diffeqpy
crystal
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diffeqpy | crystal | |
---|---|---|
4 | 239 | |
494 | 19,110 | |
3.8% | 0.5% | |
7.7 | 9.8 | |
about 1 month ago | 6 days ago | |
Python | Crystal | |
MIT License | Apache License 2.0 |
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.
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
crystal
- A Language for Humans and Computers
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Top Paying Programming Technologies 2024
27. Crystal - $77,104
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Crystal 1.11.0 Is Released
I like the first code example on https://crystal-lang.org
# A very basic HTTP server
- Is Fortran "A Dead Language"?
- Choosing Go at American Express
- Odin Programming Language
- I Love Ruby
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Ruby 3.3's YJIT: Faster While Using Less Memory
Obviously as an interpreted language, it's never going to be as fast as something like C, Rust, or Go. Traditionally the ruby maintainers have not designed or optimized for pure speed, but that is changing, and the language is definitely faster these days compared to a decade ago.
If you like the ruby syntax/language but want the speed of a compiled language, it's also worth checking out Crystal[^1]. It's mostly ruby-like in syntax, style, and developer ergonomics.[^2] Although it's an entirely different language. Also a tiny community.
[1]: https://crystal-lang.org/
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What languages are useful for contribution to the GNOME project.
Crystal is a nice language that's not only simple to read and write but performs very well too. And the documentation is amazing as well.
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Jets: The Ruby Serverless Framework
Ruby is a super fun scripting language. I much prefer it to python when I need something with a little more "ooomph" than bash. It's just...nice...to write in. Ruby performance has come a long way in the last decade as well. There's libraries for pretty much everything.
My modern programming toolkit is basically golang + ruby + bash and I am never left wanting.
I do find Crystal (https://crystal-lang.org/) really interesting and am hoping it has its own "ruby on rails" moment that helps the language reach a tipping point in popularity. All the beauty of ruby with all of the speed of Go (and then some, it often compares favorably to languages like rust in benchmarks).
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.
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
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
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
go - The Go programming language
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]
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
csvzip - A standalone CLI tool to reduce CSVs size by converting categorical columns in a list of unique integers.
mint-lang - :leaves: A refreshing programming language for the front-end web
PySR - High-Performance Symbolic Regression in Python and Julia
Odin - Odin Programming Language