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This is one I remember https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_inference/ https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_... A

This is one I remember https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_inference/ https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_... All of the cost function values are infinite, which results in an empty plot. There were others, but it's been too long that I don't recall exactly which ones. Edit: Here is another from the advanced tutorials DiffEqUncertainty/02-AD_and_optimization.ipynb The fourth cell produces the error Simultaneously using keywords vars and idxs is not supported. Please only use idxs. Stacktrace: [1] error(s::String) @ Base ./error.jl:35 [2] macro expansion [3] apply_recipe(plotattributes::AbstractDict{Symbol, Any}, sol::SciMLBase.AbstractTimeseriesSolution) @ SciMLBase ~/.julia/packages/RecipesBase/eU0hg/src/RecipesBase.jl:300 [4] _process_userrecipes!(plt::Any, plotattributes::Any, args::Any) @ RecipesPipeline ~/.julia/packages/RecipesPipeline/XxUHt/src/user_recipe.jl:38 [5] recipe_pipeline!(plt::Any, plotattributes::Any, args::Any) @ RecipesPipeline ~/.julia/packages/RecipesPipeline/XxUHt/src/RecipesPipeline.jl:72 [6] _plot!(plt::Plots.Plot, plotattributes::Any, args::Any) @ Plots ~/.julia/packages/Plots/aRJ6C/src/plot.jl:225 [7] #plot#182 @ ~/.julia/packages/Plots/aRJ6C/src/plot.jl:102 [inlined] [8] top-level scope @ In[4]:11 [9] eval @ ./boot.jl:368 [inlined] [10] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String) @ Base ./loading.jl:1428

europeanguy 4y ago
> This is one I remember https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_ https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_... > All of the cost function values are infinite, which results in an empty plot. I copy pasted the code up to the first plot of a cost function, and works fine.
ChrisRackauckas 4y ago
Were those run with the same versions as the project/manifests described in the repository reproducibility section (https://docs.sciml.ai/DiffEqParamEstim/dev/#Reproducibility https://docs.sciml.ai/DiffEqParamEstim/dev/#Reproducibility)? Downloading the described package version for DiffEqParamEstim.jl seems to work fine, as I just checked and another user described. The build system builds the outputs and plots using those described versions, so if it errors it would put the error into the documentation. If I had to guess what your issue was, you might be using some old Linux distro that's giving you like a Julia v1.3 with ancient package versions: that's something outside of my control and is the most common reason we've found for people reporting weird failures. We highly highly recommend following the standard installation instructions (https://docs.sciml.ai/Overview/stable/getting_started/installation/#installation https://docs.sciml.ai/Overview/stable/getting_started/instal...) and using the Julia generic binaries (https://julialang.org/downloads/ https://julialang.org/downloads/). On a higher note though... I'm a bit confused: we were talking about DifferentialEquations.jl, one of the libraries that is most heavily maintained, and then when asked for examples of what tutorials aren't working, you don't post any examples from DifferentialEquations.jl but from tutorials of other libraries? One of which we publicly discuss all of the time as not yet completed (DiffEqUncertainty -> SciMLUncertainty, one of the current research projects)? I would not conflate the two, it's like saying SciPy isn't robust because Python's ODES library isn't: they are two different libraries and the authors are quite public about the differences. We have been very public that the UQ stuff is still ongoing research with monthly talks about it, and we keep it in a separate Github repo with separate documentation. We don't call that DifferentialEquations.jl for a very good reason.

europeanguy 4y ago

> This is one I remember https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_ https://docs.sciml.ai/DiffEqParamEstim/stable/tutorials/ODE_... > All of the cost function values are infinite, which results in an empty plot. I copy pasted the code up to the first plot of a cost function, and works fine.

ChrisRackauckas 4y ago

Were those run with the same versions as the project/manifests described in the repository reproducibility section (https://docs.sciml.ai/DiffEqParamEstim/dev/#Reproducibility https://docs.sciml.ai/DiffEqParamEstim/dev/#Reproducibility)? Downloading the described package version for DiffEqParamEstim.jl seems to work fine, as I just checked and another user described. The build system builds the outputs and plots using those described versions, so if it errors it would put the error into the documentation. If I had to guess what your issue was, you might be using some old Linux distro that's giving you like a Julia v1.3 with ancient package versions: that's something outside of my control and is the most common reason we've found for people reporting weird failures. We highly highly recommend following the standard installation instructions (https://docs.sciml.ai/Overview/stable/getting_started/installation/#installation https://docs.sciml.ai/Overview/stable/getting_started/instal...) and using the Julia generic binaries (https://julialang.org/downloads/ https://julialang.org/downloads/). On a higher note though... I'm a bit confused: we were talking about DifferentialEquations.jl, one of the libraries that is most heavily maintained, and then when asked for examples of what tutorials aren't working, you don't post any examples from DifferentialEquations.jl but from tutorials of other libraries? One of which we publicly discuss all of the time as not yet completed (DiffEqUncertainty -> SciMLUncertainty, one of the current research projects)? I would not conflate the two, it's like saying SciPy isn't robust because Python's ODES library isn't: they are two different libraries and the authors are quite public about the differences. We have been very public that the UQ stuff is still ongoing research with monthly talks about it, and we keep it in a separate Github repo with separate documentation. We don't call that DifferentialEquations.jl for a very good reason.