1.4071833818984558 0.4647373260398336; 0.4647373260398336 1.4034111425410771] (rtol=0.01)...
Just following the instructions posted on Slack for adding downgrade CI. I bumped all the way up, and then bumped down until the packaged compiled locally and passed the tests.
Not sure how to best fix these method ambiguities. In practice those are not methods ever to be called.
Such that `makedocs` `warnonly = [:docs_block, :missing_docs]` can be removed.
I'm trying to format the docstrings to SciML style. The "mutable struct" here messes up the formatter. https://docs.sciml.ai/DiffEqNoiseProcess/stable/abstract_noise_processes/#DiffEqNoiseProcess.NoiseProcess Remove it like https://docs.sciml.ai/DiffEqDocs/stable/types/ode_types/#SciMLBase.ODEFunction ?
```jl using DiffEqNoiseProcess #using DifferentialEquations u0 = rand(2) tgrid = 0.0:0.1:10.0 brownian_noise = randn(length(u0)*(length(tgrid)-1)) brownian_noise = reshape(brownian_noise,length(u0),length(tgrid)-1) brownian_noise = vcat([zeros(length(u0))], [c for c in eachcol(brownian_noise)]) W = NoiseGrid(tgrid,brownian_noise) prob =...
```jl using DiffEqNoiseProcess using DifferentialEquations u0 = rand(2) tgrid = 0.0:0.1:10.0 brownian_noise = randn(length(u0)*(length(tgrid)-1)) brownian_noise = reshape(brownian_noise,length(u0),length(tgrid)-1) brownian_noise = vcat([zeros(length(u0))], [c for c in eachcol(brownian_noise)]) W = NoiseGrid(tgrid,brownian_noise) prob =...