diffeqpy
r-bigsimr
| diffeqpy | r-bigsimr | |
|---|---|---|
| 4 | 2 | |
| 499 | 2 | |
| 2.4% | - | |
| 7.7 | 5.5 | |
| 2 months ago | 3 months ago | |
| Python | R | |
| MIT License | MIT License |
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diffeqpy
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How Julia ODE Solve Compile Time Was Reduced From 30 Seconds to 0.1
With Python you have to write packages in some other language anyways, so you might as well do that with Julia. One of the reasons for getting all of this precompilation going is to eventually ship precompiled system images with things like https://github.com/SciML/diffeqpy, effectively using Julia as a replacement for where C/Fortran is traditionally used there. If I can make that pipeline smooth, then I think Julia as a Python package building source will be a good option for a lot of folks. Right now it's a very manual, but it could easily improve with a bit of tooling.
- ‘Machine Scientists’ Distill the Laws of Physics from Raw Data
- Is it possible to create a Python package with Julia and publish it on PyPi?
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Julia vs R/Python
10-100x speed increase was not an exaggeration for me. With julia I was able to run things quickly on my own machine which I had been running on a compute cluster. I agree that numba could be just as fast as julia. I also just saw that you can run that DE library from julia that I like so much from python using this package. https://github.com/SciML/diffeqpy
r-bigsimr
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Is it possible to create a Python package with Julia and publish it on PyPi?
One more example for you. Our group wrote our core package in Julia called Bigsimr.jl (here) and then wrote interfaces to it for R (here and on cran) and Python (here and on PyPi)
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[D] What's your favorite concept/rule/theorem in statistics and why?
We have an R interface to the Julia library, but it still requires Julia to be installed. It uses JuliaCall, and honestly you don't even need to use our R wrapper.
What are some alternatives?
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
PySR - High-Performance Symbolic Regression in Python and Julia
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
Bigsimr.jl - Simulate multivariate distributions with arbitrary marginals.
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
python-bigsimr
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond
FunctionalModels.jl - Equation-based modeling and simulations in Julia