Infiltrator.jl
DifferentialEquations.jl
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| Infiltrator.jl | DifferentialEquations.jl | |
|---|---|---|
| 5 | 6 | |
| 379 | 2,756 | |
| 5.3% | 1.6% | |
| 7.1 | 7.2 | |
| 14 days ago | 21 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Infiltrator.jl
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I can never debug codes in Julia without issues. Help?
Also Infiltrator is very fast and useful but don't try to use it from the Vscode integrated terminal.
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Just downloaded Julia, what packages/other things do I need to download to have it all work properly?
The package Infiltrator.jl might be what you seek. It's not as good as inserting breakpoints like in Matlab but it's still better than printing everywhere haha
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Julia 1.7 has been released
Yes, it uses Debugger.jl, which relies on JuliaInterpreter.jl under the hood, so while you can tell the debugger to compile functions in certain modules, it will mostly interpret your code.
You might be interested in https://github.com/JuliaDebug/Infiltrator.jl, which uses an approach more similar to what you describe.
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Error handling and unwinding stacks in Julia
Another small thing is in the REPL when you trigger an error in Common lisp it drops you into the debugger where you can redefine code and retry directly from the stack without unwinding the entire stack. Does Julia have functionality similar to this? Currently when I trigger an error Julia just throw the error and goes right back to the top level prompt. To resolve this issue I've tried sprinkling my code with a combination of GitHub - JuliaDebug/Infiltrator.jl + Stack Traces · The Julia Language wrapped in try catch blocks so that if an error is singled it drops into a debugger of sorts. This is ok and it works but it isn't really as good. Is there a current package that can emulate what I am trying to do? I think that the REPL workflow is good in julia but the workflow stalls out when you run into errors that don't drop into debuggers and such.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
DifferentialEquations.jl
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Startups are building with the Julia Programming Language
This lists some of its unique abilities:
https://docs.sciml.ai/DiffEqDocs/stable/
The routines are sufficiently generic, with regard to Julia’s type system, to allow the solvers to automatically compose with other packages and to seamlessly use types other than Numbers. For example, instead of handling just functions Number→Number, you can define your ODE in terms of quantities with physical dimensions, uncertainties, quaternions, etc., and it will just work (for example, propagating uncertainties correctly to the solution¹). Recent developments involve research into the automated selection of solution routines based on the properties of the ODE, something that seems really next-level to me.
[1] https://lwn.net/Articles/834571/
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From Common Lisp to Julia
https://github.com/SciML/DifferentialEquations.jl/issues/786. As you could see from the tweet, it's now at 0.1 seconds. That has been within one year.
Also, if you take a look at a tutorial, say the tutorial video from 2018,
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When is julia getting proper precompilation?
It's not faith, and it's not all from Julia itself. https://github.com/SciML/DifferentialEquations.jl/issues/785 should reduce compile times of what OP mentioned for example.
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Julia 1.7 has been released
Let's even put raw numbers to it. DifferentialEquations.jl usage has seen compile times drop from 22 seconds to 3 seconds over the last few months.
https://github.com/SciML/DifferentialEquations.jl/issues/786
- Suggest me a Good library for scientific computing in Julia with good support for multi-core CPUs and GPUs.
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DifferentialEquations compilation issue in Julia 1.6
https://github.com/SciML/DifferentialEquations.jl/issues/737 double posted, with the answer here. Please don't do that.
What are some alternatives?
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Debugger.jl - Julia debugger
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
Gridap.jl - Grid-based approximation of partial differential equations in Julia
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
ApproxFun.jl - Julia package for function approximation
Diffractor.jl - Next-generation AD
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
ResultTypes.jl - A Result type for Julia—it's like Nullables for Exceptions
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms